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Constrained contour matching in hand posture recognition

Wilkowski A., Kasprzak W.

Image Processing & Communications

2009

Vol. 14, no 2-3

31-41

In this paper constrained contour models are applied for hand posture-recognition in single color images. In particular, the proposed algorithm utilizes a class of physics-based modelling methods called Deformable Templates [1],[2],[3]. After color-based image segmentation a contour hypothesis is detected and some features are extracted, suitable for comparison with the template's geometric properties. Several metrics for matching contour templates against image data are discussed. The described methods are evaluated experimentally and referred to a known hand posture recognition algorithm.

Neural network models for combinatorial optimization : a survey of deterministic, stochastic and chaotic approaches

Smith K., Potvin J., Kwok T.

Control and Cybernetics

2002

Vol. 31, no 2

183-216

This paper serves as a tutorial on the use of neural networks for solving combinatorial optimization problems. It reviews the two main classes of neural network models : the gradient-based neural networks such as the Hopfield network, and the deformable template approaches such as the elastic net method and self organizing maps. In each class, the original model is presented, its limitations discussed, and subsequent developments and extensions are reviewed. Particular emphasis is placed on stochastic and chaotic variations on the neural network models designed to improve the optimization performance. Finally, the performance of these neural network models is compared and discussed relative to other heuristic approaches.