By Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong
Illustrating crucial facets of adaptive photograph processing from a computational intelligence point of view, the second one variation of Adaptive picture Processing: A Computational Intelligence point of view presents an authoritative and certain account of computational intelligence (CI) tools and algorithms for adaptive picture processing in regularization, aspect detection, and early vision.
With 3 new chapters and up to date info all through, the recent variation of this renowned reference comprises massive new fabric that specializes in purposes of complex CI innovations in picture processing purposes. It introduces new strategies and frameworks that reveal how neural networks, aid vector machines, fuzzy good judgment, and evolutionary algorithms can be utilized to handle new demanding situations in photo processing, together with low-level picture processing, visible content material research, function extraction, and development recognition.
Emphasizing advancements in cutting-edge CI innovations, reminiscent of content-based photograph retrieval, this booklet maintains to supply educators, scholars, researchers, engineers, and technical managers in visible info processing with the up to date figuring out required to deal with modern demanding situations in photo content material processing and research.
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Additional info for Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition)
1 was enhanced to remove the step where the energy reduction is checked following the calculation of fˆi . Paik and Katsaggelos presented an algorithm which made use of a more complicated neuron. In their model, each pixel was represented by a single neuron that takes discrete values between 0 and S, and is capable of updating its value by ±1, or keeping the same value during a single step. A new method for calculating fˆi was also presented: ´ i (ui ) fˆi = G where where θi = − 12 wii > 0.
3) in the case of a sequential algorithm . 3). 2 would result in a decrease of the energy function at each iteration. 3, however, changes the current neuron’s state if and only if an energy reduction will occur and | fˆi | = 1. 3 can only reduce the energy function and never increase it. 3 brings the network closer to a local minimum of the function. 3 ever reach a local minimum and terminate? 44) where u is a vector whose ith element contains the current input to neuron i. Note that during any iteration, u will always point in a direction that reduces the energy function.
More importantly, no retraining and alteration of architecture is required for applying the same network to noisy images, unlike conventional edge detectors that usually require threshold readjustment. 3 Application of Fuzzy Set Theory to Adaptive Regularization For the adaptive regularization problem in image restoration, apart from the requirement of adopting different regularization parameters for smooth regions and regions with high gray-level variances, it is also desirable to further separate the latter regions into edge and textured regions.