基于粒子群优化的基本矩阵高精度估计研究

Yan-ju Liu, Ze-dong Li, Hongwei Gao
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引用次数: 0

摘要

本文研究了图像中基本矩阵的恢复问题。首先,利用Harris算子检测角点,并匹配互相关角点;根据极坐标方程的定义,匹配图像中特征点的极坐标必须经过对应点。然后,以角与对应极坐标的距离为准则,正确提取匹配点并消除误差。最后,以极坐标方程残差为目标函数,利用粒子群算法得到了更为精确的基本矩阵。实验结果证明了相关算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study of Fundamental Matrix High-Precision Estimation Based on Particle Swarm Optimization
The paper researches the issue that fundamental matrix is recovered in images. Firstly, the corners are detected by Harris operator, and matches cross-correlation corners. According to the definition of polar equation, the polar of feature point must pass corresponding point in matching images. Then, to extract correctly matching points and eliminate error by the criterion which is the distance between corner and corresponding polar. At the end, More precise fundamental matrix is got by Particle Swarm Optimization according to the target function that is residual error of polar equation. Experimental result proves that interrelated arithmetic is effective.
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