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Optimizing Region of Support for Boundary-Based Corner Detection: A Statistic Approach

机译:基于边界的角点检测的支持区域优化:一种统计方法

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摘要

Boundary-based corner detection has been widely applied in spline curve fitting, automated optical inspection, image segmentation, object recognition, etc. In order to obtain good results, users usually need to adjust the length of region of support to resist zigzags due to quantization and random noise on digital boundaries. To automatically determine the length of region of support for corner detection, Teh-Chin and Guru-Dinesh presented adaptive approaches based on some local properties of boundary points. However, these local-property based approaches are sensitive to noise. In this paper, we propose a new approach to find the optimum length of region of support for corner detection based on a statistic discriminant criterion. Since our approach is based on the global perspective of all boundary points, rather than the local properties of some points, the experiments show that the determined length of region of support increases as the noise intensity strengthens. In addition, the detected corners based on the optimum length of region of support are consistent with human experts' judgment, even for noisy boundaries.
机译:基于边界的角点检测已广泛应用于样条曲线拟合,自动光学检查,图像分割,目标识别等。为了获得良好的结果,用户通常需要调整支撑区域的长度以抵抗由于量化而产生的锯齿以及数字边界上的随机噪声。为了自动确定用于拐角检测的支撑区域的长度,Teh-Chin和Guru-Dinesh提出了基于边界点某些局部属性的自适应方法。但是,这些基于本地属性的方法对噪声敏感。在本文中,我们提出了一种新的方法,该方法可根据统计判别准则找到用于拐角检测的最佳支撑区域长度。由于我们的方法基于所有边界点的全局视角,而不是某些点的局部特性,因此实验表明,随着噪声强度的增强,确定的支撑区域长度会增加。此外,即使对于嘈杂的边界,基于最佳支撑区域长度检测到的角点也与人类专家的判断一致。

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