Image Segmentation by Weight Adaptation and Oscillatory Correlation
We propose a novel approach/or image segmentation on the basis of a neural oscillator network. Unlike previous approaches, weight adaptation is introduced during segmentation for noise removal and feature preservation. Moreover, a logarithmic grouping rule is proposed to facilitate grouping of oscillators representing pixels with coherent properties. We show that our weight adaptation scheme is insensitive to termination times, and the resulting dynamic weights in a wide range of iterations lead to the same segmentation results. A computer algorithm derived from oscillatory dynamics is applied to synthetic and real images, and simulation results show that the algorithm yields favorable segmentation results.
Image segmentation Weight adaptation Oscillatory correlation LEGION Synchronization Desynchronization
Ke CHEN DeLiang WANG Xiuwen LIU
School of Computer Science, The University of Birmingham, Birmingham B15 2TT, United Kingdom Department of Computer and Information Science, Ohio State University, Columbus, OH 43210, USA Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
305-310
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）