Download Applications of Pulse-Coupled Neural Networks by Yide Ma, Kun Zhan, Zhaobin Wang PDF

By Yide Ma, Kun Zhan, Zhaobin Wang

Functions of Pulse-Coupled Neural Networks explores the fields of photograph processing, together with snapshot filtering, photo segmentation, photo fusion, photo coding, photo retrieval, and biometric reputation, and the position of pulse-coupled neural networks in those fields. This e-book is meant for researchers and graduate scholars in synthetic intelligence, development attractiveness, digital engineering, and laptop technology. Prof. Yide Ma conducts study on clever info processing, biomedical photograph processing, and embedded approach improvement on the institution of data technological know-how and Engineering, Lanzhou college, China.

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Extra resources for Applications of Pulse-Coupled Neural Networks

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3, in which the iteration number N is set to 35, the cross-entropy of each pulse image output from the PCNN and the Fig. 3. 3 Image Segmentation Using Simplified PCNN and GA 35 original image is plotted in (a) and the entropy of each pulse image is shown in (b). From (a), the cross-entropy begins with a larger value, decreases quickly and reaches the minimum when iteration time is 14, then increases slowly. The situation of (b) is almost opposite to (a). The entropy increases quickly from 0 to the maximum, nearly 1, when the iteration time is 10 and then decreases slowly.

The two main features of the GA, the population search strategy and the information interchange of individuals in population, can be omployed to obtain and accumulate the knowledge about the search space and control the process adaptively to obtain the optimal solution or approximate optimal solution during the search process. the GA operates in a more stochastic manner than the traditional search algorithm. This controllable randomness often allows the GA to find optimal solutions much quicker than their deterministic counterparts [11, 12].

13) 2 [I(i, j) − I (i, j)] i=0 j=0 where I(i, j) and I (i, j) are the intensity of the original image and reconstructed image, respectively corresponding to coordinates (i, j); M and N mean the number of rows and columns of image.

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