基于选择性相关系数的鲁棒图像配准

Yutaka Sato, S. Kaneko, S. Igarashi
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引用次数: 12

摘要

我们提出了一种新的鲁棒图像配准方法,称为“选择性相关系数(SCC)”,以搜索在恶劣光照,离子或部分遮挡下的图像。在匹配前选择图像的像素,生成相关掩模图像。掩码图像可以从任何对象图像和模板图像定义的二进制编码增量符号图像派生。遮挡区域的掩蔽率理论上预期为50%,而未遮挡区域的掩蔽率远低于50%。由于遮挡可以忽略被遮挡区域的亮度不一致,实现了在病态环境下的鲁棒性。在此基础上,提出了掩模扩展方法以获得更稳定的鲁棒性。该方法提高了概率掩蔽的有效性,遮挡区域的掩蔽率约为70%。本文对该方法进行了理论建模和分析,并给出了一些真实图像的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Image Registration Using Selective Correlation Coefficient
\Ve propose a new method for robust image registrat,ion called 'Selective Correlation Coefficient (SCC)' in order to search images under illconditioned illuminat,ion or partial occlusion. A correlation mask image is generated for selecting pixels of a image before matching. The mask image can be derived from a binary-coded increment sign image defined from any object image and the template image. The masking rate of occluded regions is theoretically expected to be 50%, while unoccluded regions have much lower rate than 50%. We realize robustness under ill-conditioned environment since inconsistent brightness of occluded regions can be omitted by the masking operation. We propose, furthermore, the mask expanding procedure to get more stable robustness. The effectiveness of probabilistic masking increases by the procedure, resulting the rate around 70% of masking of occluded regions. This paper includes a theoretical modeling and analysis of the proposed method and some experimental results with real images.
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