Download e-book for kindle: A Computational Framework for Segmentation and Grouping by G. Medioni, Mi-Suen Lee, Chi-Keung Tang

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

ISBN-10: 0444503536

ISBN-13: 9780444503534

This ebook represents a precis of the study we now have been accomplishing because the early Nineteen Nineties, and describes a conceptual framework which addresses a few present shortcomings, and proposes a unified method for a wide classification of difficulties. whereas the framework is outlined, our learn maintains, and a few of the weather awarded the following will doubtless evolve within the coming years.It is equipped in 8 chapters. within the advent bankruptcy, we current the definition of the issues, and provides an outline of the proposed process and its implementation. specifically, we illustrate the constraints of the 2.5D comic strip, and inspire using a illustration by way of layers instead.
In bankruptcy 2, we evaluation a number of the correct study within the literature. The dialogue specializes in normal computational techniques for early imaginative and prescient, and person equipment are just mentioned as references. bankruptcy three is the elemental bankruptcy, because it offers the weather of our salient function inference engine, and their interplay. It brought tensors so that it will signify details, tensor fields for you to encode either constraints and effects, and tensor balloting because the conversation scheme. bankruptcy four describes the function extraction steps, given the computations played by means of the engine defined previous. In bankruptcy five, we observe the standard framework to the inference of areas, curves, and junctions in 2-D. The enter might take the shape of 2-D issues, without or with orientation. We illustrate the process on a few examples, either easy and complicated. In bankruptcy 6, we practice the framework to the inference of surfaces, curves and junctions in three-D. the following, the enter contains a suite of 3-D issues, without or with as linked general or tangent course. We convey a few illustrative examples, and in addition element to a few purposes of the technique. In bankruptcy 7, we use our framework to take on three early imaginative and prescient difficulties, form from shading, stereo matching, and optical stream computation. In bankruptcy eight, we finish this ebook with a number of feedback, and speak about destiny learn directions.
We comprise three appendices, one on Tensor Calculus, one facing proofs and info of the function Extraction procedure, and one facing the better half software program applications.

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It is also assumed that all the points belong to the shape to be reconstructed. Finally, from a technical point of view, special care is needed to take into account changes of topology between the initial guess (such as a sphere) and the desired solution (such as a torus). To overcome some of this problems, a new implicit representations have been proposed under the name of level set methods [1]. 1 in the 2-D case). Then, instead of evolving the curve F , we consider the evolution of the function O and extract the zero levels to see the results.

Given a sparse feature saliency tensor field as input, each input token casts a tensor vote, and generates a tensor value at any location in the neighborhood defined by the voting kernels. This cast tensor value describes the voter's estimate of geometric information at the location, and the associated saliency of the estimation. The actual value T of the tensor vote cast by the input token at a given location q, is given by the tensor field voting functions T(q)=V(S,q-p), where • q-p is the vector joining the voting sitep and the receiving site q, expressed in the coordinate system {e^^ei^e^ defined by 5.

This cast tensor value describes the voter's estimate of geometric information at the location, and the associated saliency of the estimation. The actual value T of the tensor vote cast by the input token at a given location q, is given by the tensor field voting functions T(q)=V(S,q-p), where • q-p is the vector joining the voting sitep and the receiving site q, expressed in the coordinate system {e^^ei^e^ defined by 5. 44 • 5 is the tensor at/;, and • y is the tensor field corresponding to sum of the ball, stick, and surface voting kernel components.

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


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