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A matching algorithm on statistical properties of Harris corner

机译:哈里斯角统计特性的匹配算法

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The fundamental goal of target recognition and video tracking is to match target template with source image. Most matching methods are based on image intensity or multi-feature points. And the latter method is more popular for its high accuracy and small calculation. Image Registration Based on Feature Points focus on effective feature extraction of image points and paradigm. Harris corner in the image rotation, gray, noise and viewpoint change conditions, has an ideal match results, is more recent application of one feature point. This paper extract the Harris corner deviation and covariance firstly, experiments show that the two features exclusive, then applied them to image registration for the first time. A set of actual images have shown, this proposed method not only overcomes the complicated background, gray uneven distribution problems, but also pan and zoom the image has a good resistance.
机译:目标识别和视频跟踪的基本目标是使目标模板与源图像匹配。大多数匹配方法基于图像强度或多特征点。后一种方法因其精度高,计算量小而广受欢迎。基于特征点的图像配准专注于图像点和范式的有效特征提取。哈里斯角在图像旋转,灰度,噪声和视点变化的条件下,具有理想的匹配效果,是近来应用的一个特征点。本文首先提取了Harris角点偏差和协方差,实验表明这两个特征互斥,然后将它们首次应用于图像配准。一组实际图像已经表明,该方法不仅克服了背景复杂,灰度分布不均匀的问题,而且对图像的平移和缩放具有良好的抵抗力。

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