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


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

Illustrating crucial elements of adaptive picture processing from a computational intelligence standpoint, the second one variation of Adaptive picture Processing: A Computational Intelligence point of view offers an authoritative and distinct account of computational intelligence (CI) equipment and algorithms for adaptive photograph processing in regularization, area detection, and early vision.

With 3 new chapters and up-to-date details all through, the hot variation of this renowned reference comprises vast new fabric that specializes in functions of complex CI concepts in snapshot processing purposes. It introduces new innovations and frameworks that show how neural networks, help vector machines, fuzzy common sense, 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, corresponding to content-based photograph retrieval, this publication keeps to supply educators, scholars, researchers, engineers, and technical managers in visible details processing with the up to date knowing required to handle modern demanding situations in picture content material processing and research.

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

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3 repeat { For i = 1, . . 42) 42 Adaptive Image Processing: A Computational Intelligence Perspective   0,   where K (u) = u,    S, u<0 0≤u≤S u≥S } t =t+1 } until fˆi (t) = fˆi (t − 1)∀i = 1, . . , L) Each neuron is visited sequentially and has its input calculated. Using the input value, the state change needed to minimize the neuron’s energy contribution to the network is calculated. 42)] is equivalent to checking that at least a unit step can be taken which will reduce the energy of the network.

If g is smaller than f then the problem in general has an infinite number of solutions. If g is of equal size as f, then there is no additional information about the imaging system or f contained in g to help us to disambiguate the contributions of Hf and n on g. Depending on the form of H, there may be an infinite number of solutions, or a single solution that is extremely sensitive to the exact noise realization n present in the system. For this reason, extra constraints will be required. Consider a matrix D ∈ L×L designed to act as a high-pass operator.

Motion blur can also result when the camera moves during the exposure. Noise is generally a distortion due to the imaging system rather than the scene recorded. Noise results in random variations to pixel values in the image. This could be caused by the imaging system itself, or the recording or transmission medium. Sometimes the definitions are not clear as in the case where an image is distorted by atmospheric turbulence, such as heat haze. In this case, the image appears blurry because the atmospheric distortion has caused sections of the object to be imaged to move about randomly.

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