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Sketch Recognition Lab
Director: Dr. Tracy Anne Hammond

SRL Dissertations and Theses


 


Dissertations


2016 
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 Valentine, Stephanie. Design, Deployment, Identity, & Conformity: An Analysis of Children's Online Social Networks. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. August, 2016. First Position: TAMU Asst. Research Scientist. Link
Show Abstract:

To Be Entered

Show BibTex

@mastersthesis{stephanievalentine2016PhD,
type = {{PhD Doctoral Dissertation}},
author = {Valentine, Stephanie},
title = {Design, Deployment, Identity, \& Conformity: An Analysis of Children's Online Social Networks},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2016 
PublicationImagePublicationImage
 Alamudun, Folami. Analysis of Visuo-cognitive Behavior in Screening Mammography. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Oak Ridge Laboratories. Link
Show Abstract:

Predictive modeling of human visual search behavior and the underlying metacognitive processes is now possible thanks to significant advances in bio-sensing device technology and machine intelligence. Eye tracking bio-sensors, for example, can measure psycho-physiological response through change events in configuration of the human eye. These events include positional changes such as visual fixation, saccadic movements, and scanpath, and non-positional changes such as blinks and pupil dilation and constriction. Using data from eye-tracking sensors, we can model human perception, cognitive processes, and responses to external stimuli. In this study, we investigated the visuo-cognitive behavior of clinicians during the diagnostic decision process for breast cancer screening under clinically equivalent experimental conditions involving multiple monitors and breast projection views. Using a head-mounted eye tracking device and a customized user interface, we recorded eye change events and diagnostic decisions from 10 clinicians (three breast-imaging radiologists and seven Radiology residents) for a corpus of 100 screening mammograms (comprising cases of varied pathology and breast parenchyma density). We proposed novel features and gaze analysis techniques, which help to encode discriminative pattern changes in positional and non-positional measures of eye events. These changes were shown to correlate with individual image readers' identity and experience level, mammographic case pathology and breast parenchyma density, and diagnostic decision. Furthermore, our results suggest that a combination of machine intelligence and bio-sensing modalities can provide adequate predictive capability for the characterization of a mammographic case and image readers diagnostic performance. Lastly, features characterizing eye movements can be utilized for biometric identification purposes. These findings are impactful in real-time performance monitoring and personalized intelligent training and evaluation systems in screening mammography. Further, the developed algorithms are applicable in other application domains involving high-risk visual tasks

Show BibTex

@mastersthesis{folamialamudun2016PhD,
type = {{PhD Doctoral Dissertation}},
author = {Alamudun, Folami},
title = {Analysis of Visuo-cognitive Behavior in Screening Mammography},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2016 
PublicationImagePublicationImage
 Kim, Hong-hoe . A Fine Motor Skill Classifying Framework to Support Children's Self-regulation Skills and School Readiness. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Samsung. Link
Show Abstract:

Children’s self-regulation skills predict their school-readiness and social behaviors, and assessing these skills enables parents and teachers to target areas for improvement or prepare children to enter school ready to learn and achieve. Assessing these skills enables parents and teachers to target areas for improvement or prepare children to enter school ready to learn and achieve. To assess children’s fine motor skills, current educators are assessing those skills by either determining their shape drawing correctness or measuring their drawing time durations through paper-based assessments. However, the methods involve human experts manually assessing children’s fine motor skills, which are time consuming and prone to human error and bias. As there are many children that use sketch-based applications on mobile and tablet devices, computer-based fine motor skill assessment has high potential to solve the limitations of the paper-based assessments. Furthermore, sketch recognition technology is able to offer more detailed, accurate, and immediate drawing skill information than the paper-based assessments such as drawing time or curvature difference. While a number of educational sketch applications exist for teaching children how to sketch, they are lacking the ability to assess children’s fine motor skills and have not proved the validity of the traditional methods onto tablet-environments. We introduce our fine motor skill classifying framework based on children’s digital drawings on tablet-computers. The framework contains two fine motor skill classifiers and a sketch-based educational interface (EasySketch). The fine motor skill classifiers contain: (1) KimCHI: the classifier that determines children’s fine motor skills based on their overall drawing skills and (2) KimCHI2: the classifier that determines children’s fine motor skills based on their curvature- and corner-drawing skills. Our fine motor skill classifiers determine children’s fine motor skills by generating 131 sketch features, which can analyze their drawing ability (e.g. DCR sketch feature can determine their curvature-drawing skills). We first implemented the KimCHI classifier, which can determine children’s fine motor skills based on their overall drawing skills. From our evaluation with 10- fold cross-validation, we found that the classifier can determine children’s fine motor skills with an f-measure of 0.904. After that, we implemented the KimCHI2 classifier, which can determine children’s fine motor skills based on their curvature- and corner-drawing skills. From our evaluation with 10-fold cross-validation, we found that the classifier can determine children’s curvature-drawing skills with an f-measure of 0.82 and corner-drawing skills with an f-measure of 0.78. The KimCHI2 classifier outperformed the KimCHI classifier during the fine motor skill evaluation. EasySketch is a sketch-based educational interface that (1) determines children’s fine motor skills based on their drawing skills and (2) assists children how to draw basic shapes such as alphabet letters or numbers based on their learning progress. When we evaluated our interface with children, our interface determined children’s fine motor skills more accurately than the conventional methodology by f-measures of 0.907 and 0.744, accordingly. Furthermore, children improved their drawing skills from our pedagogical feedback. Finally, we introduce our findings that sketch features (DCR and Polyline Test) can explain children’s fine motor skill developmental stages. From the sketch feature distributions per each age group, we found that from age 5 years, they show notable fine motor skill development.

Show BibTex

@mastersthesis{honghoekim2016PhD,
type = {{PhD Doctoral Dissertation}},
author = {Kim, Hong-hoe },
title = {A Fine Motor Skill Classifying Framework to Support Children's Self-regulation Skills and School Readiness},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2014 
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 Prasad, Manoj. Designing Tactile Interfaces for Abstract Interpersonal Communication, Pedestrian Navigation and Motocyclists Navigation. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. 183 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2014. First Position: Microsoft. Link
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The tactile medium of communication with users is appropriate for displaying information in situations where auditory and visual mediums are saturated. There are situations where a subject’s ability to receive information through either of these channels is severely restricted by the environment they are in or through any physical impairments that the subject may have. In this project, we have focused on two groups of users who need sustained visual and auditory focus in their task: Soldiers on the battlefield and motorcyclists. Soldiers on the battlefield use their visual and auditory capabilities to maintain awareness of their environment to guard themselves from enemy assault. One of the major challenges to coordination in a hazardous environment is maintaining communication between team members while mitigating cognitive load. Compromise in communication between team members may result in mistakes that can adversely affect the outcome of a mission. We have built two vibrotactile displays, Tactor I and Tactor II, each with nine actuators arranged in a three-by-three matrix with differing contact areas that can represent a total of 511 shapes. We used two dimensions of tactile medium, shapes and waveforms, to represent verb phrases and evaluated ability of users to perceive verb phrases the tactile code. We evaluated the effectiveness of communicating verb phrases while the users were performing two tasks simultaneously. The results showed that performing additional visual task did not affect the accuracy or the time taken to perceive tactile codes. Another challenge in coordinating Soldiers on a battlefield is navigating them to respective assembly areas. We have developed HaptiGo, a lightweight haptic ii vest that provides pedestrians both navigational intelligence and obstacle detection capabilities. HaptiGo consists of optimally-placed vibro-tactile sensors that utilize natural and small form factor interaction cues, thus emulating the sensation of being passively guided towards the intended direction. We evaluated HaptiGo and found that it was able to successfully navigate users with timely alerts of incoming obstacles without increasing cognitive load, thereby increasing their environmental awareness. Additionally, we show that users are able to respond to directional information without training. The needs of motorcyclists are different from those of Soldiers. Motorcyclists’ need to maintain visual and auditory situational awareness at all times is crucial since they are highly exposed on the road. Route guidance systems, such as the Garmin, have been well tested on automobilists, but remain much less safe for use by motorcyclists. Audio/visual routing systems decrease motorcyclists’ situational awareness and vehicle control, and thus increase the chances of an accident. To enable motorcyclists to take advantage of route guidance while maintaining situa- tional awareness, we created HaptiMoto, a wearable haptic route guidance system. HaptiMoto uses tactile signals to encode the distance and direction of approaching turns, thus avoiding interference with audio/visual awareness. Evaluations show that HaptiMoto is intuitive for motorcyclists, and a safer alternative to existing solutions.

