Computational Intelligence in Image Processing

Computational Intelligence in Image Processing

Language: English

Pages: 304

ISBN: 364243164X

Format: PDF / Kindle (mobi) / ePub

This book offers comprehensive coverage of computational intelligence techniques applied to image processing applications. It provides a unified view of the modern computational intelligence tech-niques required to solve real-world problems.

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Histogram equalization. IEEE Trans. Image Process. 9(5), 889–896 (2000) 22. Wang, C., Ye, Z.: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Trans. Consumer Electron. 51(4), 1326–1334 (2005) 23. Wang, D., Kwok, N.M., Liu, D.K., Ha, Q.P.: Ranked Pareto particle-swarm optimization for mobile robot motion planning. Design and Control of Intelligent Robotic Systems. In: Liu, D.K., Wang, L.F., Tan, K.C. (eds.) vol. 177/2009, pp. 97–118.

256 gray levels for an 8-bit image [30, 31]. Let Dk = {(x, y)|I (x, y) = k, (x, y) ∈ D}, k ∈ G. The histogram of an image, defined as H = {h 0 , h 1 , . . . , h L−1 }, presents the frequency of occurrence of each gray level in the image and is obtained directly from the observation of the considered image. In view of this consideration, the kth gray level in the image is defined as follows: nk , k = 0, 1, . . . , L − 1 (3.2) hk = N∗M where n k denotes the total number of pixels in Dk , and N ∗ M.

Migration operator according to Algorithm 3.2 11: end for 12: for i = 1 to NP do 13: Mutate the non-elite members of the population with the mutation operator according to Algorithm 3.3 14: end for 15: for i = 1 to NP do 16: Evaluate the new habitats in the population 17: Replace the habitats with their new versions 18: Apply elitism to preserve n elit best habitats 19: end for 20: end for 3.5 Description of the Proposed DBBO-Fuzzy Algorithm Motivated by the exploration capabilities of the.

The terminals and nonterminals sets must satisfy two criteria: closure and sufficiency. The first states that each function has to accept as input any value or kind of data that can be generated by any combination of terminals of outputs of functions. The latter states that the superset of terminals and nonterminals must have all the elements needed for a satisfactory solution to the problem. In the same way as all evolutionary computation methods, GP also has a number of control parameters that.

Blocks that traverse the largest depth are better candidate solutions. Among these solutions, the one with the smallest Euclidean distance with the template is declared as the winner. The work differs with respect to classical hierarchical template matching by two counts. First, the conditions here are induced with fuzzy measurements of the features. Fuzzy encoding eliminates small changes in imaging features due to variations in lighting conditions and head movement. Second, information gain is.

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