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Effect of noise on the performance of the temporally-sequenced intelligent block-matching and motion-segmentation algorithm

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2 Author(s)
Xiaofu Zhang ; Complex Adaptive Syst. Laboratory, Cincinnati Univ., OH, USA ; Minai, A.A.

Most algorithms for motion-based segmentation depend on the system's ability to estimate optic flow from successive image frames. Block-matching is often used for this, but it faces the problems of noise-sensitivity and texture-insufficiency. Recently, we proposed a two-pathway approach based on locally coupled neural networks to address this issue. The system uses a pixel-level (P) pathway to perform robust block-matching in regions with sufficient texture, and a region-level (R) pathway to estimate motion from feature matching in low-texture regions. The fused optic-flow from the P and R pathways is then segmented by a pulse-coupled neural network (PCNN). The algorithm has produced very good results on synthetic and natural images. We show that its performance shows significant robustness to additive noise in the images.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004