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Shape Recognition with Generalized Beam Angle Statistics

机译:广义束角统计的形状识别

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摘要

In this study, we develop a new shape descriptor and a matching algorithm in order to find a given template shape in an edge detected image without extracting the boundary. The shape descriptor based on Generalized Beam Angle Statistics (GBAS) defines the angles between the lines connecting each boundary point with the rest of the points, as random variable. Then, it assigns a feature vector to each point using the moments of beam angles. The proposed matching algorithm performs shape recognition by matching the feature vectors of boundary points on the template shape and the edge pixels on the image. The matching process also considers the spatial distance of the edge pixels. The experiments performed on MPEG-7 data set show that the template shapes are found successfully on the noisy images.
机译:在这项研究中,我们开发了一种新的形状描述符和匹配算法,以便在不提取边界的情况下在边缘检测图像中找到给定的模板形状。基于广义波束角统计量(GBAS)的形状描述符将连接每个边界点与其余点的线之间的角度定义为随机变量。然后,它使用束角矩将特征向量分配给每个点。所提出的匹配算法通过匹配模板形状上的边界点的特征向量和图像上的边缘像素来进行形状识别。匹配过程还考虑边缘像素的空间距离。对MPEG-7数据集进行的实验表明,在嘈杂的图像上成功找到了模板形状。

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