Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for recognizing the simultaneous aspects of American sign language
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Statistical color models with application to skin detection
International Journal of Computer Vision
A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Signer-Independent Sign Language Recognition Based on SOFM/HMM
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
3D Tracking = Classification + Interpolation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Exploiting Depth Discontinuities for Vision-Based Fingerspelling Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Learning-based dynamic coupling of discrete and continuous trackers
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Gesture recognition using image comparison methods
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Vision-Based recognition of fingerspelled acronyms using hierarchical temporal memory
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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We propose a new principle for recognizing fingerspelling sequences from American Sign Language (ASL) Instead of training a system to recognize the static posture for each letter from an isolated frame, we recognize the dynamic gestures corresponding to transitions between letters This eliminates the need for an explicit temporal segmentation step, which we show is error-prone at speeds used by native signers We present results from our system recognizing 82 different words signed by a single signer, using more than an hour of training and test video We demonstrate that recognizing letter-to-letter transitions without temporal segmentation is feasible and results in improved performance.