Recognition of sign language motion images
Pattern Recognition
Review and analysis of solutions of the three point perspective pose estimation problem
International Journal of Computer Vision
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Appearance-based hand sign recognition from intensity image sequences
Computer Vision and Image Understanding
Linear fitting with missing data for structure-from-motion
Computer Vision and Image Understanding
Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-Time Continuous Gesture Recognition System for Sign Language
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Purdue RVL-SLLL ASL Database for Automatic Recognition of American Sign Language
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A SRN/HMM System for Signer-Independent Continuous Sign Language Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Recognition of Local Features for Camera-Based Sign Language Recognition System
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
American sign language recognition: reducing the complexity of the task with phoneme-based modeling and parallel hidden markov models
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-Dimensional Shape and Motion Reconstruction for the Analysis of American Sign Language
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Rapid Signer Adaptation for Isolated Sign Language Recognition
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
American sign language recognition in game development for deaf children
Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
Detecting Objects of Variable Shape Structure With Hidden State Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sign Language Recognition by Combining Statistical DTW and Independent Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distribution-Based Dimensionality Reduction Applied to Articulated Motion Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new instrumented approach for translating American sign language into sound and text
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A fuzzy rule-based approach to spatio-temporal hand gesturerecognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Image and Vision Computing
A framework for continuous multimodal sign language recognition
Proceedings of the 2009 international conference on Multimodal interfaces
Human-inspired search for redundancy in automatic sign language recognition
ACM Transactions on Applied Perception (TAP)
The Journal of Machine Learning Research
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The manual signs in sign languages are generated and interpreted using three basic building blocks: handshape, motion, and place of articulation. When combined, these three components (together with palm orientation) uniquely determine the meaning of the manual sign. This means that the use of pattern recognition techniques that only employ a subset of these components is inappropriate for interpreting the sign or to build automatic recognizers of the language. In this paper, we define an algorithm to model these three basic components form a single video sequence of two-dimensional pictures of a sign. Recognition of these three components are then combined to determine the class of the signs in the videos. Experiments are performed on a database of (isolated) American Sign Language (ASL) signs. The results demonstrate that, using semi-automatic detection, all three components can be reliably recovered from two-dimensional video sequences, allowing for an accurate representation and recognition of the signs.