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 picture filtering, photograph segmentation, snapshot fusion, snapshot coding, snapshot retrieval, and biometric acceptance, and the function of pulse-coupled neural networks in those fields. This publication is meant for researchers and graduate scholars in synthetic intelligence, trend attractiveness, digital engineering, and desktop technology. Prof. Yide Ma conducts learn on clever details processing, biomedical snapshot processing, and embedded procedure improvement on the institution of knowledge technological know-how and Engineering, Lanzhou college, China.

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Accessed 11 October 2007 [7] Ostu N (1979) A threshold selection method from gray-level histograms. IEEE transactions on System, Man, and Cybernetics 9(1): 62 – 66 [8] Zhang YJ (1996) A survey on evaluation methods for image segmentation. Pattern recognition 29(8): 1335 – 1346 42 Chapter 3 Image Segmentation [9] Ranganath HS, Kuntimad G (1999) Object detection using pulse coupled neural networks. IEEE Transactions on Neural Networks 10(3): 615 – 620 [10] Li CH, Lee CK (1993) Minimum cross-entropy thresholding.

2. 3(a) shows that the chain code for the enlarged object contour from top to bottom is {2, 3, 3, 3, 0, 0, 3, 0}, associated with four directions, and a total of 8 × 2 bits = 16 bits is needed, while the code associated with eight directions is {5, 6, 7, 0, 7}, and a total of 5 × 3 bits = 15 bits is needed, as shown in Figs. 3(c), respectively. It shows that eight directions coding may require fewer codes than four directions coding for a certain length contour. Fig. 3. , αN }. N f (x, y) = αi ϕi (x, y).

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. 3 Image Segmentation Using Simplified PCNN and GA Genetic Algorithm (GA) is a random optimization algorithm and was proposed drawing lessons from the natural selection and natural genetic mechanisms in the organic sphere. As a search algorithm which has the features of robustness, self-adaptive and parallelism, the GA has been widely used in the fields of image processing.

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