[1]刘 锂,段芃芃.基于角度搜索和距离判定的边缘检测算法[J].计算机技术与发展,2020,30(02):67-70.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 014]
　LIU Li,DUAN Peng-peng.Edge Detection Algorithm Based on Angle Search and Distance Judgment[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(02):67-70.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 014]
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基于角度搜索和距离判定的边缘检测算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年02期
页码:
67-70
栏目:
智能、算法、系统工程
出版日期:
2020-02-10

文章信息/Info

Title:
Edge Detection Algorithm Based on Angle Search and Distance Judgment
文章编号:
1673-629X(2020)02-0067-04
作者:
刘 锂; 段芃芃
成都理工大学 工程技术学院,四川 乐山 614007
Author(s):
LIU Li; DUAN Peng-peng
School of Engineering and Technology,Chengdu University of Technology,Leshan 614007,China
关键词:
边缘检测; 凸壳内缩; 角度搜索; 距离判断
Keywords:
edge detection; convex hull inside shrink; angle search; distance judgment
分类号:
TP319
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 02. 014
摘要:
在离散数据处理中,边缘检测算法的应用非常广泛。 针对当前边缘检测算法存在的问题,如数据量的大小、数据 的几何特性、密度的集中程度、距离跨度等,提出了一种基于角度搜索和距离判定的边缘检测算法,详细考虑了边界的多 情况、凹边形和凸边形的几何特性。 首先建立四个点的边界凸壳,然后采用角度判断的方法进行凸壳内缩,最后进行距离 判断的二次内缩检验。 该方法简洁、高效、便于理解与实现,并且可以针对各种情况的离散数据。 采用乐山市城区的商业 圈聚类分析数据进行测试,该离散数据体现了高密度、低密度、数据量大、距离跨度大等特点,通过实验证明了该算法边界 检测效果较好,异常数据小,能够准确甄别出边界区域数据。 同时也证明了该算法的通用性、简洁性和高效性。
Abstract:
Edge detection algorithms are widely used in discrete data processing. In view of the problems existing in the current edge detection algorithms,such as the size of data volume,geometric characteristics of data,concentration degree of density,distance span and so on,we propose an edge detection algorithm based on angle search and distance determination,considering the multi-case of boundary, geometric characteristics of concave and convex in detail. First,the boundary of the four point convex hull is established,then the method of angle judgment within convex hull isadopted,and finally thedistancejudgment within thesecondary shrinkagetest iscarried out. This method,which is simple,efficient,and easy to understand and implement,can be used for all kinds of discrete data. The commercial cluster analysisdataofLeshan city aretested. The discrete data reflects the characteristic sofhigh density, low density, large data volume, and large distance span and so on. Experiment shows that the algorithm is effective in boundary detection,with small abnormal data,and can accurately identify the boundary data. At the same time,its generality, simplicity and efficiency are also proved.

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更新日期/Last Update: 2020-02-10