Image Processing And Analysis: Variational, Pde, Wavelet, by Tony Chan, Jianhong Shen

By Tony Chan, Jianhong Shen

This publication is certainly beneficial in that it touches on many technical parts appropriate to fashionable picture processing. It does it with a remarkably deep knowing of the innovations concerned and an grand compactness.

Definitely no longer for newcomers, extra for those that have already been brought to those subject matters and wish to reinforce their wisdom. excellent for a post-doctoral viewers.

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Extra resources for Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods

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Geometry of Curves and Surfaces 35 Integration by parts further leads to Thus away from the two ends, the rate of change of the tangent, —i, characterizes the sensitivity of length growth. Under the arc length parameter s,—t' = —Kn exactly defines the curvature information. A general second order (Euclidean) measure for a smooth curve y is given by where /(/c) is some suitable function of curvature. x-axis) and changes continuously. For a closed smooth curve, E\ has to be an integer multiple of 2n, and is thus called the total circulation.

1. An ideal image: noiseless, complete, and in good contrast. that M and m are not separated far enough. It may appear that a simple linear transform like can renormalize the range and suffice to enhance the image. But imagine the following scenario. Let w be a normal image with natural contrast and w(x) e [0, 1] for any pixel x. 2. A low-contrast version of the ideal image. Chapter 1. 5, and indeed the contrast is quite low. Furthermore, due to the nonlinearity of the distortion, the simple linear transform u —> i> proposed above cannot work faithfully to approximate the original image w.

We then turn to stochastic models of images, as originated from Geman and Geman's celebrated work on Gibbs' image models [130]. It is also discussed how to learn image field characteristics by combining filtering techniques with the maximum entropy principle, as first proposed in the remarkable work of Zhu and Mumford [328] and Zhu, Wu, and Mumford [329]. This chapter concludes with two other geometry-based image models: the level-set representation and Mumford and Shah's free boundary image model.

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