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Salient Closed Contour Detection based on Multiscale Analysis and Minimum-Angle

机译:基于多尺度分析和最小角度的突出的闭合轮廓检测

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This paper presents a novel computational approach, named MAMA, to extract perceptually salient boundaries from a set of noisy boundary fragments detected in real images. The approach first acquire a contour map using multiscale analysis. Then constructs an relation matrix between arbitrary the endpoints of each edge. At last the most salient boundary is detected by searching the smallest angle that lines got from two points and the point's grad. The proposed approch guarantees the boundary is a global structure. We collect a varietry of images for testing the proposed approach with enouraging results.
机译:本文提出了一种名为MAMA的新颖的计算方法,从实际图像中检测到的一组嘈杂的边界片段中提取感知上突出的边界。该方法首先使用多尺度分析获取轮廓图。然后在任意每个边路的终点之间构造关系矩阵。最后,通过搜索从两点和点毕业的最小角度来检测最突出的边界。拟议的批准担保边界是全球结构。我们收集了一种用于测试所提出的方法的变量,以便通过开发结果进行测试。

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