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Low-complexity fusion of intensity, motion, texture, and edge for image sequence segmentation: a neural network approach

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2 Author(s)
Jinsang Kim ; Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA ; Chen, T.

We develop an image sequence segmentation scheme which uses intensity, motion, edge, and texture features. The proposed scheme is simple and inherently parallel in nature. Motion confidence values are employed for a feature weighting scheme in order to suppress unreliable feature components. These feature vectors are quantized by training self-organizing feature maps (SOFM). In order to generate more meaningful boundaries of the segmentation, we also develop an edge fusion algorithm in which an edge-linked map extracted from a real-time edge linking algorithm is incorporated for the segmentation. Experimental results show the validity of our approach

Published in:

Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop  (Volume:2 )

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