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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将目标模板与源图像匹配。 大多数匹配方法都基于图像强度或多特征点。 后者方法对于其高精度和小的计算更受欢迎。 基于特征点的图像登记侧重于图像点和范例的有效特征提取。 哈里斯角在图像旋转,灰色,噪音和视点变化条件下,具有理想的匹配结果,最近的应用是一个特征点。 本文提取哈里斯角偏差和协方差首先,实验表明,这两个特点是独家,然后将它们应用于第一次进行图像配准。 已经显示了一组实际图像,这一提出的方法不仅克服了复杂的背景,灰色不均匀分布问题,还摇动并缩放图像具有良好的阻力。

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