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

This publication represents a precis of the learn we now have been undertaking because the early Nineteen Nineties, and describes a conceptual framework which addresses a few present shortcomings, and proposes a unified strategy for a huge type of difficulties. whereas the framework is outlined, our learn maintains, and a few of the weather offered right here will doubtless evolve within the coming years.It is prepared in 8 chapters. within the advent bankruptcy, we current the definition of the issues, and provides an summary of the proposed strategy and its implementation. specifically, we illustrate the restrictions of the 2.5D caricature, and encourage using a illustration by way of layers instead.
In bankruptcy 2, we evaluation a number of the correct learn within the literature. The dialogue specializes in common computational techniques for early imaginative and prescient, and person tools are just brought up as references. bankruptcy three is the basic bankruptcy, because it provides the weather of our salient function inference engine, and their interplay. It brought tensors so as to characterize info, tensor fields which will encode either constraints and effects, and tensor vote casting because the communique scheme. bankruptcy four describes the characteristic extraction steps, given the computations played by means of the engine defined prior. In bankruptcy five, we follow the widespread 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 technique on a couple of examples, either simple and complex. In bankruptcy 6, we practice the framework to the inference of surfaces, curves and junctions in 3D. right here, the enter involves a suite of 3-D issues, without or with as linked general or tangent path. We convey a couple of illustrative examples, and likewise aspect to a couple functions of the process. 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 booklet with a number of comments, and speak about destiny learn directions.
We contain three appendices, one on Tensor Calculus, one facing proofs and information of the function Extraction method, and one facing the spouse software program programs.

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We refer the reader to the tutorial by Zhang [92] and the articles by Meer [56, 57] on robust parameter estimation techniques for image analysis and computer vision. , m are the carriers (basis functions). Two major issues need to be addressed: (1) parameter estimation (2) inlier/outlier classification (data error correction) Robust estimators often make use of the algebraic properties of the carrier functions and the constraint equation to handle parameters estimation, and apply statistical tools to tackle data error correction.

Despite the existence of multiple solutions, humans usually perceive one, and only one, scene configuration. To imitate this 21 perceptual capability, computer vision researchers have, since the early days, attempted to identify and model the physical constraints that make the early vision problem determined and solvable, as in Marr's model [55]. In [68], Poggio and Torre have shown that the under-constrained nature of the early vision problems has led to the development of a class of methods that use variational principles to impose specific physical constraints.

We refer the reader to the works by Faugeras and Berthod [19] and Hummel and Zucker [44] for two different formulations of the continuous relaxation labeling process. 3 Stochastic relaxation labeling Stochastic relaxation labeling is similar to continuous relaxation labeling, except that the labeling weights, and the constraints preference weights are replaced by probability distributions. Stochastic relaxation labeling for computer vision is based on the use of a stochastic modeling of physical phenomena, known as Markov Random Fields (MRF).

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