Show BibTex

@mastersthesis{manojprasad2014PhD,
type = {{PhD Doctoral Dissertation}},
author = {Prasad, Manoj},
title = {Designing Tactile Interfaces for Abstract Interpersonal Communication, Pedestrian Navigation and Motocyclists Navigation},
school = {Texas A\&M University (TAMU)},
year = {2014},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 183 pages.}
}
2013 
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 Cummings, Danielle. Multimodal Interaction for Enhancing Team Coordination on the Battlefield. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. 201 pages. Texas A&M University (TAMU), College Station, TX, USA. August, 2013. First Position: NSA. Link
Show Abstract:

URI

Show BibTex

@mastersthesis{daniellecummings2013PhD,
type = {{PhD Doctoral Dissertation}},
author = {Cummings, Danielle},
title = {Multimodal Interaction for Enhancing Team Coordination on the Battlefield},
school = {Texas A\&M University (TAMU)},
year = {2013},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 201 pages.}
}
2013 
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 Sashikanth, Raju; Damaraju, Sriranga. An Exploration of Multi-touch Interaction Techniques. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. 145 pages. Texas A&M University (TAMU), College Station, TX, USA. August, 2013. First Position: Zillabyte. Link
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Research in multi-touch interaction has typically been focused on direct spatial manipulation; techniques have been created to result in the most intuitive mapping between the movement of the hand and the resultant change in the virtual object. As we attempt to design for more complex operations, the effectiveness of spatial manipulation as a metaphor becomes weak. We introduce two new platforms for multi-touch computing: a gesture recognition system, and a new interaction technique. I present Multi-Tap Sliders, a new interaction technique for operation in what we call non-spatial parametric spaces. Such spaces do not have an obvious literal spatial representation, (Eg.: exposure, brightness, contrast and saturation for image editing). The multi-tap sliders encourage the user to keep her visual focus on the tar- get, instead of requiring her to look back at the interface. My research emphasizes ergonomics, clear visual design, and fluid transition between modes of operation. Through a series of iterations, I develop a new technique for quickly selecting and adjusting multiple numerical parameters. Evaluations of multi-tap sliders show improvements over traditional sliders. To facilitate further research on multi-touch gestural interaction, I developed mGestr: a training and recognition system using hidden Markov models for designing a multi-touch gesture set. Our evaluation shows successful recognition rates of up to 95%. The recognition framework is packaged into a service for easy integration with existing applications.

Show BibTex

@mastersthesis{sashikanthdamaraju2013PhD,
type = {{PhD Doctoral Dissertation}},
author = {Sashikanth, Raju; Damaraju, Sriranga},
title = {An Exploration of Multi-touch Interaction Techniques},
school = {Texas A\&M University (TAMU)},
year = {2013},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 145 pages.}
}
2010 
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 Paulson, Brandon C.. Rethinking pen input interaction: Enabling freehand sketching through improved primitive recognition. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. 217 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2010. First Position: Capsure. Link
Show Abstract:

Online sketch recognition uses machine learning and artificial intelligence techniques to interpret markings made by users via an electronic stylus or pen. The goal of sketch recognition is to understand the intention and meaning of a particular user's drawing. Diagramming applications have been the primary beneficiaries of sketch recognition technology, as it is commonplace for the users of these tools to rst create a rough sketch of a diagram on paper before translating it into a machine understandable model, using computer-aided design tools, which can then be used to perform simulations or other meaningful tasks. Traditional methods for performing sketch recognition can be broken down into three distinct categories: appearance-based, gesture-based, and geometric-based. Although each approach has its advantages and disadvantages, geometric-based methods have proven to be the most generalizable for multi-domain recognition. Tools, such as the LADDER symbol description language, have shown to be capable of recognizing sketches from over 30 different domains using generalizable, geometric techniques. The LADDER system is limited, however, in the fact that it uses a low-level recognizer that supports only a few primitive shapes, the building blocks for describing higher-level symbols. Systems which support a larger number of primitive shapes have been shown to have questionable accuracies as the number of primitives increase, or they place constraints on how users must input shapes (e.g. circles can only be drawn in a clockwise motion; rectangles must be drawn starting at the top-left corner). This dissertation allows for a significant growth in the possibility of free-sketch recognition systems, those which place little to no drawing constraints on users. In this dissertation, we describe multiple techniques to recognize upwards of 18 primitive shapes while maintaining high accuracy. We also provide methods for producing confidence values and generating multiple interpretations, and explore the difficulties of recognizing multi-stroke primitives. In addition, we show the need for a standardized data repository for sketch recognition algorithm testing and propose SOUSA (sketch-based online user study application), our online system for performing and sharing user study sketch data. Finally, we will show how the principles we have learned through our work extend to other domains, including activity recognition using trained hand posture cues.

Show BibTex

@mastersthesis{brandonpaulson2010PhD,
type = {{PhD Doctoral Dissertation}},
author = {Paulson, Brandon C.},
title = {Rethinking pen input interaction: Enabling freehand sketching through improved primitive recognition},
school = {Texas A\&M University (TAMU)},
year = {2010},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 217 pages.}
}
2007 
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 Hammond, Tracy. LADDER: A Perceptually-Based Language to Simplify Sketch Recognition User Interface Development. Massachusetts Institute of Technology (MIT) PhD Doctoral Dissertation. Advisor: Randall Davis. 495 pages. Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. February, 2007. First Position: TAMU Asst. Professor. Link
Show Abstract:

Diagrammatic sketching is a natural modality of human-computer interaction that can be used for a variety of tasks, for example, conceptual design. Sketch recognition systems are currently being developed for many domains. However, they require signal-processing expertise if they are to handle the intricacies of each domain, and they are time-consuming to build. Our goal is to enable user interface designers and domain experts who may not have expertise in sketch recognition to be able to build these sketch systems. We created and implemented a new framework (FLUID - facilitating user interface development) in which developers can specify a domain description indicating how domain shapes are to be recognized, displayed, and edited. This description is then automatically transformed into a sketch recognition user interface for that domain. LADDER, a language using a perceptual vocabulary based on Gestalt principles, was developed to describe how to recognize, display, and edit domain shapes. A translator and a customizable recognition system (GUILD - a generator of user interfaces using ladder descriptions) are combined with a domain description to automatically create a domain specific recognition system. With this new technology, by writing a domain description, developers are able to create a new sketch interface for a domain, greatly reducing the time and expertise for the task. Continuing in pursuit of our goal to facilitate UI development, we noted that 1) human generated descriptions contained syntactic and conceptual errors, and that 2) it is more natural for a user to specify a shape by drawing it than by editing text. However, computer generated descriptions from a single drawn example are also flawed, as one cannot express all allowable variations in a single example. In response, we created a modification of the traditional model of active learning in which the system selectively generates its own near-miss examples and uses the human teacher as a source of labels. System generated near-misses offer a number of advantages. Human generated examples are tedious to create and may not expose problems in the current concept. It seems most effective for the near-miss examples to be generated by whichever learning participant (teacher or student) knows better where the deficiencies lie; this will allow the concepts to be more quickly and effectively refined. When working in a closed domain such as this one, the computer learner knows exactly which conceptual uncertainties remain, and which hypotheses need to be tested and confirmed. The system uses these labeled examples to auto- matically build a LADDER shape description, using a modification of the version spaces algorithm that handles interrelated constraints, and which also has the ability to learn negative and disjunctive constraints.

