PLDD: Point-lines distance distribution for detection of arbitrary triangles,regular polygons and circles |
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| Affiliation: | 1. Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei 10617, Taiwan;2. Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan;1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China;2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, PR China;1. School of Computer & Information, Hefei University of Technology, Hefei 230009, China;2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China;3. Institute of Health Sciences, Anhui University, Hefei, Anhui 230601, China;1. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China;2. University of Texas at San Antonio, United States |
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| Abstract: | In this paper, a general framework is presented for detection of arbitrary triangles, regular polygons, and circles, which is inspired by the common geometric property that the incenter of the shape is equidistant to the tangential lines of the contour points. The idea of point-lines distance distribution (PLDD) is introduced to compute the shape energy of each pixel. Then, shape centers can be exacted from the produced PLDD map, and shape radii are obtained simultaneously based on the distance distribution of the shape center. The shape candidates are thus determined and represented with three independent characteristics: shape center, shape radius, and contour points. Finally, distinguish different types of the shape from shape candidates using shape contour points information. Compared with exiting methods, the PLDD based method detects the shapes mainly using the inherent information of edge points, such as distance, and it is simple and general. Comparative experiments both on synthetic and natural images with the state of the art also prove that the PLDD based method performs more robustly and accurately with the maximal time complexity O(n2) at the worst condition. |
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| Keywords: | Point-lines distance distribution (PLDD) Geometric shape detection Shape energy Shape center Shape radius Circle detection Arbitrary triangle detection Regular polygon detection |
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