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
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology)
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Clustering
Immune-based algorithms for dynamic optimization
Information Sciences: an International Journal
Modelling and segmenting subunits for sign language recognition based on hand motion analysis
Pattern Recognition Letters
Cluster-based genetic segmentation of time series with DWT
Pattern Recognition Letters
Clustering of time series data-a survey
Pattern Recognition
Toward modeling sign language coarticulation
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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The paper considers automatic vision based modelling and recognition of sign language expressions using smaller units than words. Modelling gestures with subunits is similar to modelling speech by means of phonemes. To define the subunits a data---driven procedure is proposed. The procedure consists in partitioning time series of feature vectors obtained from video material into subsequences which form homogeneous clusters. The cut points are determined by an optimisation procedure based on quality assessment of the resulting clusters. Then subunits are selected in two ways: as clusters' representatives or as hidden Markov models of clusters. These two approaches result in differences in classifier design. Details of the solution and results of experiments on a database of 101 Polish words and 35 sentences used at the doctor's and in the post office are given. Our subunit---based classifiers outperform their whole---word---based counterpart, which is particularly evident when new expressions are recognised on the basis of a small number of examples.