Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
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
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
Recognition of gestures in Arabic sign language using neuro-fuzzy systems
Artificial Intelligence
Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A Continuous Chinese Sign Language Recognition System
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Relevant Features for Video-Based Continuous Sign Language Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An Approach Based on Phonemes to Large Vocabulary Chinese Sign Language Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Novel Approach to Automatically Extracting Basic Units from Chinese Sign Language
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning recurrent behaviors from heterogeneous multivariate time-series
Artificial Intelligence in Medicine
Viewpoint invariant sign language recognition
Computer Vision and Image Understanding
Modelling and segmenting subunits for sign language recognition based on hand motion analysis
Pattern Recognition Letters
Distribution-Based Dimensionality Reduction Applied to Articulated Motion Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering multivariate motifs using subsequence density estimation and greedy mixture learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
BoostMap: a method for efficient approximate similarity rankings
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Statistical motion model based on the change of feature relationships: human gait-based recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present a probabilistic framework to automatically learn models of recurring signs from multiple sign language video sequences containing the vocabulary of interest. We extract the parts of the signs that are present in most occurrences of the sign in context and are robust to the variations produced by adjacent signs. Each sentence video is first transformed into a multidimensional time series representation, capturing the motion and shape aspects of the sign. Skin color blobs are extracted from frames of color video sequences, and a probabilistic relational distribution is formed for each frame using the contour and edge pixels from the skin blobs. Each sentence is represented as a trajectory in a low dimensional space called the space of relational distributions. Given these time series trajectories, we extract signemes from multiple sentences concurrently using iterated conditional modes (ICM). We show results by learning single signs from a collection of sentences with one common pervading sign, multiple signs from a collection of sentences with more than one common sign, and single signs from a mixed collection of sentences. The extracted signemes demonstrate that our approach is robust to some extent to the variations produced within a sign due to different contexts. We also show results whereby these learned sign models are used for spotting signs in test sequences.