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Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong's Adaptive Image Processing: A Computational Intelligence PDF

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

ISBN-10: 1420084356

ISBN-13: 9781420084351

Illustrating crucial features of adaptive snapshot processing from a computational intelligence point of view, the second one variation of Adaptive photo Processing: A Computational Intelligence viewpoint offers an authoritative and specific account of computational intelligence (CI) tools and algorithms for adaptive photo processing in regularization, part detection, and early vision.

With 3 new chapters and up-to-date info all through, the recent version of this well known reference contains colossal new fabric that specializes in functions of complex CI recommendations in picture processing functions. It introduces new innovations and frameworks that exhibit how neural networks, aid vector machines, fuzzy good judgment, and evolutionary algorithms can be utilized to handle new demanding situations in snapshot processing, together with low-level photograph processing, visible content material research, function extraction, and trend recognition.

Emphasizing advancements in state of the art CI recommendations, similar to content-based photo retrieval, this booklet maintains to supply educators, scholars, researchers, engineers, and technical managers in visible details processing with the updated knowing required to handle modern demanding situations in photograph content material processing and research.

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

Sample text

Since the formula for the energy reduction resulting from a change in neuron state fˆi is a simple quadratic, it is possible to solve for the fˆi which produces the maximum energy reduction. 1 and hence the same final state of each neuron after it is updated. 1 For each neuron i in the network during each iteration, there exists a state change ∗ fˆi such that the energy contribution of neuron i is minimized. 40) fˆi gives us δ E = −wii δ fˆi The value of fˆi . 41) QED. 1, an improved algorithm is presented below.

Note that p is a set of positivity constraints, while h comprises the constraint on the norm of the high-frequency components in the image. Let us look now at how we can solve this equation. The following definition and theory will be useful, and we will be making use of these in subsequent chapters. 1 Let f satisfy h(f ) = 0, p(f ) ≤ 0 and let J {f } be the index set of active inequality constraints, that is, J {f } = { j; p j (f ) = 0}. Then f is a regular point if ∇h(f ), ∇ p j (f ) are linearly independent ∀ j ∈ J {f } [94].

32) where wi j is the interconnection strength between pixels i and j, and b i is the bias input to neuron (pixel) i. 3) and di j is the (i, j)th element of matrix D. Now let’s look at some neural networks in the literature to solve this problem. 6 Neural Network Restoration Algorithms in the Literature In the network described by Zhou et al. [46] for an image with S + 1 gray levels, each pixel is represented by S + 1 neurons. Each neuron can have a value of 0 or 1. 33) k=0 where vi,k is the state of the kth neuron of the ith pixel.

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Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition) by Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong


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