Texture Image Segmentation Using Pulse Coupled Neural Networks

摘要：

Texture, a representation of the spatial relationship of gray levels in an image, is an important characteristic for the automated or semi-automated interpretation of digital images. Many previous analyses have shown how to discriminate texture images, which include gray level co-occurrence matrix (GLCM), Laws texture energy (LAWS) and Gabor multi-channel filtering (GABOR) etc. We have devised a new method based pulse coupled neural networks (PCNN) to perform texture image segmentation. We propose a segmentation scheme, using PCNN to extract texture features of image and classified by Fuzzy c-Means algorithm (FCM). For demonstration purpose, this paper compares the discrimination ability of two texture analysis methods: pulse coupled neural networks (PCNN) and Gabor multi-channel filtering (GABOR). Experimental results indicate that our method is superior to Gabor multi-channel filtering for a wide range of texture pairs

作者: LI Yi TONG Qinye FAN Yingle

作者单位: Zhejiang University, China Hangzhou Dianzi University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）