Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robustly estimating changes in image appearance
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Space-Time Behavior Based Correlation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Fuzzy clustering of human motor motion
Applied Soft Computing
Cyclic and non-cyclic gesture spotting and classification in real-time applications
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
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In this paper, we introduce a tuned eigenspace technique so as to classify human motion. The method presented here overcomes those problems related to articulated motion and dress texture effects by learning various human motions in terms of their sequential postures in an eigenspace. In order to cope with the variability inherent to articulated motion, we propose a method to tune the set of sequential eigenspaces. Once the learnt tuned eigenspaces are at hand, the recognition task then becomes a nearest-neighbor search over the eigenspaces. We show how our tuned eigenspace method can be used for purposes of real-world and synthetic pose recognition. We also discuss and overcome the problem related to clothing texture that occurs in real-world data, and propose a background subtraction method to employ the method in out-door environment. We provide results on synthetic imagery for a number of human poses and illustrate the utility of the method for the purposes of human motion recognition.