A New Algorithm of Image Segmentation Based on Pulse-Coupled Neural Networks and the Entropy of Images
As all known, the performance of the image segmentation depends not only directly on the adjustment of PCNN parameters and the statistical properties of image but also on the cyclic iteration times, N, of PCNN. If the parameters have been properly set, it turns out to be essential to select a suitable criterion to determine N. While N is usually determined by means of visual judgement which decreases the efficient of PCNN image segmentation. This article raises a new method to implement the image segmentation automatically based on the PCNN model and the entropy of image. It is the criterion of maximal entropy of segmented binary image of PCNN output. According to this criterion, the iteration times, N, is determined automatically. Index Terms-PCNN (pulse-coupled neural network), image segmentation, entropy, statistics
Yi-De Ma Ro-Lan Dai Li Lian Shi Fei
The state key laboratory of Arid Agroecology, Lanzhou University, Lanzhou of Gansu China,730000
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
311-316
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）