By Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong
Illustrating crucial elements of adaptive photograph processing from a computational intelligence perspective, the second one variation of Adaptive photograph Processing: A Computational Intelligence point of view offers an authoritative and special account of computational intelligence (CI) tools and algorithms for adaptive picture processing in regularization, facet detection, and early vision.
With 3 new chapters and up to date details all through, the hot version of this well known reference contains giant new fabric that specializes in functions of complicated CI concepts in picture processing purposes. It introduces new recommendations and frameworks that reveal how neural networks, aid vector machines, fuzzy common sense, and evolutionary algorithms can be utilized to deal with new demanding situations in photo processing, together with low-level picture processing, visible content material research, characteristic extraction, and trend recognition.
Emphasizing advancements in cutting-edge CI strategies, reminiscent of content-based snapshot retrieval, this booklet maintains to supply educators, scholars, researchers, engineers, and technical managers in visible info processing with the up to date knowing required to handle modern demanding situations in photo content material processing and research.
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Additional resources for Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition)
Chapter 10 examines content representation in compressed domain image classification using evolutionary algorithm. Finally, Chapter 11 explores the fuzzy user perception and small sample problem in content-based image retrieval, and develops computational intelligence techniques to address these challenges. 1 Neural Networks as a CI Architecture In the next few chapters, we examine CI (computational intelligence) concepts by looking at a specific problem: image restoration. Image restoration is the problem of taking a blurred, noisy image and producing the best estimate of the original image possible.
The generalized Hopfield network can be applied to this case . 3), W, b and c are functions of H, D, λ, and n, and other problem related constraints. In terms of a neural network energy function, the (i, j)th element of W corresponds to the interconnection 36 Adaptive Image Processing: A Computational Intelligence Perspective strength between neurons (pixels) i and j in the network. Similarly, vector b corresponds to the bias input to each neuron. 3), the bias inputs and interconnection strengths can be found such that as the neural network minimizes its energy function, the image will be restored.
More importantly, instead of providing quantitative values for the threshold as in the usual case, the users are asked to provide qualitative opinions on what they regard as edges by manually tracing their desired edges on an image. The gray-level configurations around the trace are then used as training examples for the model-based neural network to acquire an internal model of the edges, which is another example of the design of an adaptive image-processing system through the training process.
Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition) by Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong