A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Gesture recognition using recurrent neural networks
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Device Independence and Extensibility in Gesture Recognition
VR '03 Proceedings of the IEEE Virtual Reality 2003
Hand Gesture Recognition Following the Dynamics of a Topology-Preserving Network
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gesture recognition with a Wii controller
Proceedings of the 2nd international conference on Tangible and embedded interaction
TreeHeaven: a table game using vision-based gesture recognition
Proceedings of the 2011 ACM symposium on The role of design in UbiComp research & practice
Gesture-based interaction and communication: automated classification of hand gesture contours
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Framework for Sign Language Sentence Recognition by Commonsense Context
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Large vocabulary sign language recognition based on fuzzy decision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Finding the convex hull of a simple polygon
Pattern Recognition Letters
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Algorithms for hand feature extraction used in gesture recognition systems have some problems such as unnecessary information gathering. This paper proposes a novel method for feature extraction in gesture recognition systems based on the Local Contour Sequence (LCS). It is called the Convexity Local Contour Sequence (CLCS) and represents the hand shape only with the most significant information. This generates a smaller output result, but capable to model an entire dynamic gesture. It is used to classify dynamic gestures with an Elman Recurrent Network and Hidden Markov Model and presents a better result compared to regular LCS.