Computational Intelligence in Image Processing by Amitava Chatterjee, Patrick Siarry

By Amitava Chatterjee, Patrick Siarry

Computational intelligence dependent recommendations have firmly demonstrated themselves as potential, trade, mathematical instruments for greater than a decade. they've been generally hired in lots of structures and alertness domain names, between those sign processing, automated regulate, commercial and patron electronics, robotics, finance, production structures, electrical strength platforms, and gear electronics. picture processing can be a very powerful zone which has attracted the atten­tion of many researchers who're drawn to the improvement of recent computational intelligence-based strategies and their compatible functions, in either study prob­lems and in real-world difficulties.

Part I of the publication discusses numerous picture preprocessing algorithms; half II commonly covers picture compression algorithms; half III demonstrates how computational intelligence-based innovations will be successfully applied for photograph research reasons; and half IV exhibits how development popularity, type and clustering-based suggestions might be constructed for the aim of photograph inferencing. The publication bargains a unified view of the trendy computational intelligence tech­niques required to resolve real-world difficulties and it really is compatible as a reference for engineers, researchers and graduate students.

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The optimized procedure has been verified in a set of test results from real-world images by a comprehensive comparison with a number of contrastenhancement approaches available in the literature. References 1. : A solution to the deficiencies of image enhancement. Signal Process. 90, 44–56 (2010) 2. : Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans. Consumer Electron. 49(4), 1310–1319 (2003) 3. : Vision processing for realtime 3d data acquisition based on coded structured light.

Fig. 3 The Gaussian weighting kernel to remove boundary discontinuities corresponding to the sectors shown in Fig. 2 sectors. In addition, enhancements in each sector should be retained as much as possible. Here, these requirements are satisfied by weighting the sectors with a Gaussian kernel and then integrating with the original image. 11) where superscript b ∈ {u, v} denotes if the Gaussian is for the height (v) or width (u) for the image dimension, δ is the distance from the boundary along the associated dimension, and σ is the Gaussian standard deviation.

C. ) vol. 177/2009, pp. 97–118. Springer-Verlag, Berlin, Heidelberg (2009) 24. : Image enhancement based on equal area dualistic subimage histogram equalization method. IEEE Trans. Consumer Electron. 45(1), 68–75 (1999) 25. : Image contrast enhancement by constrained local histogram equalization. Comput. Vision Image Underst. 73(2), 281–290 (1999) 26. : Contrast limited adaptive histogram equalization, pp. 474–485. id=180895. 180940 Chapter 3 Hybrid BBO-DE Algorithms for Fuzzy Entropy-Based Thresholding Ilhem Boussaïd, Amitava Chatterjee, Patrick Siarry and Mohamed Ahmed-Nacer Abstract This chapter shows how a recently proposed stochastic optimization algorithm, called biogeography-based optimization (BBO), can be efficiently employed for development of three-level thresholding-based image segmentation.

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