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首页> 外文会议>Computational Intelligence for Image Processing, 2009. CIIP '09 >A dual belief propagation method for shape recognition
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A dual belief propagation method for shape recognition

机译：一种用于形状识别的双重置信传播方法

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摘要

We present a shape recognition framework which includes two steps: shape searching and shape matching by deformation. First, the user can draw a contour shape descriptor as a search template. The first Bayesian belief propagation (BP I) algorithm is used to find possible targets allowing for translation, scale, and rotation transformations to all contours in a cluttered image. The contour segments with common transformation values are grouped and hypothesized as belonging to the contour in the search template. The search template is then transformed for each possible transformation value. A second belief propagation (BP II) is applied to perform a deformable contour matching. The matching score or cost function determines whether there is an actual match. The algorithm overcomes the weaknesses of the other approaches since it does not require any pre-processing to detect feature points, it can match targets at any position, scale, or rotation transformations, and it does not use any accumulation space that my have peak clustering problems such as in the Hough transform.
机译：我们提出了一个形状识别框架，该框架包括两个步骤：形状搜索和通过变形进行形状匹配。首先，用户可以绘制轮廓形状描述符作为搜索模板。第一个贝叶斯信念传播（BP I）算法用于查找可能的目标，从而允许对杂乱图像中的所有轮廓进行平移，缩放和旋转变换。具有共同变换值的轮廓线段被分组并假设为属于搜索模板中的轮廓线。然后针对每个可能的转换值对搜索模板进行转换。应用第二置信传播（BP II）来执行可变形轮廓匹配。匹配分数或成本函数确定是否存在实际匹配。该算法克服了其他方法的缺点，因为它不需要进行任何预处理即可检测特征点，它可以在任何位置，比例或旋转变换中匹配目标，并且不使用具有峰值聚类的任何累积空间。 Hough转换等问题。

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