Show BibTex

@mastersthesis{tracyhammond2007PhD,
type = {{PhD Doctoral Dissertation}},
author = {Hammond, Tracy},
title = {LADDER: A Perceptually-Based Language to Simplify Sketch Recognition User Interface Development},
school = {Massachusetts Institute of Technology (MIT)},
year = {2007},
month = {February},
address = {Cambridge, MA, USA},
note = {Advisor: Randall Davis. 495 pages.}
}



Master's Theses


2017 
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 Jung In, Koh. Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. June, 2017. Link
Show Abstract:

Recent trends in computer-mediated communications (CMC) have not only led to expanded instant messaging (IM) through the use of images and videos, but have also expanded traditional text messaging with richer content, so-called visual communication markers (VCM) such as emoticons, emojis, and stickers. VCMs could prevent a potential loss of subtle emotional conversation in CMC, which is delivered by nonverbal cues that convey affective and emotional information. However, as the number of VCMs grows in the selection set, the problem of VCM entry needs to be addressed. Additionally, conventional ways for accessing VCMs continues to rely on input entry methods that are not directly and intimately tied to expressive nonverbal cues. One such form of expressive nonverbal that does exist and is well-studied come in the form of hand gestures. In this work, I propose a user-defined hand gesture set that is highly representative to VCMs and a two-stage hand gesture recognition system (feature-based, shape based) that distinguishes the user-defined hand gestures. The goal of this research is to provide users to be more immersed, natural, and quick in generating VCMs through gestures. The idea is for users to maintain the lower-bandwidth online communication of text messaging to largely retain its convenient and discreet properties, while also incorporating the advantages of higher-bandwidth online communication of video messaging by having users naturally gesture their emotions that are then closely mapped to VCMs. Results show that the accuracy of user-dependent is approximately 86% and the accuracy of user independent is about 82%.

Show BibTex

@mastersthesis{junginkoh2017MS,
type = {{MS Master's Thesis}},
author = {Jung In, Koh},
title = {Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {June},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2017 
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 Polsley, Seth. Identifying outcomes of care from medical records to improve doctor-patient communication. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 81 pages. Texas A&M University (TAMU), College Station, TX, USA. June, 2017. Link
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Show BibTex

@mastersthesis{sethpolsley2017MS,
type = {{MS Master's Thesis}},
author = {Polsley, Seth},
title = {Identifying outcomes of care from medical records to improve doctor-patient communication},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {June},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 81 pages.}
}
2017 
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 Cherian, Josh. Recognition of Everyday Activities through Wearable Sensors and Machine Learning. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond & Theresa Maldonado. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. First Position: TAMU PhD Student. Link
Show Abstract:

Over the past several years, the use of wearable devices has increased dramatically,primarily for fitness monitoring, largely due to their greater sensor reliability, increasedfunctionality, smaller size, increased ease of use, and greater affordability.These devices have helped many people of all ages live healthier lives and achieve their personal fitnessgoals, as they are able to see quantifiable and graphical results of their efforts every step of the way (i.e. in real-time). Yet, while these device systems work well within the fitnessdomain, they have yet to achieve a convincing level of functionality in the larger domainof healthcare. As an example, according to the Alzheimer’s Association, there are currently approxi-mately 5.5 million Americans with Alzheimer’s Disease and approximately 5.3 million ofthem are over the age of 65, comprising 10% of this age group in the U.S. The economictoll of this disease is estimated to be around $259 billion. By 2050 the number of Amer-icans with Alzheimer’s disease is predicted to reach around 16 million with an economictoll of over $1 trillion. There are other prevalent and chronic health conditions that arecritically important to monitor, such as diabetes, complications from obesity, congestiveheart failure, and chronic obstructive pulmonary disease (COPD) among others. The goal of this research is to explore and develop accurate and quantifiable sensingand machine learning techniques for eventual real-time health monitoring by wearabledevice systems. To that end, a two-tier recognition system is presented that is designed to identify health activities in a naturalistic setting based on accelerometer data of commonactivities. In Tier I a traditional activity recognition approach is employed to classify shortwindows of data, while in Tier II these classified windows are grouped to identify instancesof a specific activity. Everyday activities that were explored in this research include brushing one’s teeth, combing one’s hair, scratching one’s chin, washing one’s hands,taking medication, and drinking. Results show that an F-measure of 0.83 is achievablewhen identifying these activities from each other and an F-measure of of 0.82 is achievablewhen identifying instances of brushing teeth over the course of a day.

Show BibTex

@mastersthesis{joshcherian2017MS,
type = {{MS Master's Thesis}},
author = {Cherian, Josh},
title = {Recognition of Everyday Activities through Wearable Sensors and Machine Learning},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond \& Theresa Maldonado. }
}
2017 
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 Bhat, Aqib Niaz. Sketchography - Automatic grading of map sketches for geography education. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. Link
Show Abstract:

TBD

Show BibTex

@mastersthesis{aqibbhat2017MS,
type = {{MS Master's Thesis}},
author = {Bhat, Aqib Niaz},
title = {Sketchography - Automatic grading of map sketches for geography education},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2017 
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 Herrera Camara, Jorge Ivan. bFlow2code - From Hand-drawn Flowchart to Code Execution. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. Link
Show Abstract:

TBD

Show BibTex

@mastersthesis{jorgecamara2017MS,
type = {{MS Master's Thesis}},
author = {Herrera Camara, Jorge Ivan},
title = {bFlow2code - From Hand-drawn Flowchart to Code Execution},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2017 
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 Villanueva Luna, Nahum. ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. Link
Show Abstract:

TBD

Show BibTex

@mastersthesis{nahumvillanueva2017MS,
type = {{MS Master's Thesis}},
author = {Villanueva Luna, Nahum},
title = {ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2016 
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 Copesetty, Siddhartha Karthik . Labeling by Example. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. August, 2016. First Position: Uber. Link
Show Abstract:

TBD

Show BibTex

@mastersthesis{siddharthakarthik2016MS,
type = {{MS Master's Thesis}},
author = {Copesetty, Siddhartha Karthik },
title = {Labeling by Example},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2016 
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 Keshavabhotla, Swarna. PerSketchTivity: Recognition and Progressive Learning Analysis. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. August, 2016. First Position: FactSet Research Systems. Link
Show Abstract:

TBD

Show BibTex

@mastersthesis{swarnakeshavabhotla2016MS,
type = {{MS Master's Thesis}},
author = {Keshavabhotla, Swarna},
title = {PerSketchTivity: Recognition and Progressive Learning Analysis},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2016 
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 Ashok Kumar, Shalini Priya. Evaluation of Conceptual Sketches on Stylus-based Devices. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Google. Link
Show Abstract:

TBD

Show BibTex

@mastersthesis{shaliniashokkumar2016MS,
type = {{MS Master's Thesis}},
author = {Ashok Kumar, Shalini Priya},
title = {Evaluation of Conceptual Sketches on Stylus-based Devices},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2016 
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 Kaul, Purnendu. Gaze Assisted Classification of On-Screen Tasks (by Difficulty Level) and User Activities (Reading, Writing/Typing, Image-Gazing). Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Walmart Technologies. Link
Show Abstract:

TBD

Show BibTex

@mastersthesis{purnendukaul2016MS,
type = {{MS Master's Thesis}},
author = {Kaul, Purnendu},
title = {Gaze Assisted Classification of On-Screen Tasks (by Difficulty Level) and User Activities (Reading, Writing/Typing, Image-Gazing)},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2016 
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 Ray, Jaideep. Finding Similar Sketches. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Facebook. Link
Show Abstract:

Searching is an important tool for managing and navigating the massive amounts of data available in today’s information age. While new searching methods have be-come increasingly popular and reliable in recent years, such as image-based searching, these methods are more limited than text-based means in that they don’t allow generic user input. Sketch-based searching is a method that allows users to draw generic search queries and return similar drawn images, giving more user control over their search content. In this thesis, we present Sketchseeker, a system for indexing and searching across a large number of sketches quickly based on their similarity. The system includes several stages. First, sketches are indexed according to efficient and compact sketch descriptors. Second, the query retrieval subsystem considers sketches based on shape and structure similarity. Finally, a trained support vector machine classifier provides semantic filtering, which is then combined with median filtering to return the ranked results. SketchSeeker was tested on a large set of sketches against existing sketch similarity metrics, and it shows significant improvements in both speed and accuracy when compared to existing known techniques. The focus of this thesis is to outline the general components of a sketch retrieval system to find near similar sketches in real time.

