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Curve Reconstruction From Points Cloud Based On Adaptive Reducing Interval Genetic Algorithm

Posted on:2006-09-09Degree:MasterType:Thesis
Country:ChinaCandidate:Y Y ChengFull Text:PDF
GTID:2120360182988023Subject:Computational Mathematics
Abstract/Summary:PDF Full Text Request
The curve reconstruction has strong applications in reverse engineering and computer view. An important task of the reverse engineering is reconstructing the geometry models from the physical models and it contains four steps: the data collection, pretreatment, the surface reconstruction and the construction of CAD models. In computer view, we often want to consider how to get the datum from the geometry models so that we can analysis and identify the shape of models easily. All of these required to figure out curves base on the unorganized points with the noise. The curve reconstruct is an important topic in geometry construction. As the technology of 3D scan becomes mature, the topic of point cloud becomes hotter and hotter. The reconstruction from the organized points have many mature methods, so people pay more attention to the reconstruction from unorganized points. On the other hand, Genetic algorithm, as a computational model simulating the biological evolution process of genetic selection theory of Darwin, is a whole new global optimization algorithm and is widely used in many fields with its remarkable characteristic of simplicity, commonality, stability, practicability, suitability for parallel process and high-efficiency. On the major premise of feasibility of this theory, this article based on the practice of forerunners, has done some further research work about the self-adapted of genetic algorithm for curve reconstruction from point cloud with a satisfactory result.This article is divided into four parts. The first chapter introduces the background and application of the curve and surface reconstruction and the latest research in the world.The second chapter briefly introduces the research history of genetic algorithm and biological background at the beginning.Then discuss the basic realization method of the algorithm, its operators, its basic characteristics, its applications and several thoughts of the author about how to use genetic algorithm to solve problems.In forth chapter, an adaptive genetic algorithm is presented for curve reconstruction based on the regular distribution property of the point set. We divide the region of point set into many grids and in each grid we elect a point by the adaptive genetic algorithm. Because of the asymmetry of the date points in each grid, here we investigate the sphere-of-influence graph (SIG) with several extensions, which provides a natural notion of proximity in our context, to adjust each data point that we have elected. We make these adjusted data points in order and then we can reconstruct the curve using B-spline curve based on these ordered data points.The forth chapter gives a lots of examples, which prove that the algorithm we present is feasible and simple, specially, as to the self-intersect point set and the point set having corner point it can also work out a satisfied result.
Keywords/Search Tags:curve reconstruction, point cloud, adaptive genetic algorithm
PDF Full Text Request
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