Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time American Sign Language recognition from video using hidden Markov models
ISCV '95 Proceedings of the International Symposium on Computer Vision
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sign Language Spotting with a Threshold Model Based on Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A dynamic gesture recognition system for the Korean sign language (KSL)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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The essential research related to sign language recognition states that there is a need of remarkable progress in this domain. Selecting features is decisive to gesture recognition, as hand gestures are very fine in shape variation, motion and textures. For static posture recognition, although it is possible to recognize hand posture by extracting some geometric features such as fingertips, finger directions it is not reliable due to self -- occlusion and lighting condition. Silhouette and textures, however, they are inadequate in recognition process. Therefore this work focuses on two of the research problems comprising automatic sign language recognition, namely robust segmentation techniques for consistent detection, preserving the shape contour which is therefore useful for textural feature extraction. The discrimination ability of the two segmentation methods for texture computation is observed and compared by objective parameters. Experiments are done in various camera based images and that it explores the need for prior effective contour segmentation and textural features for constructing a precision based recognition system for sign language.