By Yide Ma, Kun Zhan, Zhaobin Wang
Functions of Pulse-Coupled Neural Networks explores the fields of photograph processing, together with photograph filtering, photo segmentation, picture fusion, picture coding, photo retrieval, and biometric attractiveness, and the position of pulse-coupled neural networks in those fields. This e-book is meant for researchers and graduate scholars in man made intelligence, trend attractiveness, digital engineering, and laptop technological know-how. Prof. Yide Ma conducts examine on clever details processing, biomedical photo processing, and embedded process improvement on the institution of data technological know-how and Engineering, Lanzhou college, China.
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That is to say, N = 6, and the initial basis will be 1, x, y, x2 , xy, and y 2 . The codes of N coeﬃcients of orthonormal polynomial are the ones of image regions; these coeﬃcients are calculated according to the intensity of pixels corresponding to regions. In general, the larger the N is, the better the reconstructed image will be. In addition, this characteristic makes progressive image coding (or reconstruction) possible, and an image can be browsed in the rough for a smaller N or get more details for a larger N .
01. Size of Population The evaluation number of ﬁtness and the calculation complexity will increase if the size of population is big, but the phenomenon of immaturity convergence will arise if the size of population is small. So the value of size should be carefully chosen. Here, the value is set to 30; the max-generation is set to 50. 38 Chapter 3 Image Segmentation Stopping Criteria All the evolutionary systems need a mechanism to stop training when a good solution is reached. The balance needs to be met so that the system will have enough time to generate useful data which should not be so many that the data become too specialized.
2. 2. Chromosome composed of three genes. 000 1 maximum 100 400 100 Coding length(bit) 17 19 17 The overall coding length of chromosome is 53 bits. 3 Image Segmentation Using Simpliﬁed PCNN and GA 37 Fitness Function It is important to derive a suitable ﬁtness function since the performance of the system mainly relies on the quality of the function. It is the ﬁtness function that allows the GA to ﬁnd the optimal solutions for problems where the optimum is unknown. 3) is chosen as the object function in this subsection.