@conference{monteiro2012assets,

title = {Design and Evaluation of Classifier for Identifying Sign Language Videos in Video Sharing Sites},

author = {C Monteiro and R Gutierrez-Osuna and F Shipman},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/monteiro2012assets.pdf},

year  = {2012},

date = {2012-10-22},

booktitle = {13th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2012).},

abstract = {Video sharing sites provide an opportunity for the collection and use of sign language presentations about a wide range of topics. Currently, locating sign language videos (SL videos) in such sharing sites relies on the existence and accuracy of tags, titles or other metadata indicating the content is in sign language. In this paper, we describe the design and evaluation of a classifier for distinguishing between sign language videos and other videos. A test collection of SL videos and videos likely to be incorrectly recognized as SL videos (likely false positives) was created for evaluating alternative classifiers. Five video features thought to be potentially valuable for this task were developed based on common video analysis techniques. A comparison of the relative value of the five video features shows that a measure of the symmetry of movement relative to the face is the best feature for distinguishing sign language videos. Overall, an SVM classifier provided with all five features achieves 82% precision and 90% recall when tested on the challenging test collection. The performance would be considerably higher when applied to the more varied collections of large video sharing sites.},

keywords = {Computer vision, Gestures},

pubstate = {published},

tppubtype = {conference}

}

@conference{lucchese2012chi,

title = {GestureCommander: continuous touch-based gesture prediction},

author = {G Lucchese and M Field and J Ho and R Gutierrez-Osuna and T Hammond},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/lucchese2012chi.pdf},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceedings of the 2012 ACM annual conference extended abstracts on Human Factors in Computing Systems Extended Abstracts},

pages = {1925--1930},

organization = {ACM},

abstract = {GestureCommander is a touch-based gesture control system for mobile devices that is able to recognize gestures as they are being performed. Continuous recognition allows the system to provide visual feedback to the user and to anticipate user commands to possibly decrease perceived response time. To achieve this goal we employ two Hidden Markov Model (HMM) systems, one for recognition and another for generating visual feedback. We analyze a set of geometric features used in other gesture recognition systems and determine a subset that works best for HMMs. Finally we demonstrate the practicality of our recognition HMMs in a proof of concept mobile application for Google's Android mobile platform that has a recognition accuracy rate of 96% over 15 distinct gestures.},

keywords = {Gestures, Mobile computing},

pubstate = {published},

tppubtype = {conference}

}

@conference{paulson2008no,

title = {What!?! no Rubine features?: using geometric-based features to produce normalized confidence values for sketch recognition},

author = {B Paulson and P Rajan and P Davalos and R Gutierrez-Osuna and T Hammond},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/paulson2008no.pdf},

year  = {2008},

date = {2008-01-01},

booktitle = {HCC Workshop: Sketch Tools for Diagramming},

pages = {57--63},

abstract = {As pen-based interfaces become more popular in today’s applications, the need for algorithms to accurately recognize hand-drawn sketches and shapes has increased. In many cases, complex shapes can be constructed hierarchically as a combination of smaller primitive shapes meeting certain geometric constraints.However, in order to construct higher level shapes, it is imperative to accurately recognize the lower-level primitives. Two approaches have become widespread in the sketch recognition field for recognizing lower-level primitives: gesture-based recognition and geometric-based recognition. Our goal is to use a hybrid approach that combines features from both traditional gesture based recognition systems and geometric-based recognition systems. In this paper, we show that we can produce a system with high recognition rates while providing the added benefit of being able to produce normalized confidence values for alternative interpretations;something most geometric-based recognizers lack. Moresignificantly, results from feature subset selection indicate that geometric features aid the recognition process more than gesture-based features when given naturally sketched data.},

keywords = {Gestures},

pubstate = {published},

tppubtype = {conference}

}