Show BibTex

@mastersthesis{jaideepray2016MS,
type = {{MS Master's Thesis}},
author = {Ray, Jaideep},
title = {Finding Similar Sketches},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2015 
PublicationImagePublicationImage
 Guo, Shiqiang (Frank). ResuMatcher: A Personalized Resume-Job Matching System. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Treacy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2015. First Position: Amazon. Link
Show Abstract:

Today, online recruiting web sites such as Monster and Indeed.com have become one of the main channels for people to find jobs. These web platforms have provided their services for more than ten years, and have saved a lot of time and money for both job seekers and organizations who want to hire people. However, traditional information retrieval techniques may not be appropriate for users. The reason is because the number of results returned to a job seeker may be huge, so job seekers are required to spend a significant amount of time reading and reviewing their options. One popular approach to resolve this difficulty for users are recommender systems, which is a technology that has been studied for a long time. In this thesis we have made an effort to propose a personalized job-résumé matching system, which could help job seekers to find appropriate jobs more easily. We create a finite state transducer based information extraction library to extract models from résumés and job descriptions. We devised a new statistical-based ontology similarity measure to compare the résumé models and the job models. Since the most appropriate jobs will be returned first, the users of the system may get a better result than current job finding web sites. To evaluate the system, we computed Normalized Discounted Cumulative Gain (NDCG) and precision@k of our system, and compared to three other existing models as well as the live result from Indeed.com.

Show BibTex

@mastersthesis{shiqiangguo2015MS,
type = {{MS Master's Thesis}},
author = {Guo, Shiqiang (Frank)},
title = {ResuMatcher: A Personalized Resume-Job Matching System},
school = {Texas A\&M University (TAMU)},
year = {2015},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Treacy Hammond. }
}
2014 
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 Yin, Zhangliang. Chinese Calligraphist: A Sketch Based Learning Tool for Learning Written Chinese. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. August, 2014. First Position: Amazon. Link
Show Abstract:

Learning Chinese as a foreign language is becoming more and more popular in western countries, however it is also very hard to be proficient, especially in writing. The involvement of the teachers in the process of learning Chinese writing is extremely necessary because they can give timely critiques and feedbacks as well as correct the students’ bad writing habits. However, it is inadequate and inefficient of the large class capacity therefore it is urgent and necessary to design a computer-based system to help students in practice Chinese writing, correct their bad writing habits early, and give feedback personally. The current written Chinese learning tools such as online tutorials emphasize writing rules including stroke order, but it could not provide practicing sessions and feedback. Hashigo, a novel CALL (Computer Assisted Language Learning) system, introduced the concept of sketch-based learning, but it’s low level recognizer is not proper for Chinese character domain. Therefore in order to help western students learn Chinese with better understanding, we adopted LADDER description language, machine learning techniques, and sketch recognition algorithms to improve handwritten Chinese stroke recognition rate. With our multilayer perceptron recognizer, it improved Chinese stroke recognition accuracy by 15.7% than the average of the four basic recognizer. In feature selection study we found that the most important features were “the aspect of the bounding box”, and the “density metrics”, and “curviness”. We chose 8 most important features after the recursive selecting stabilized. We discovered that in most situations, feature recognition is more important than template recognition. Since the writing technique is emphasized while they are taught, only 2 templates is enough. It worked as well as 20 templates, which improved recognition speed dramatically. In conclusion, in this thesis our contribution is that we (1) proposed a natural way to describe Chinese characters; (2) implemented a hierarchical Chinese character recognizer combining LADDER with the multilayer perceptron low level recognizer; (3) analyzed the performance of different recognition schemes; (4) designed a sketch-based Chinese writing learning tool, Chinese Calligraphist; and (5) find the best feature combination to recognize Chinese strokes while improving the recognition accuracy.

Show BibTex

@mastersthesis{zhengliangyin2014MS,
type = {{MS Master's Thesis}},
author = {Yin, Zhangliang},
title = {Chinese Calligraphist: A Sketch Based Learning Tool for Learning Written Chinese},
school = {Texas A\&M University (TAMU)},
year = {2014},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2012 
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 Kim, Hong-Hoe. Analysis of Children's Sketches to Improve Recognition Accuracy in Sketch-Based Applications. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 105 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2012. First Position: TAMU PhD Student. Link
Show Abstract:

The current education systems in elementary schools are usually using traditional teaching methods such as paper and pencil or drawing on the board. The benefit of paper and pencil is their ease of use. Researchers have tried to bring this ease of use to computer-based educational systems through the use of sketch-recognition. Sketch-recognition allows students to draw naturally while at the same time receiving automated assistance and feedback from the computer. There are many sketch-based educational systems for children. However, current sketch-based educational systems use the same sketch recognizer for both adults and children. The problem of this approach is that the recognizers are trained by using sample data drawn by adults, even though the drawing patterns of children and adults are markedly different. We propose that if we make a separate recognizer for children, we can increase the recognition accuracy of shapes drawn by children. By creating a separate recognizer for children, we improved the recognition accuracy of children’s drawings from 81.25% (using the adults’ threshold) to 83.75% (using adjusted threshold for children). Additionally, we were able to automatically distinguish children’s drawings from adults’ drawings. We correctly identified the drawer’s age (age 3, 4, 7, or adult) with 78.3%. When distinguishing toddlers (age 3 and 4) from matures (age 7 and adult), we got a precision of 95.2% using 10-fold cross validation. When we removed adults and distinguished between toddlers and 7 year olds, we got a precision of 90.2%. Distinguishing between 3, 4, and 7 year olds, we got a precision of 86.8%. Furthermore, we revealed that there is a potential gender difference since our recognizer was more accurately able to recognize the drawings of female children (91.4%) than the male children (85.4%). Finally, this paper introduces a sketch-based teaching assistant tool for children, EasySketch, which teaches children how to draw digits and characters. Children can learn how to draw digits and characters by instructions and feedback.

Show BibTex

@mastersthesis{honghoekim2012MS,
type = {{MS Master's Thesis}},
author = {Kim, Hong-Hoe},
title = {Analysis of Children's Sketches to Improve Recognition Accuracy in Sketch-Based Applications},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 105 pages.}
}
2012 
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 Logsdon, Drew. Arm-Hand-Finger Video Game Interaction. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 108 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2012. First Position: IBM. Link
Show Abstract:

Despite the growing popularity and expansion of video game interaction techniques and research in the area of hand gesture recognition, the application of hand gesture video game interaction using arm, hand, and finger motion has not been extensively explored. Most current gesture-based approaches to video game interaction neglect the use of the fingers for interaction, but inclusion of the fingers will allow for more natural and unique interaction and merits further research. To implement arm, hand and finger-based interaction for the video game domain, several problems must be solved including gesture recognition, segmentation, hand visualization, and video game interaction that responds to arm, hand, and finger input. Solutions to each of these problems have been implemented. The potential of this interaction style is illustrated through the introduction of an arm, hand, and finger controlled video game system that responds to players' hand gestures. It includes a finger-gesture recognizer as well as a video game system employing various interaction styles. This consists of a first person shooter game, a driving game, and a menu interaction system. Several users interacted with and played these games, and this form of interaction is especially suitable for real time interaction in first-person games. This is perhaps the first implementation of its kind for video game interaction. Based on test results, arm, hand, and finger interaction a viable form of interaction that deserves further research. This implementation bridges the gap between existing gesture interaction methods and more advanced virtual reality techniques. It successfully combines the solutions to each problem mentioned above into a single, working video game system. This type of interaction has proved to be more intuitive than existing gesture controls in many situations and also less complex to implement than a full virtual reality setup. It allows more control by using the hands' natural motion and allows each hand to interact independently. It can also be reliably implemented using today's technology. This implementation is a base system that can be greatly expanded on. Many possibilities for future work can be applied to this form of interaction.

