Translation and scale-invariant gesture recognition in complex scenes
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Nearest neighbor search methods for handshape recognition
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Tracking with Dynamic Hidden-State Shape Models
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Modelling and recognition of the linguistic components in American Sign Language
Image and Vision Computing
A database-based framework for gesture recognition
Personal and Ubiquitous Computing
Rotation invariant non-rigid shape matching in cluttered scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Features extraction from hand images based on new detection operators
Pattern Recognition
The Global-Local transformation for noise resistant shape representation
Computer Vision and Image Understanding
Visual pathways for shape abstraction
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
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This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term "variable shape structure" is used to characterize object classes in which some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models (HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using a probabilistic framework. A polynomial inference algorithm automatically determines object location, orientation, scale and structure by finding the globally optimal registration of model states with the image features, even in the presence of clutter. Experiments with real images demonstrate that the proposed method can localize objects of variable shape structure with high accuracy. For the task of hand shape localization and structure identification, the proposed method is significantly more accurate than previously proposed methods based on chamfer-distance matching. Furthermore, by integrating simple temporal constraints, the proposed method gains speed-ups of more than an order of magnitude, and produces highly accurate results in experiments on non-rigid hand motion tracking.