Feature Extraction from Noisy Image Using PCNN
The entropy sequence of the output image, gotten from the original gray image by Pulse-Coupled Neural Network (PCNN), as feature vector of the gray image, can be used as a unique feature expression of gray image, which has been proved by our experiment. therefore, in this paper, it is used in the image classification, and the mean square error (MSE) between the feature vector of the input image and standard feature vector is used to judge the input image belong to which kind of image groups. At the same time, the results of our experiment show that this method is strongly flexible to resist noises and greatly robust to recognize image, if the tested images in our experiment are disturbed with Gaussian noise, impulse noise or both of this.
Feature Extraction pattern recognition entropy Pulse-Coupled Neural Network (PCNN)
Yide Ma Zhaobin Wang Chenghu Wu
School of Information Science and Engineering, Lanzhou University, Lanzhou City, Gansu Province, China
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
808-813
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）