Show BibTex

@mastersthesis{drewlogsdon2012MS,
type = {{MS Master's Thesis}},
author = {Logsdon, Drew},
title = {Arm-Hand-Finger Video Game Interaction},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 108 pages.}
}
2012 
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 Lucchese, George. Sketch Recognition on Mobile Devices. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 54 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2012. First Position: IBM. Link
Show Abstract:

Sketch recognition allows computers to understand and model hand drawn sketches and diagrams. Traditionally sketch recognition systems required a pen based PC interface, but powerful mobile devices such as tablets and smartphones can provide a new platform for sketch recognition systems. We describe a new sketch recognition library, Strontium (SrL) that combines several existing sketch recognition libraries modified to run on both personal computers and on the Android platform. We analyzed the recognition speed and accuracy implications of performing low-level shape recognition on smartphones with touch screens. We found that there is a large gap in recognition speed on mobile devices between recognizing simple shapes and more complex ones, suggesting that mobile sketch interface designers limit the complexity of their sketch domains. We also found that a low sampling rate on mobile devices can affect recognition accuracy of complex and curved shapes. Despite this, we found no evidence to suggest that using a finger as an input implement leads to a decrease in simple shape recognition accuracy. These results show that the same geometric shape recognizers developed for pen applications can be used in mobile applications, provided that developers keep shape domains simple and ensure that input sampling rate is kept as high as possible.

Show BibTex

@mastersthesis{georgelucchese2012MS,
type = {{MS Master's Thesis}},
author = {Lucchese, George},
title = {Sketch Recognition on Mobile Devices},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 54 pages.}
}
2012 
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 Li, Wenzhe. Acoustic Based Sketch Recognition. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 91 pages. Texas A&M University (TAMU), College Station, TX, USA. August, 2012. First Position: USC PhD Student, Goldman Sachs. Link
Show Abstract:

Sketch recognition is an active research field, with the goal to automatically recognize hand-drawn diagrams by a computer. The technology enables people to freely interact with digital devices like tablet PCs, Wacoms, and multi-touch screens. These devices are easy to use and have become very popular in market. However, they are still quite costly and need more time to be integrated into existing systems. For example, handwriting recognition systems, while gaining in accuracy and capability, still must rely on users using tablet-PCs to sketch on. As computers get smaller, and smart-phones become more common, our vision is to allow people to sketch using normal pencil and paper and to provide a simple microphone, such as one from their smart-phone, to interpret their writings. Since the only device we need is a single simple microphone, the scope of our work is not limited to common mobile devices, but also can be integrated into many other small devices, such as a ring. In this thesis, we thoroughly investigate this new area, which we call acoustic based sketch recognition, and evaluate the possibilities of using it as a new interaction technique. We focus specifically on building a recognition engine for acoustic sketch recognition. We first propose a dynamic time wrapping algorithm for recognizing isolated sketch sounds using MFCC(Mel-Frequency Cesptral Coefficients). After analyzing its performance limitations, we propose improved dynamic time wrapping algorithms which work on a hybrid basis, using both MFCC and four global features including skewness, kurtosis, curviness and peak location. The proposed approaches provide both robustness and decreased computational cost. Finally, we evaluate our algorithms using acoustic data collected by the participants using a device's built-in microphone. Using our improved algorithm we were able to achieve an accuracy of 90% for a 10 digit gesture set, 87% accuracy for the 26 English characters and over 95% accuracy for a set of seven commonly used gestures.

Show BibTex

@mastersthesis{wenzheli2012MS,
type = {{MS Master's Thesis}},
author = {Li, Wenzhe},
title = {Acoustic Based Sketch Recognition},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 91 pages.}
}
2012 
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 Vides Ceron, Francisco. TAYouKi: A Sketch-Based Tutoring System for Young Kids. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 129 pages. Texas A&M University (TAMU), College Station, TX, USA. August, 2012. First Position: PayPal. Link
Show Abstract:

Intelligent tutoring systems (ITS) have proven to be effective tools for aiding in the instruction of new skills for young kids; however, interaction methods that employ traditional input devices such as the keyboard and mouse may present barriers to children who have yet learned how to write. Existing applications which utilize pen-input devices better mimic the physical act of writing, but few provide useful feedback to the users. This thesis presents a system specifically designed to serve as a useful tool in teaching children how to draw basic shapes, and helping them develop basic drawing and writing skills. The system uses a combination of sketch recognition techniques to interpret the handwritten strokes from sketches of the children, and then provides intelligent feedback based on what they draw. Our approach provides a virtual coach to assist teachers teaching the critical skills of drawing and handwriting. We do so by guiding children through a set of exercises of increasing complexity according to their progress, and at the same time keeping track of students' performance and engagement, giving them differentiated instruction and feedback. Our system would be like a virtual Teaching Assistant for Young Kids, hence we call it TAYouKi. We collected over five hundred hand-drawn shapes from grownups that had a clear understanding of what a particular geometric shape should look like. We used this data to test the recognition of our system. Following, we conducted a series of case studies with children in age group three to six to test the interactivity efficacy of the system. The studies served to gain important insights regarding the research challenges in different domains. Results suggest that our approach is appealable and engaging to children and can help in more effectively teach them how to draw and write.

Show BibTex

@mastersthesis{franciscovides2012MS,
type = {{MS Master's Thesis}},
author = {Vides Ceron, Francisco},
title = {TAYouKi: A Sketch-Based Tutoring System for Young Kids},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 129 pages.}
}
2010 
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 Taele, Paul. Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 96 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2010. First Position: TAMU PhD Student. Link
Show Abstract:

One of the challenges students face in studying an East Asian (EA) language (e.g., Chinese, Japanese, and Korean) as a second language is mastering their selected language’s written component. This is especially true for students with native fluency of English and deficient written fluency of another EA language. In order to alleviate the steep learning curve inherent in the properties of EA languages’ complicated writing scripts, language instructors conventionally introduce various written techniques such as stroke order and direction to allow students to study writing scripts in a systematic fashion. Yet, despite the advantages gained from written technique instruction, the physical presence of the language instructor in conventional instruction is still highly desirable during the learning process; not only does it allow instructors to offer valuable real-time critique and feedback interaction on students’ writings, but it also allows instructors to correct students’ bad writing habits that would impede mastery of the written language if not caught early in the learning process. The current generation of computer-assisted language learning (CALL) applications specific to written EA languages have therefore strived to incorporate writing-capable modalities in order to allow students to emulate their studies outside the classroom setting. Several factors such as constrained writing styles, and weak feedback and assessment capabilities limit these existing applications and their employed techniques from closely mimicking the benefits that language instructors continue to offer. In this thesis, I describe my geometric-based sketch recognition approach to several writing scripts in the EA languages while addressing the issues that plague existing CALL applications and the handwriting recognition techniques that they utilize. The approach takes advantage of A Language to Describe, Display, and Editing in Sketch Recognition (LADDER) framework to provide users with valuable feedback and assessment that not only recognizes the visual correctness of students’ written EA Language writings, but also critiques the technical correctness of their stroke order and direction. Furthermore, my approach provides recognition independent of writing style that allows students to learn with natural writing through size- and amount-independence, thus bridging the gap between beginner applications that only recognize single-square input and expert tools that lack written technique critique.

