Advances in Reasoning-Based Image Processing Intelligent - download pdf or read online

By Roumen Kountchev, Kazumi Nakamatsu

ISBN-10: 3642246923

ISBN-13: 9783642246920

The publication places particular pressure at the modern suggestions for reasoning-based snapshot processing and research: studying dependent photo illustration and complicated video coding; clever photograph processing and research in clinical imaginative and prescient structures; similarity studying versions for photo reconstruction; visible belief for cellular robotic movement regulate, simulation of human mind task within the research of video sequences; shape-based invariant positive factors extraction; crucial of paraconsistent neural networks, creativity and clever illustration in computational platforms.

The ebook contains 14 chapters. every one bankruptcy is a small monograph, representing resent investigations of authors within the region. the themes of the chapters disguise huge clinical and alertness parts and supplement each-other rather well. The chapters’ content material is predicated on basic theoretical displays, by way of experimental effects and comparability with comparable thoughts. the dimensions of the chapters is well-ballanced which allows a radical presentation of the investigated difficulties. The authors are from universities and R&D associations around the globe; many of the chapters are ready by means of foreign groups. The publication can be of use for collage and PhD scholars, researchers and software program builders operating within the zone of electronic photo and video processing and analysis.

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Kountchev, V. Todorov, and R. Kountcheva E′p−p1(i, j)= Ep−p1(i, j) + Mp{εp−1(i, j)+Mp−1[εp−2 (i, j) + ... + M1[ε0 (i, j)]]}+εp (i, j). 12) Here ε p (i, j) is the quantization noise of the pth IPD component. The restored image could be represented using the original image and Eqs. 13) where the total quantization error, accumulated on the last level p=n, is: n ε Σ (i, j) =  ε p (i, j) + M 1 [ε 0 (i, j)] + M 2 {ε1 (i, j) + M1 [ ε0 (i, j)]} + ... 14) + 2 M n −1{ε n − 2 (i, j) + M n − 2 [ ε n −3 (i, j) + ....

Kountchev et al. ): Advances in Reasoning-Based Image Processing, ISRL 29, pp. 35–89. com © Springer-Verlag Berlin Heidelberg 2012 36 R. Kountchev, V. Todorov, and R. Kountcheva the kind KLT, РСА, SVD, DFT, DCT, etc. (Ahmed and Rao, Pratt), or on the use of a pyramidal representation (Rabbani and Jones, Topiwala). ), based on various mathematical models for image representation in the corresponding spectral space. According to Kunt et al. again, pyramidal decompositions are assigned to the second generation of methods for image coding, which better go together with human visual system.

Todorov, and R. Kountcheva 2. , Bovik), obtained through sub-band decomposition and discrete wavelet transform (DWT) in particular. These pyramids have practically independent neighbor levels which is a prerequisite for their efficient coding. The first level in a pyramid of this kind contains four images with half the size of the original and each fourth corresponds to definite part of the 2D Fourier spectrum. The first one, LL contains the low-frequency spectrum frequencies, which have on the basic part of the visual information; the second, LH contains the lowest frequencies in the vertical direction and the highest – in the horizontal; the third, HL – the highest frequencies in the vertical direction and the lowest in the horizontal, and the fourth, HH – the highest frequencies in both directions, which correspond to finest structure.

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Advances in Reasoning-Based Image Processing Intelligent Systems: Conventional and Intelligent Paradigms by Roumen Kountchev, Kazumi Nakamatsu


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