A New Method for Polygon Detection Based on Hough Parameter Space and USAN Region

Li Shupei, Z. Hui, Zhang Zhisheng, Xia Zhijie
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Abstract

This paper propose a new approach that combine Hough Transform (HT) and corner detection to detect polygons, which consider integrated characteristics not the individual characteristics. We establish a Polygon Parameter Space (PPS) to fit and characterize polygons, which consist of angles, coordinates, USAN values and every two lines of intersections. Firstly, canny operator is used to extract edges map, applied HT to detect line along edges of polygon shape and compute PPS. Secondly, corner detection among intersections is realized by comparing USAN value with angle of intersections, an adaptive threshold and adjusted brightness of nucleus of USAN is introduced to obtain accurate vertices from corners. Finally, we propose an algorithm based on Deep First Search (DFS) to fit the set of vertices regardless convex polygons (CVPs) or concave polygons (CCPs) according to parameters in PPS. The experimental results show that the proposed approach can effectively detect polygons with a less running time and higher accuracy, and shows the advantage of detecting the CVP and CCP shapes of broken vertices.
基于Hough参数空间和USAN区域的多边形检测新方法
本文提出了一种将霍夫变换与角点检测相结合的多边形检测方法,该方法考虑的是多边形的整体特征而不是单个特征。建立了多边形参数空间(Polygon Parameter Space, PPS)来拟合和表征多边形,多边形由角度、坐标、USAN值和每两条交点线组成。首先,利用canny算子提取边缘映射,利用HT检测多边形形状边缘的直线,计算PPS;其次,通过对比USAN值与交点角度实现交点间的角点检测,引入自适应阈值和调整USAN核亮度,从交点处获取精确的顶点;最后,我们提出了一种基于深度优先搜索(Deep First Search, DFS)的算法,根据深度优先搜索中的参数对凸多边形(CVPs)和凹多边形(ccp)的顶点集进行拟合。实验结果表明,该方法可以有效地检测多边形,且运行时间短,精度高,在检测破碎顶点的CVP和CCP形状方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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