A New Automatic Parameter Setting Method of a simplified PCNN for Image Segmentation

Title
A New Automatic Parameter Setting Method of a simplified PCNN for Image Segmentation
Authors
박성기Yide Ma라제쉬
Keywords
PCNN; neural network; image segmentation; Automatic parameter setting; dynamic property; general formulae; optimal histogram threshold; simplified pulse coupled neural network; standard deviation; static property
Issue Date
2011-06
Publisher
IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council
Citation
VOL 22, NO 6, 880-892
Abstract
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.
URI
http://pubs.kist.re.kr/handle/201004/42284
ISSN
1045-9227
Appears in Collections:
KIST Publication > Article
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