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Proceedings Paper

Motion tracking of color image sequences using neural networks
Author(s): Haruyuki Iwata; Hiroshi Nagahashi
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Paper Abstract

Region segmentation of images is a well-known 'ill-posed problem', and a specific algorithm like regularization seems to be available. In this paper, an active region segmentation algorithm based on a regularization approach using the Hopfield neural network is proposed. The objective function to be minimized by the network is defined based on the criteria that integrates region growing and edge detection for the image segmentation. The energy of the network tends to converge on a local minimum, sot hat pyramid images are used to avoid such local minima and to achieve fast convergence. Moreover, the active region segmentation algorithm is applied to a sequence of color images to track an object region that change in appearance through complex and nonstationary background/foreground situations. Experimental results show that it's possible to segment images and track the object region using the minimization principle of the energy function of the Hopfield neural network.

Paper Details

Date Published: 10 January 1997
PDF: 11 pages
Proc. SPIE 3024, Visual Communications and Image Processing '97, (10 January 1997); doi: 10.1117/12.263231
Show Author Affiliations
Haruyuki Iwata, Tokyo Institute of Technology (Japan)
Hiroshi Nagahashi, Tokyo Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 3024:
Visual Communications and Image Processing '97
Jan Biemond; Edward J. Delp III, Editor(s)

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