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Contour Feature Detection Based on Gestalt Rule and Maximum Entropy of Neighborhood

机译:基于GESTALT规则和邻域的最大熵的轮廓特征检测

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摘要

A novel approach is presented to detect contour of object. Firstly, the zero-cross operator to imitate the visual receptive field is used to detect edge of image. Secondly, facing the large amount of noise in complex background, the neighborhood description operator is designed, and the neighborhood information of interesting point is analyzed as well. Then the contours of objects are acquired by combining with the Gestalt psychology theories. During the process, the maximum entropy and state transition probability of feature mode are introduced to ensure the effectiveness of contour detection. Finally, the experiments verify the validity of the proposed method.
机译:提出了一种新的方法来检测物体的轮廓。首先,用于模拟视觉接收领域的零交叉操作者用于检测图像的边缘。其次,面对复杂背景中的大量噪声,设计了邻域描述操作员,并且还分析了有趣点的邻居信息。然后通过与Gestalt心理学理论组合来获取物体的轮廓。在此过程中,引入了特征模式的最大熵和状态转换概率以确保轮廓检测的有效性。最后,实验验证了所提出的方法的有效性。

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