Fundamentals of speech recognition
Fundamentals of speech recognition
Looking at People: Sensing for Ubiquitous and Wearable Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video-Based Sign Language Recognition Using Hidden Markov Models
Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
Vision-Based Gesture Recognition: A Review
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
American sign language recognition: reducing the complexity of the task with phoneme-based modeling and parallel hidden markov models
Enhanced (PC)2 A for face recognition with one training image per person
Pattern Recognition Letters
Resampling for Face Detection by Self-Adaptive Genetic Algorithm
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Mean Shift Is a Bound Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Chinese sign language recognition system based on SOFM/SRN/HMM
Pattern Recognition
Resampling for face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Deciphering gestures with layered meanings and signer adaptation
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Re-sampling for chinese sign language recognition
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Static gesture quantization and DCT based sign language generation
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
A fuzzy rule-based approach to spatio-temporal hand gesturerecognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Gesture-based interaction and communication: automated classification of hand gesture contours
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Robust fusion of uncertain information
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Large vocabulary sign language recognition based on fuzzy decision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis
AmI '09 Proceedings of the European Conference on Ambient Intelligence
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The lack of training samples is an important problem in the field of sign language recognition. This paper presents a method of generating synthetic multi-stream samples so as to enlarge the training set of sign. The mean shift algorithm is able to obtain the directions of maximum increase and decrease in the density function, so it is used to control the direction and the intensity of synthetic data generation. The synthetic data generation proposed in this paper satisfies the need of the synthetic samples, which must include a large amount of effective information of unspecific signers. The proposed method is evaluated under different experimental conditions, such as the generating strategy, the capacity of the model, as well as the intensity and direction of the generating process. The results show that in most cases recognition accuracy is improved; and in some, even greatly improved.