By Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong
Illustrating crucial facets of adaptive photograph processing from a computational intelligence standpoint, the second one variation of Adaptive picture Processing: A Computational Intelligence point of view offers an authoritative and distinctive account of computational intelligence (CI) tools and algorithms for adaptive snapshot processing in regularization, aspect detection, and early vision.
With 3 new chapters and up-to-date info all through, the hot variation of this renowned reference contains big new fabric that specializes in purposes of complicated CI ideas in picture processing purposes. It introduces new thoughts and frameworks that display how neural networks, help vector machines, fuzzy good judgment, and evolutionary algorithms can be utilized to handle new demanding situations in photograph processing, together with low-level photograph processing, visible content material research, function extraction, and development recognition.
Emphasizing advancements in state of the art CI options, similar to content-based photograph retrieval, this publication keeps to supply educators, scholars, researchers, engineers, and technical managers in visible info processing with the updated realizing 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)
2, for each pixel, the point takes a unit step in a direction that reduces the network energy along that dimension. If the weight matrix is negative definite (−W is positive definite), however, regardless of how these algorithms work, the end results must be 40 Adaptive Image Processing: A Computational Intelligence Perspective similar (if each algorithm ends at a minimum). The reason for this is that when the weight matrix is negative definite, there is only the global minimum. That is, the function has only one minimum.
In this algorithm, we have derived a parametric representation that expresses the regularization parameter value as a function of the local image variance. Generating a population of these regularization strategies that are vectors of the above hyperparameters, we apply the processes of mutation, competition, and selection to the members of the population to obtain the optimal regularization strategy. This approach is then further extended to solve the problem of blind deconvolution by including the point spread function coefficients in the set of hyperparameters associated with each individual in the population.
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.