Show BibTex

@mastersthesis{paultaele2010MS,
type = {{MS Master's Thesis}},
author = {Taele, Paul},
title = {Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages},
school = {Texas A\&M University (TAMU)},
year = {2010},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 96 pages.}
}
2010 
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 Wolin, Aaron. Segmenting Hand-Drawn Strokes. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 160 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2010. First Position: Credera. Link
Show Abstract:

Pen-based interfaces utilize sketch recognition so users can create and interact with complex, graphical systems via drawn input. In order for people to freely draw within these systems, users' drawing styles should not be constrained. The low-level techniques involved with sketch recognition must then be perfected, because poor low-level accuracy can impair a user's interaction experience. Corner finding, also known as stroke segmentation, is one of the first steps to free-form sketch recognition. Corner finding breaks a drawn stroke into a set of primitive symbols such as lines, arcs, and circles, so that the original stoke data can be transformed into a more machine-friendly format. By working with sketched primitives, drawn objects can then be described in a visual language, noting what primitive shapes have been drawn and the shapes? geometric relationships to each other. We present three new corner finding techniques that improve segmentation accuracy. Our first technique, MergeCF, is a multi-primitive segmenter that splits drawn strokes into primitive lines and arcs. MergeCF eliminates extraneous primitives by merging them with their neighboring segments. Our second technique, ShortStraw, works with polyline-only data. Polyline segments are important since many domains use simple polyline symbols formed with squares, triangles, and arrows. Our ShortStraw algorithm is simple to implement, yet more powerful than previous polyline work in the corner finding literature. Lastly, we demonstrate how a combination technique can be used to pull the best corner finding results from multiple segmentation algorithms. This combination segmenter utilizes the best corners found from other segmentation techniques, eliminating many false negatives (missed primitive segmentations) from the final, low-level results. We will present the implementation and results from our new segmentation techniques, showing how they perform better than related work in the corner finding field. We will also discuss limitations of each technique, how we have sought to overcome those limitations, and where we believe the sketch recognition subfield of corner finding is headed.

Show BibTex

@mastersthesis{aaronwolin2010MS,
type = {{MS Master's Thesis}},
author = {Wolin, Aaron},
title = {Segmenting Hand-Drawn Strokes},
school = {Texas A\&M University (TAMU)},
year = {2010},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 160 pages.}
}
2009 
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 Dixon, Daniel. A Methodology for Using Assistive Sketch Recognition For Improving a Person’s Ability to Draw. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 114 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2009. First Position: ReelFX. Link
Show Abstract:

When asked to draw, most people are hesitant because they believe themselves unable to draw well. A human instructor can teach students how to draw by encouraging them to practice established drawing techniques and by providing personal and directed feedback to foster their artistic intuition and perception. This thesis describes the first methodology for a computer application to mimic a human instructor by providing direction and feedback to assist a student in drawing a human face from a photograph. Nine design principles were discovered and developed for providing such instruction, presenting reference media, giving corrective feedback, and receiving actions from the student. Face recognition is used to model the human face in a photograph so that sketch recognition can map a drawing to the model and evaluate it. New sketch recognition techniques and algorithms were created in order to perform sketch understanding on such subjective content. After two iterations of development and user studies for this methodology, the result is a computer application that can guide a person toward producing his/her own sketch of a human model in a reference photograph with step-bystep instruction and computer generated feedback.

Show BibTex

@mastersthesis{danieldixon2009MS,
type = {{MS Master's Thesis}},
author = {Dixon, Daniel},
title = {A Methodology for Using Assistive Sketch Recognition For Improving a Person’s Ability to Draw},
school = {Texas A\&M University (TAMU)},
year = {2009},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 114 pages.}
}
2001 
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 Hammond, Tracy. Ethnomathematics: Concept Definition and Research Perspectives. Columbia University MA Master's Thesis. Advisor: Ellen Marakowitz. 57 pages. Columbia University, New York, NY, USA. February, 2001. First Position: MIT PhD Student. Link
Show Abstract:

Although the term ethnomathematics has been in use in the anthropological literature for quite sometime now, a standard definition of the construct has yet to emerge. More than one definition exists, causing confusion and inhibiting systematic research on the subject. Most definitions loosely refer to it as the study of mathematical ideas of non-literate peoples (e.g., Ascher and Ascher, 1997), thereby ignoring or underplaying its profound relationship to culture. More importantly, current definitions are restrictive and too narrow to adequately explain phenomena that rightfully fall within its realm. Providing a conceptually grounded definition is a necessary first step to galvanize the thinking and investigative activity on the subject. My aim in this thesis is to offer such a definition and to descriptively examine its relevance for theory building and research on ethnomathematics. I start with a brief review of the current definitions of ethnomathematics, highlighting their parochial nature. I then propose an over-arching definition that derives its grounding from interaction and reciprocity-based models. My definition suggests ethnomathematics as the study of the evolution of mathematics that has shaped, and in turn shaped by, the values of groups of people. I then use this definition to historically examine how mathematics, despite its universality and constancy themes, suffers from culture-based disparities and has been influenced in its development by various social groups over time. Specifically, I examine the role of culture in the learning and use of math, gender capabilities in math, and how even racism has played a significant part in the evolution of math.

Show BibTex

@mastersthesis{tracyhammond2001MA,
type = {{MA Master's Thesis}},
author = {Hammond, Tracy},
title = {Ethnomathematics: Concept Definition and Research Perspectives},
school = {Columbia University},
year = {2001},
month = {February},
address = {New York, NY, USA},
note = {Advisor: Ellen Marakowitz. 57 pages.}
}



Undergraduate Honor's Theses


2017 
PublicationImagePublicationImage
 Leland, Jake. Recognizing Seatbelt-Fastening Activity Using Wearable Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond. 43 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. Coauthored with Ellen Stanfill. Link
Show Abstract:

Many fatal car accidents involve victims who were not wearing a seatbelt, even though systems for detecting such behavior and intervening to correct it already exist. Activity recognition using wearable sensors has been previously applied to many health-related fields with high accuracy. In this paper, activity recognition is used to generate an algorithm for real-time recognition of putting on a seatbelt, using a smartwatch. Initial data was collected from twelve participants to determine the validity of the approach. Novel features were extracted from the data and used to classify the action, with a final accuracy of 1.000 and an F-measure of 1.000 using the MultilayerPerceptron classifier using laboratory collected data. Then, an iterative real-time recognition user study was conducted to investigate classification accuracy in a naturalistic setting. The F-measure of naturalistic classification was 0.825 with MultilayerPerceptron. This work forms the basis for further studies which will aim to provide user feedback to increase seatbelt use.

Show BibTex

@mastersthesis{jakeleland2017BS,
type = {{Undergraduate Honors Thesis}},
author = {Leland, Jake},
title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 43 pages. Coauthored with Ellen Stanfill.}
}
2017 
PublicationImagePublicationImage
 Stanfill, Ellen. Recognizing Seatbelt-Fastening Activity Using Wearable Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond. 43 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. Coauthored with Jake Leland. Link
Show Abstract:

Many fatal car accidents involve victims who were not wearing a seatbelt, even though systems for detecting such behavior and intervening to correct it already exist. Activity recognition using wearable sensors has been previously applied to many health-related fields with high accuracy. In this paper, activity recognition is used to generate an algorithm for real-time recognition of putting on a seatbelt, using a smartwatch. Initial data was collected from twelve participants to determine the validity of the approach. Novel features were extracted from the data and used to classify the action, with a final accuracy of 1.000 and an F-measure of 1.000 using the MultilayerPerceptron classifier using laboratory collected data. Then, an iterative real-time recognition user study was conducted to investigate classification accuracy in a naturalistic setting. The F-measure of naturalistic classification was 0.825 with MultilayerPerceptron. This work forms the basis for further studies which will aim to provide user feedback to increase seatbelt use.

Show BibTex

@mastersthesis{ellenstanfill2017BS,
type = {{Undergraduate Honors Thesis}},
author = {Stanfill, Ellen},
title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 43 pages. Coauthored with Jake Leland.}
}
2016 
PublicationImagePublicationImage
 Brhlik, David. Enhancing Blind Navigation with the Use of Wearable Sensor Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond & Theresa Maldonado. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. Coauthored with Temiloluwa Otuyelu and Chad Young. Link
Show Abstract:

The goal of this research is to design and develop a wearable technology that will enable blind and visually impaired users to accomplish regular navigation without the assistance of another person, a guidance animal, or a cane. This new technology will have the distinct advantage of being more discreet and user friendly than an animal or cane, allowing the user to feel more comfortable as they use the device. Extensive research will be performed to determine the best user interface; this includes the location of the sensors on the body and how the device will communicate with the user. Potential devices could be designed to be worn on the shoes, belt, hat, glasses, or any number of other locations. The device may communicate with the wearer using vibrations, pressure, or sound. The best combination of wearability and communication will be built for user testing. This research will enable the visually impaired population to navigate more quickly, easily, and discretely; and help them learn their surroundings.

Show BibTex

@mastersthesis{davidbrhlik2016BS,
type = {{Undergraduate Honors Thesis}},
author = {Brhlik, David},
title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond \& Theresa Maldonado. Coauthored with Temiloluwa Otuyelu and Chad Young.}
}
2016 
PublicationImagePublicationImage
 Otuyelu,Temiloluwa. Enhancing Blind Navigation with the Use of Wearable Sensor Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond & Theresa Maldonado. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. Coauthored with David Brhlik and Chad Young. Link
Show Abstract:

The goal of this research is to design and develop a wearable technology that will enable blind and visually impaired users to accomplish regular navigation without the assistance of another person, a guidance animal, or a cane. This new technology will have the distinct advantage of being more discreet and user friendly than an animal or cane, allowing the user to feel more comfortable as they use the device. Extensive research will be performed to determine the best user interface; this includes the location of the sensors on the body and how the device will communicate with the user. Potential devices could be designed to be worn on the shoes, belt, hat, glasses, or any number of other locations. The device may communicate with the wearer using vibrations, pressure, or sound. The best combination of wearability and communication will be built for user testing. This research will enable the visually impaired population to navigate more quickly, easily, and discretely; and help them learn their surroundings.

Show BibTex

@mastersthesis{temiotuyelu2016BS,
type = {{Undergraduate Honors Thesis}},
author = {Otuyelu,Temiloluwa},
title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond \& Theresa Maldonado. Coauthored with David Brhlik and Chad Young.}
}
2016 
PublicationImagePublicationImage
 Young, Chad. Enhancing Blind Navigation with the Use of Wearable Sensor Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond & Theresa Maldonado. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. Coauthored with Temiloluwa Otuyelu and David Bhrlik. Link
Show Abstract:

The goal of this research is to design and develop a wearable technology that will enable blind and visually impaired users to accomplish regular navigation without the assistance of another person, a guidance animal, or a cane. This new technology will have the distinct advantage of being more discreet and user friendly than an animal or cane, allowing the user to feel more comfortable as they use the device. Extensive research will be performed to determine the best user interface; this includes the location of the sensors on the body and how the device will communicate with the user. Potential devices could be designed to be worn on the shoes, belt, hat, glasses, or any number of other locations. The device may communicate with the wearer using vibrations, pressure, or sound. The best combination of wearability and communication will be built for user testing. This research will enable the visually impaired population to navigate more quickly, easily, and discretely; and help them learn their surroundings.

Show BibTex

@mastersthesis{chadyoung2016BS,
type = {{Undergraduate Honors Thesis}},
author = {Young, Chad},
title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond \& Theresa Maldonado. Coauthored with Temiloluwa Otuyelu and David Bhrlik.}
}
2012 
PublicationImagePublicationImage
 Regmi, Sarin. Haptigo Tactile Navigation System. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2012. First Position: Motorola. Link
Show Abstract:

Tactile navigation systems employ the use of ones sense of touch with haptic feedback to communicate directions. This type of navigation presents a potentially faster and more accurate mode of navigation than preexisting visual or auditory forms. We developed a navigation system, HaptiGo, which uses a tactile harness controlled by an Android application to communicate directions. The use of a smartphone to provide GPS and compass information allows for a more compact and user-friendly system then previous tactile navigation systems. HaptiGo has been tested for functionality and user approval of tactile navigation. It was further tested to determine if tactile navigation provides for faster navigation times, increased path accuracy and improved environmental awareness compared to traditional maps navigation methods. We discuss the novel usage of smartphones for tactile navigation, the effectiveness of the HaptiGo navigation system, its accuracy compared to the use of static map-based navigation, and the potential benefits of tactile navigation.

Show BibTex

@mastersthesis{sarinregmi2012BS,
type = {{Undergraduate Honors Thesis}},
author = {Regmi, Sarin},
title = {Haptigo Tactile Navigation System},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}
2011 
PublicationImagePublicationImage
 Valentine, Stephanie. A Shape Comparison Technique for Use in Sketch-based Tutoring Systems. St. Mary's University of Minnesota Undergraduate Honors Thesis. Advisor: Ann Smith & Tracy Hammond. St. Mary's University of Minnesota, Winona, MN, USA. May, 2011. First Position: TAMU PhD Student. Link
Show Abstract:

To Be Entered

Show BibTex

@mastersthesis{stephanievalentine2011BS,
type = {{Undergraduate Honors Thesis}},
author = {Valentine, Stephanie},
title = {A Shape Comparison Technique for Use in Sketch-based Tutoring Systems},
school = {St. Mary's University of Minnesota},
year = {2011},
month = {May},
address = {Winona, MN, USA},
note = {Advisor: Ann Smith \& Tracy Hammond. }
}


Show All BibTex


@mastersthesis{stephanievalentine2016PhD,
type = {{PhD Doctoral Dissertation}},
author = {Valentine, Stephanie},
title = {Design, Deployment, Identity, \& Conformity: An Analysis of Children's Online Social Networks},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{folamialamudun2016PhD,
type = {{PhD Doctoral Dissertation}},
author = {Alamudun, Folami},
title = {Analysis of Visuo-cognitive Behavior in Screening Mammography},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{honghoekim2016PhD,
type = {{PhD Doctoral Dissertation}},
author = {Kim, Hong-hoe },
title = {A Fine Motor Skill Classifying Framework to Support Children's Self-regulation Skills and School Readiness},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{manojprasad2014PhD,
type = {{PhD Doctoral Dissertation}},
author = {Prasad, Manoj},
title = {Designing Tactile Interfaces for Abstract Interpersonal Communication, Pedestrian Navigation and Motocyclists Navigation},
school = {Texas A\&M University (TAMU)},
year = {2014},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 183 pages.}
}


@mastersthesis{daniellecummings2013PhD,
type = {{PhD Doctoral Dissertation}},
author = {Cummings, Danielle},
title = {Multimodal Interaction for Enhancing Team Coordination on the Battlefield},
school = {Texas A\&M University (TAMU)},
year = {2013},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 201 pages.}
}


@mastersthesis{sashikanthdamaraju2013PhD,
type = {{PhD Doctoral Dissertation}},
author = {Sashikanth, Raju; Damaraju, Sriranga},
title = {An Exploration of Multi-touch Interaction Techniques},
school = {Texas A\&M University (TAMU)},
year = {2013},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 145 pages.}
}


@mastersthesis{brandonpaulson2010PhD,
type = {{PhD Doctoral Dissertation}},
author = {Paulson, Brandon C.},
title = {Rethinking pen input interaction: Enabling freehand sketching through improved primitive recognition},
school = {Texas A\&M University (TAMU)},
year = {2010},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 217 pages.}
}


@mastersthesis{tracyhammond2007PhD,
type = {{PhD Doctoral Dissertation}},
author = {Hammond, Tracy},
title = {LADDER: A Perceptually-Based Language to Simplify Sketch Recognition User Interface Development},
school = {Massachusetts Institute of Technology (MIT)},
year = {2007},
month = {February},
address = {Cambridge, MA, USA},
note = {Advisor: Randall Davis. 495 pages.}
}


@mastersthesis{junginkoh2017MS,
type = {{MS Master's Thesis}},
author = {Jung In, Koh},
title = {Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {June},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{sethpolsley2017MS,
type = {{MS Master's Thesis}},
author = {Polsley, Seth},
title = {Identifying outcomes of care from medical records to improve doctor-patient communication},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {June},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 81 pages.}
}


@mastersthesis{joshcherian2017MS,
type = {{MS Master's Thesis}},
author = {Cherian, Josh},
title = {Recognition of Everyday Activities through Wearable Sensors and Machine Learning},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond \& Theresa Maldonado. }
}


