By Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong

Illustrating crucial points of adaptive snapshot processing from a computational intelligence perspective, the second one variation of Adaptive photo Processing: A Computational Intelligence point of view presents an authoritative and targeted account of computational intelligence (CI) tools and algorithms for adaptive picture processing in regularization, side detection, and early vision.

With 3 new chapters and up-to-date info all through, the hot version of this renowned reference comprises mammoth new fabric that specializes in functions of complicated CI ideas in snapshot processing purposes. It introduces new ideas and frameworks that exhibit how neural networks, help vector machines, fuzzy common sense, and evolutionary algorithms can be utilized to deal with new demanding situations in snapshot processing, together with low-level picture processing, visible content material research, characteristic extraction, and trend recognition.

Emphasizing advancements in cutting-edge CI concepts, reminiscent of content-based picture retrieval, this ebook maintains to supply educators, scholars, researchers, engineers, and technical managers in visible details processing with the updated knowing required to deal with modern demanding situations in picture content material processing and research.

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Additional resources for Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition)

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As a result, the model pdf of the ETC measure serves as a kind of signature for correctly regularized images, and we should minimize the difference between the corresponding pdf of the image being restored and the model pdf using some kind of distance measure. The requirement to approximate this pdf using a histogram, which involves the counting of discrete quantities, and the resulting nondifferentiability of the distance measure with respect to the various regularization parameters, necessitates the use of evolutionary computational algorithms for optimization.

4 Application of Evolutionary Programming to Adaptive Regularization and Blind Deconvolution Apart from the neural network-based techniques, we have developed an alternative solution to the problem of adaptive regularization using evolutionary programming, which is a member of the class of evolutionary computational algorithms [39,40]. Returning again to the ETC measure, we have observed that the distribution of the values of this quantity assumes a typical form for a large class of images. In other words, the shape of the probability density function (pdf) of this measure is similar across a broad class of images and can be modeled using piecewise continuous functions.

3) should be, hence minimizing the second term will involve reducing the noise in the image at the expense of restoring sharpness. Choosing λ becomes an important consideration when restoring an image. Too great a value of λ will oversmooth the restored image, whereas too small a value of λ will not properly suppress noise. At their essence, neural networks minimize cost functions such as that above. It is not unexpected that there exist neural network models to restore degraded imagery, and such models will be explored in the next section.

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