期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 22, 期 6, 页码 880-892出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2011.2128880
关键词
Automatic parameter setting; dynamic property; general formulae; image segmentation; optimal histogram threshold; simplified pulse coupled neural network; standard deviation; static property; sub-intensity range
类别
资金
- National Science Foundation of China [60872109]
- Korea Institute of Science and Technology
- National Research Council of Science & Technology (NST), Republic of Korea [2E2235] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
An automatic parameter setting method of a simplified pulse coupled neural network (SPCNN) is proposed here. Our method successfully determines all the adjustable parameters in SPCNN and does not need any training and trials as required by previous methods. In order to achieve this goal, we try to derive the general formulae of dynamic threshold and internal activity of the SPCNN according to the dynamic properties of neurons, and then deduce the sub-intensity range expression of each segment based on the general formulae. Besides, we extract information from an input image, such as the standard deviation and the optimal histogram threshold of the image, and attempt to build a direct relation between the dynamic properties of neurons and the static properties of each input image. Finally, the experimental segmentation results of the gray natural images from the Berkeley Segmentation Dataset, rather than synthetic images, prove the validity and efficiency of our proposed automatic parameter setting method of SPCNN.
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