A Computational Framework for Segmentation and Grouping by G. Medioni, Mi-Suen Lee, Chi-Keung Tang PDF

By G. Medioni, Mi-Suen Lee, Chi-Keung Tang

ISBN-10: 0444503536

ISBN-13: 9780444503534

This booklet represents a precis of the study we've been undertaking because the early Nineteen Nineties, and describes a conceptual framework which addresses a few present shortcomings, and proposes a unified technique for a wide classification of difficulties. whereas the framework is outlined, our study maintains, and a few of the weather provided right here will doubtless evolve within the coming years.It is geared up in 8 chapters. within the advent bankruptcy, we current the definition of the issues, and provides an outline of the proposed technique and its implementation. particularly, we illustrate the restrictions of the 2.5D comic strip, and encourage using a illustration by way of layers instead.
In bankruptcy 2, we assessment many of the correct learn within the literature. The dialogue makes a speciality of common computational ways for early imaginative and prescient, and person equipment are just brought up as references. bankruptcy three is the basic bankruptcy, because it provides the weather of our salient characteristic inference engine, and their interplay. It brought tensors so as to symbolize details, tensor fields so that it will encode either constraints and effects, and tensor vote casting because the conversation scheme. bankruptcy four describes the function extraction steps, given the computations played via the engine defined previous. In bankruptcy five, we practice the standard framework to the inference of areas, curves, and junctions in 2-D. The enter could take the shape of 2-D issues, without or with orientation. We illustrate the strategy on a couple of examples, either uncomplicated and complicated. In bankruptcy 6, we observe the framework to the inference of surfaces, curves and junctions in 3-D. the following, the enter includes a collection of three-D issues, without or with as linked general or tangent course. We express a few illustrative examples, and in addition aspect to a couple functions of the procedure. In bankruptcy 7, we use our framework to take on three early imaginative and prescient difficulties, form from shading, stereo matching, and optical movement computation. In bankruptcy eight, we finish this ebook with a number of feedback, and talk about destiny study directions.
We comprise three appendices, one on Tensor Calculus, one facing proofs and info of the characteristic Extraction technique, and one facing the significant other software program applications.

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The second term evaluates the model. It expresses prior beliefs about what we think is a good model, which corresponds to the regularization term ||Gfxj|| in the regularization theory. The third term describes how important specific data are evaluated, within the total set of data. In computer vision, this term can be used to distinguish old data from new data, when time dependent data are available. Another possibility is to discard data once discontinuities are detected. Again, variational principles are widely used in solving the transformed functional optimization problem.

Intuitively, the shape of the tensor defines the type of information captured (point, curve, or surface element), and the associated size represents the saliency. T For instance, in 2-D, a very salient curve element at (x •,};•) is represented by a thin ellipse, whose major axis represents the estimated tangent direction, and whose length reflects the saliency of the estimation. Such a representation is not very different from the vector representation. However, a point P at {x-,y^ with no associated orientation, is now represented by a disk, whose radius encodes the saliency of the estimation.

Morel and Solimini have argued rigorously that ''all the other ones (models) are variants, or algorithms tending to minimize these variants" of the Mumford-Shah variational model. Optical Flow Estimation Given a sequence of images, the goal of optical flow estimation is to recover the apparent movement of each pixel in the images. A good review of methods for calculating optical flow can be found in [6,7, 62]. Due to the aperture problem [36], the movement of constant brightness contour is only determined up to the direction normal to the contour.

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A Computational Framework for Segmentation and Grouping by G. Medioni, Mi-Suen Lee, Chi-Keung Tang


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