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

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

ISBN-10: 0444503536

ISBN-13: 9780444503534

This publication represents a precis of the examine now we have been undertaking because the early Nineteen Nineties, and describes a conceptual framework which addresses a few present shortcomings, and proposes a unified process for a large category of difficulties. whereas the framework is outlined, our learn maintains, and a few of the weather offered right here will without doubt 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 method and its implementation. specifically, we illustrate the restrictions of the 2.5D caricature, and inspire using a illustration by way of layers instead.
In bankruptcy 2, we assessment the various appropriate examine within the literature. The dialogue specializes in basic computational techniques for early imaginative and prescient, and person equipment are just brought up as references. bankruptcy three is the elemental bankruptcy, because it offers the weather of our salient characteristic inference engine, and their interplay. It brought tensors with a view to characterize 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 by way of the engine defined past. In bankruptcy five, we practice the accepted framework to the inference of areas, curves, and junctions in 2-D. The enter may perhaps take the shape of 2-D issues, without or with orientation. We illustrate the method on a couple of examples, either easy and complex. In bankruptcy 6, we observe the framework to the inference of surfaces, curves and junctions in 3-D. right here, the enter comprises a suite of 3-D issues, without or with as linked common or tangent course. We exhibit a few illustrative examples, and likewise element to a couple functions of the strategy. 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 publication with a number of feedback, and talk about destiny examine directions.
We comprise three appendices, one on Tensor Calculus, one facing proofs and information of the characteristic Extraction approach, and one facing the significant other software program programs.

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Recently, Shi and Malik have devised a clustering by graph technique called normalized cuts and have applied it to image segmentation [76] and motion segmentation and tracking [77]. Most of the clustering techniques are iterative and sensitive to \ht a priori choice of the number of clusters in the feature space. If this choice is very poor, the final partition of the space may be incorrect as well. This sensitivity is an important limitation in com30 puter vision, where only rarely is the number of significant feature properties known a priori.

We refer the interested reader to Sethian [114] for a review. In our context, Zhao, Osher, Merriman and Kang [142] recently proposed an approach for shape reconstruction using the level set method. 1 Principle of the level set formulation In the 2-D case, a curve can be seen as the zero crossing of a function defined over a 3-D space. 2 3 polation problem. Let 5 = {;c^} e Q in ^ or ^ be the set of points we wish to interpolate. 6) using for instance techniques developed by Mauch [89]. 7) vr where p>l is fixed and correspond to the desired norm.

In particular, regularization theory provides techniques to determine the best X [85, 86]. There also exists a large body of results dealing with the form of the stabilizing function G that ensures both uniqueness of the result and convergence of the iterative search for a solution. In the case of early vision, the problem is transformed into the non-linear, scalar functional optimization framework, which can then be solved using standard numerical techniques, namely the variational principles [27, 36, 37, 40, 80].

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

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