PLDD: Point-lines distance distribution for detection of arbitrary triangles, regular polygons and circles

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(n^2) at the worst condition.

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  Li-Qin Jia,et al.  Polygon detection based on meta-representation , 2011 .

[3]  T. Garlipp,et al.  Detection of linear and circular shapes in image analysis , 2006, Comput. Stat. Data Anal..

[4]  Fatin Zaklouta,et al.  Real-time traffic sign recognition in three stages , 2014, Robotics Auton. Syst..

[5]  Gang Wu,et al.  A Shape Detection Method Based on the Radial Symmetry Nature and Direction-Discriminated Voting , 2007, 2007 IEEE International Conference on Image Processing.

[6]  Ling-Hwei Chen,et al.  A fast ellipse/circle detector using geometric symmetry , 1995, Pattern Recognit..

[7]  Nick Barnes,et al.  The regular polygon detector , 2010, Pattern Recognit..

[8]  Panos Liatsis,et al.  Genetically fine-tuning the Hough transform feature space, for the detection of circular objects , 1998, Image Vis. Comput..

[9]  Keqiu Li,et al.  An effective solution for trademark image retrieval by combining shape description and feature matching , 2010, Pattern Recognit..

[10]  Miki Haseyama,et al.  Real-time polygon extraction from complex images , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[11]  D. Chaudhuri,et al.  A simple least squares method for fitting of ellipses and circles depends on border points of a two-tone image and their 3-D extensions , 2010, Pattern Recognit. Lett..

[12]  Bhabani P. Sinha,et al.  Parallel algorithms for identifying convex and non-convex basis polygons in an image , 2005, Parallel Comput..

[13]  Hua Wang,et al.  The feature extraction of nonparametric curves based on niche genetic algorithms and multi-population competition , 2005, Pattern Recognit. Lett..

[14]  Andrew F. Laine,et al.  Circle recognition through a 2D Hough Transform and radius histogramming , 1999, Image Vis. Comput..

[15]  Luc Van Gool,et al.  Multi-view traffic sign detection, recognition, and 3D localisation , 2014, 2009 Workshop on Applications of Computer Vision (WACV).

[16]  Jordi Vitrià,et al.  Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification , 2009, IEEE Transactions on Intelligent Transportation Systems.

[17]  Ponnuthurai N. Suganthan,et al.  Evaluation of genetic operators and solution representations for shape recognition by genetic algorithms , 2002, Pattern Recognit. Lett..

[18]  E. R. Davies,et al.  Machine vision - theory, algorithms, practicalities , 2004 .

[19]  Geoff A. W. West,et al.  Salience Distance Transforms , 1995, CVGIP Graph. Model. Image Process..

[20]  S. Jaishankar,et al.  3D Off-Line Path Planning For Aerial Vehicle Using Distance Transform Technique , 2011, ICCS.

[21]  Atsushi Imiya,et al.  The randomized-Hough-transform-based method for great-circle detection on sphere , 2007, Pattern Recognit. Lett..

[22]  Roberto Cipolla,et al.  Skeletonization using an extended Euclidean distance transform , 1995, Image Vis. Comput..

[23]  Rujie Liu,et al.  Shape detection from line drawings with local neighborhood structure , 2010, Pattern Recognit..

[24]  Antonio Fernández-Caballero,et al.  An optimization on pictogram identification for the road-sign recognition task using SVMs , 2010, Comput. Vis. Image Underst..

[25]  Luca Salgarelli,et al.  Support Vector Machines for TCP traffic classification , 2009, Comput. Networks.

[26]  Zhi-Heng Wang,et al.  Detection of arbitrary triangle , 2011, 2011 4th International Congress on Image and Signal Processing.

[27]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[28]  Heikki Kälviäinen,et al.  Randomized or probabilistic Hough transform: unified performance evaluation , 2000, Pattern Recognit. Lett..

[29]  Yerach Doytsher,et al.  Right‐Angle Rooftop Polygon Extraction in Regularised Urban Areas: Cutting the Corners , 2004 .

[30]  Hilario Gómez Moreno,et al.  Traffic sign shape classification and localization based on the normalized FFT of the signature of blobs and 2D homographies , 2008 .

[31]  Nassir Navab,et al.  Distance transform templates for object detection and pose estimation , 2009, CVPR.

[32]  Joaquim A. Jorge,et al.  Polygon Detection from a Set of Lines , 2023, ArXiv.

[33]  Raúl Enrique Sánchez-Yáñez,et al.  Circle detection on images using genetic algorithms , 2006, Pattern Recognit. Lett..

[34]  V. F. F. Leavers.  Shape Detection in Computer Vision Using the Hough Transform , 2011 .

[35]  Shu-Cherng Fang,et al.  On the Convergence of a Population-Based Global Optimization Algorithm , 2004, J. Glob. Optim..

[36]  Satoshi Goto,et al.  An MRF model-based approach to the detection of rectangular shape objects in color images , 2007, Signal Process..

[37]  Nick Barnes,et al.  Real-Time Regular Polygonal Sign Detection , 2006 .

[38]  Ki-Sang Hong,et al.  Linear band detection based on the Euclidean distance transform and a new line segment extraction method , 2001, Pattern Recognit..

[39]  Erik Valdemar Cuevas Jiménez,et al.  Circle detection using electro-magnetism optimization , 2014, Inf. Sci..

[40]  Chung-Lin Huang,et al.  Road sign detection and recognition using matching pursuit method , 2001, Image Vis. Comput..

[41]  Dariu M Gavrila,et al.  REAL-TIME OBJECT DETECTION USING DISTANCE TRANSFORMS , 1998 .

[42]  Xiaohui Liu,et al.  Real-time traffic sign recognition from video by class-specific discriminative features , 2010, Pattern Recognit..

[43]  Xiaohong W. Gao,et al.  Recognition of traffic signs based on their colour and shape features extracted using human vision models , 2006, J. Vis. Commun. Image Represent..

[44]  Jamshid Dehmeshki,et al.  Automated detection of lung nodules in CT images using shape-based genetic algorithm , 2007, Comput. Medical Imaging Graph..