By Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong
Illustrating crucial features of adaptive photograph processing from a computational intelligence standpoint, the second one variation of Adaptive snapshot Processing: A Computational Intelligence point of view offers an authoritative and exact 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 details all through, the recent version of this well known reference comprises mammoth new fabric that makes a speciality of purposes of complex CI options in snapshot processing purposes. It introduces new options and frameworks that reveal how neural networks, help vector machines, fuzzy good judgment, and evolutionary algorithms can be utilized to deal with new demanding situations in picture processing, together with low-level photo processing, visible content material research, characteristic extraction, and trend recognition.
Emphasizing advancements in state of the art CI options, equivalent to content-based photo retrieval, this ebook maintains to supply educators, scholars, researchers, engineers, and technical managers in visible details processing with the updated realizing required to deal with modern demanding situations in snapshot content material processing and research.
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Extra resources for Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition)
Consider a matrix D ∈ L×L designed to act as a high-pass operator. Df is therefore the high-pass component of the estimated image. To reduce the effects of noise, we can Fundamentals of CI-Inspired Adaptive Image Restoration 33 constrain Df 2 to be equal to a preset constant. If the constant is chosen too low, the image will be blurred, and if the constant is chosen too high, the image will contain unacceptable levels of noise. This problem can then be stated as f = arg minf g − Hf 2 subject to h(f ) = 0 and p(f ) ≤ 0 where h(f) ∈ , h(f) = Df 2 − σ 2 and p(f) ∈ L , p(f) = −f, p j (f) = − f j , 1 ≤ j ≤ L.
11) Since p(g) has f integrated out, it is not a function of f. 10) is called the posterior probability density of f. This is the conditional probability of f given a known observation g. The posterior density of f takes into account our prior knowledge about f. The f that maximizes the posterior density is the f that is most probable given the observation and the prior density. 10). d. 3). We can see that constrained least square image restoration computes the maximum a posteriori estimate of the original image given the degraded image, knowledge of the blur function, and the assumptions we have made above regarding the form of the additive noise in the system and the prior distribution of the original image.
Since the network is restricted to changing state only when an energy reduction is possible, the network cannot iterate forever. 1 for the same problem. 3 must approach the same local minimum is the fact that they operate on the pixel in an identical manner. 1, each of the S + 1 neurons associated with pixel i is 44 Adaptive Image Processing: A Computational Intelligence Perspective adjusted to reduce its contribution to the energy function. 1 equals the final grayscale value of that neuron. 1 the current pixel can change to any allowable value.
Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition) by Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong