摘要
本文研究了同一场景不同视点的两幅图像,基于双目视觉,通过小波变换提取特征点,进行像点匹配获取基础矩阵,采用对极几何理论,从而实现三维立体重建。
首先我们介绍了计算机的立体视觉理论,对极几何的基本原理,讨论了基础矩阵求解算法。然后介绍了立体视觉匹配技术,三维重建。立体视觉匹配是计算机视觉和非接触测量研究中最基本的关键问题之一,该技术通过匹配像点获取基础矩阵,从而实现三维立体再现,但是同时图像匹配也是最难彻底解决的问题。本文的图像匹配采用的是特征点匹配,采用小波变换提取图像突变特征点的方法,而且该方法与传统的采用角点方法在性能上作了一些比较。在立体匹配方面,提出了一种良好的特征点匹配方法,首先采用视差方法和区域支持方法求取初始匹配,引入候选匹配原则,从而有效的提高了匹配的正确率,得到初始的匹配点对集合。在此基础上利用基础矩阵对匹配点对集合不断优化,一方面不断去除误配点对,另一方面得到了精确的基础矩阵。
最后我们介绍了立体视觉基于图像的分层化三维重建理论,并且给出了实验结果和分析,并对部分匹配点对进行了射影空间的三维重建。
The two images of the same scene with different viewpoints is studied in this massage. Based on computer vision, with wavelet transformation feature points extraction, fundamental matrix is gotten by feature points matching. The image-based 3D is reconstructed by the theory of epipolar geometry.
At first the theory of computer vision is proposed. The principle of epipolar geometry is introduced. And arithmetic about how to solve fundamental matrix is discussed. Then the theory of vision stereo matching and 3-D reconstruction are introduced.Vision stereo matching is one of the fundamental and significant problems in the study of the computer vision and contactless measurements. This technique makes it possible to reproduce a three-dimensional stereo by getting fundamental matrix through matching pixels. But matching is the most difficult problem to be solved completely. In this article, image is matched by feature points. The characteristic of corner points and feature points extracted by wavelet transformation are presented and compared. In this passage a nicer feature points matching method is proposed. At first, initial feature points pairs are being found with parallax method and area supporting. In this way more correct feature point pairs are founded out and initial feature point pairs aggregation is built up. Then the aggregation is optimized continually through fundamental matrix method. On one hand, error point pairs are moved out. On the other hand, accurate fundamental matrix is built.
At last,the hierarchical structure of 3-D reconstruction technology is introduced. Experiments on the real images show that the algorithm in this work is applicable and effective. And a proportion of matching point pairs is reconstructed in projective space.
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