Download Computational Intelligence in Image Processing by Amitava Chatterjee, Patrick Siarry PDF

By Amitava Chatterjee, Patrick Siarry

Computational intelligence dependent options have firmly verified themselves as potential, trade, mathematical instruments for greater than a decade. they've been generally hired in lots of platforms and alertness domain names, between those sign processing, computerized regulate, business and client electronics, robotics, finance, production structures, electrical energy platforms, and tool electronics. picture processing is usually a really powerful quarter which has attracted the atten­tion of many researchers who're attracted to the improvement of recent computational intelligence-based ideas and their appropriate purposes, in either learn prob­lems and in real-world difficulties.

Part I of the e-book discusses numerous photo preprocessing algorithms; half II commonly covers picture compression algorithms; half III demonstrates how computational intelligence-based thoughts could be successfully applied for photo research reasons; and half IV indicates how development popularity, class and clustering-based concepts will be constructed for the aim of photo inferencing. The ebook deals a unified view of the fashionable computational intelligence tech­niques required to resolve real-world difficulties and it's appropriate as a reference for engineers, researchers and graduate students.

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Extra resources for Computational Intelligence in Image Processing

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2, giving equalized sectors as sd s s sd = {S pq : S pq (ci ) = E pq (c j )}. 10) The result is depicted in Fig. 2, where it can be seen that for each individual sector, the contrast is increased. However, it is also observed that along the sector boundaries, intensity differences or discontinuities are noticeable and need to be mitigated. 2 Mitigation of Sector Discontinuities In order to reduce the difference of intensities along sector boundaries, an arithmetic mean aggregation approach is adopted in order to combine the locally equalized 28 N.

Thresholding techniques can be classified as bilevel and multilevel thresholding, depending on number of image segments into which an original image is decomposed. In bilevel thresholding, each image pixel is assigned to one of two brightness regions, object and background, according to whether its intensity (gray level or colour) is greater than a specified threshold T or not. In multilevel thresholding, pixels can be classified into many classes, not just foreground and background. Therefore, more than one threshold should be determined to segment the image into certain brightness regions which may correspond to one background and several objects.

These include specific considerations in minimizing the mean brightness error between the input and output images [2]. In [22], the maximum entropy or information content criterion was invoked in contrast enhancement. A computational intelligence optimization-based method is presented in this chapter as an alternative approach to the contrast enhancement problem for color images. The image is first randomly divided into sectors, and their contrast is increased by individual histogram equalization.

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