@mastersthesis{aqibbhat2017MS,
type = {{MS Master's Thesis}},
author = {Bhat, Aqib Niaz},
title = {Sketchography - Automatic grading of map sketches for geography education},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{jorgecamara2017MS,
type = {{MS Master's Thesis}},
author = {Herrera Camara, Jorge Ivan},
title = {bFlow2code - From Hand-drawn Flowchart to Code Execution},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{nahumvillanueva2017MS,
type = {{MS Master's Thesis}},
author = {Villanueva Luna, Nahum},
title = {ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{siddharthakarthik2016MS,
type = {{MS Master's Thesis}},
author = {Copesetty, Siddhartha Karthik },
title = {Labeling by Example},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{swarnakeshavabhotla2016MS,
type = {{MS Master's Thesis}},
author = {Keshavabhotla, Swarna},
title = {PerSketchTivity: Recognition and Progressive Learning Analysis},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{shaliniashokkumar2016MS,
type = {{MS Master's Thesis}},
author = {Ashok Kumar, Shalini Priya},
title = {Evaluation of Conceptual Sketches on Stylus-based Devices},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{purnendukaul2016MS,
type = {{MS Master's Thesis}},
author = {Kaul, Purnendu},
title = {Gaze Assisted Classification of On-Screen Tasks (by Difficulty Level) and User Activities (Reading, Writing/Typing, Image-Gazing)},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{jaideepray2016MS,
type = {{MS Master's Thesis}},
author = {Ray, Jaideep},
title = {Finding Similar Sketches},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{shiqiangguo2015MS,
type = {{MS Master's Thesis}},
author = {Guo, Shiqiang (Frank)},
title = {ResuMatcher: A Personalized Resume-Job Matching System},
school = {Texas A\&M University (TAMU)},
year = {2015},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Treacy Hammond. }
}


@mastersthesis{zhengliangyin2014MS,
type = {{MS Master's Thesis}},
author = {Yin, Zhangliang},
title = {Chinese Calligraphist: A Sketch Based Learning Tool for Learning Written Chinese},
school = {Texas A\&M University (TAMU)},
year = {2014},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{honghoekim2012MS,
type = {{MS Master's Thesis}},
author = {Kim, Hong-Hoe},
title = {Analysis of Children's Sketches to Improve Recognition Accuracy in Sketch-Based Applications},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 105 pages.}
}


@mastersthesis{drewlogsdon2012MS,
type = {{MS Master's Thesis}},
author = {Logsdon, Drew},
title = {Arm-Hand-Finger Video Game Interaction},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 108 pages.}
}


@mastersthesis{georgelucchese2012MS,
type = {{MS Master's Thesis}},
author = {Lucchese, George},
title = {Sketch Recognition on Mobile Devices},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 54 pages.}
}


@mastersthesis{wenzheli2012MS,
type = {{MS Master's Thesis}},
author = {Li, Wenzhe},
title = {Acoustic Based Sketch Recognition},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 91 pages.}
}


@mastersthesis{franciscovides2012MS,
type = {{MS Master's Thesis}},
author = {Vides Ceron, Francisco},
title = {TAYouKi: A Sketch-Based Tutoring System for Young Kids},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {August},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 129 pages.}
}


@mastersthesis{paultaele2010MS,
type = {{MS Master's Thesis}},
author = {Taele, Paul},
title = {Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages},
school = {Texas A\&M University (TAMU)},
year = {2010},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 96 pages.}
}


@mastersthesis{aaronwolin2010MS,
type = {{MS Master's Thesis}},
author = {Wolin, Aaron},
title = {Segmenting Hand-Drawn Strokes},
school = {Texas A\&M University (TAMU)},
year = {2010},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 160 pages.}
}


@mastersthesis{danieldixon2009MS,
type = {{MS Master's Thesis}},
author = {Dixon, Daniel},
title = {A Methodology for Using Assistive Sketch Recognition For Improving a Person’s Ability to Draw},
school = {Texas A\&M University (TAMU)},
year = {2009},
month = {December},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 114 pages.}
}


@mastersthesis{tracyhammond2001MA,
type = {{MA Master's Thesis}},
author = {Hammond, Tracy},
title = {Ethnomathematics: Concept Definition and Research Perspectives},
school = {Columbia University},
year = {2001},
month = {February},
address = {New York, NY, USA},
note = {Advisor: Ellen Marakowitz. 57 pages.}
}


@mastersthesis{jakeleland2017BS,
type = {{Undergraduate Honors Thesis}},
author = {Leland, Jake},
title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 43 pages. Coauthored with Ellen Stanfill.}
}


@mastersthesis{ellenstanfill2017BS,
type = {{Undergraduate Honors Thesis}},
author = {Stanfill, Ellen},
title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology},
school = {Texas A\&M University (TAMU)},
year = {2017},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. 43 pages. Coauthored with Jake Leland.}
}


@mastersthesis{davidbrhlik2016BS,
type = {{Undergraduate Honors Thesis}},
author = {Brhlik, David},
title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond \& Theresa Maldonado. Coauthored with Temiloluwa Otuyelu and Chad Young.}
}


@mastersthesis{temiotuyelu2016BS,
type = {{Undergraduate Honors Thesis}},
author = {Otuyelu,Temiloluwa},
title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond \& Theresa Maldonado. Coauthored with David Brhlik and Chad Young.}
}


@mastersthesis{chadyoung2016BS,
type = {{Undergraduate Honors Thesis}},
author = {Young, Chad},
title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},
school = {Texas A\&M University (TAMU)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond \& Theresa Maldonado. Coauthored with Temiloluwa Otuyelu and David Bhrlik.}
}


@mastersthesis{sarinregmi2012BS,
type = {{Undergraduate Honors Thesis}},
author = {Regmi, Sarin},
title = {Haptigo Tactile Navigation System},
school = {Texas A\&M University (TAMU)},
year = {2012},
month = {May},
address = {College Station, TX, USA},
note = {Advisor: Tracy Hammond. }
}


@mastersthesis{stephanievalentine2011BS,
type = {{Undergraduate Honors Thesis}},
author = {Valentine, Stephanie},
title = {A Shape Comparison Technique for Use in Sketch-based Tutoring Systems},
school = {St. Mary's University of Minnesota},
year = {2011},
month = {May},
address = {Winona, MN, USA},
note = {Advisor: Ann Smith \& Tracy Hammond. }
}


Show All Latex Include

\subsection{Dissertations and Theses}

\subsubsection{PhD Dissertations}
\begin{enumerate}
\item \bibentry{stephanievalentine2016PhD}
\item \bibentry{folamialamudun2016PhD}
\item \bibentry{honghoekim2016PhD}
\item \bibentry{manojprasad2014PhD}
\item \bibentry{daniellecummings2013PhD}
\item \bibentry{sashikanthdamaraju2013PhD}
\item \bibentry{brandonpaulson2010PhD}
\item \bibentry{tracyhammond2007PhD}
\end{enumerate}

\subsubsection{Master's Theses}
\begin{enumerate}
\item \bibentry{junginkoh2017MS}
\item \bibentry{sethpolsley2017MS}
\item \bibentry{joshcherian2017MS}
\item \bibentry{aqibbhat2017MS}
\item \bibentry{jorgecamara2017MS}
\item \bibentry{nahumvillanueva2017MS}
\item \bibentry{siddharthakarthik2016MS}
\item \bibentry{swarnakeshavabhotla2016MS}
\item \bibentry{shaliniashokkumar2016MS}
\item \bibentry{purnendukaul2016MS}
\item \bibentry{jaideepray2016MS}
\item \bibentry{shiqiangguo2015MS}
\item \bibentry{zhengliangyin2014MS}
\item \bibentry{honghoekim2012MS}
\item \bibentry{drewlogsdon2012MS}
\item \bibentry{georgelucchese2012MS}
\item \bibentry{wenzheli2012MS}
\item \bibentry{franciscovides2012MS}
\item \bibentry{paultaele2010MS}
\item \bibentry{aaronwolin2010MS}
\item \bibentry{danieldixon2009MS}
\item \bibentry{tracyhammond2001MA}
\end{enumerate}

\subsubsection{Undergraduate Honor's Theses}
\begin{enumerate}
\item \bibentry{jakeleland2017BS}
\item \bibentry{ellenstanfill2017BS}
\item \bibentry{davidbrhlik2016BS}
\item \bibentry{temiotuyelu2016BS}
\item \bibentry{chadyoung2016BS}
\item \bibentry{sarinregmi2012BS}
\item \bibentry{stephanievalentine2011BS}
\end{enumerate}