Circular hole detection algorithm based on image block

Circular hole detection is a common problem in computer vision and pattern recognition. Randomized Hough transform and randomized circle detection algorithm are commonly used in circle detection, with good detection robustness and accuracy. But for circular holes with smaller radius, the two algorithms will be too slow because of a large number of invalid sampling. Thus the circular hole detection algorithm based on image block is proposed in order to improve the speed of circular hole detection. The algorithm divides the image into several small blocks and 3 points from the same block are randomly selected for sampling each time. If a candidate circular hole can be obtained by calculating these 3 points, then the points in the 4 blocks (or less) closest to the center of the candidate hole will be used for evidence-collecting to determine whether the candidate circular hole is true. Experimental results on a large number of synthetic images and real images show that the detection speed of circular hole detection algorithm proposed is much faster than the speed of randomized Hough transform and randomized circle detection algorithm. In addition, the proposed algorithm has the same detection robustness and accuracy as the randomized circle detection algorithm. The block strategy proposed in this paper is also applicable to the detection of elliptical holes.

[1]  Kuo-Liang Chung,et al.  Efficient symmetry-based screening strategy to speed up randomized circle-detection , 2012, Pattern Recognit. Lett..

[2]  Weifeng Liu,et al.  Road Detection by Using a Generalized Hough Transform , 2017, Remote. Sens..

[3]  Erik Valdemar Cuevas Jiménez,et al.  Multi-circle detection on images inspired by collective animal behavior , 2012, Applied Intelligence.

[4]  Lei Xu,et al.  A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving , 2007, Pattern Recognit..

[5]  Ching-Yuen Chan,et al.  An opposition-based chaotic GA/PSO hybrid algorithm and its application in circle detection , 2012, Comput. Math. Appl..

[6]  Shichao Zhang,et al.  Robust Perceptual Image Hashing Based on Ring Partition and NMF , 2014, IEEE Transactions on Knowledge and Data Engineering.

[7]  Kuo-Liang Chung,et al.  Efficient sampling strategy and refinement strategy for randomized circle detection , 2012, Pattern Recognit..

[8]  Marte A. Ramírez-Ortegón,et al.  Circle detection using discrete differential evolution optimization , 2011, Pattern Analysis and Applications.

[9]  Magnus Andersson,et al.  A fast and robust circle detection method using isosceles triangles sampling , 2016, Pattern Recognit..

[10]  Weidong Yi,et al.  Curvature aided Hough transform for circle detection , 2016, Expert Syst. Appl..

[11]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[12]  Lian-yuan Jiang,et al.  Fast detection of multi-circle with randomized Hough transform , 2009 .

[13]  Kuo-Liang Chung,et al.  An Efficient Randomized Algorithm for Detecting Circles , 2001, Comput. Vis. Image Underst..

[14]  A. Oualid Djekoune,et al.  Incremental circle hough transform: An improved method for circle detection , 2017 .

[15]  Erik Cuevas,et al.  Circle detection on images based on the Clonal Selection Algorithm (CSA) , 2015 .

[16]  Erik Valdemar Cuevas Jiménez,et al.  Automatic multiple circle detection based on artificial immune systems , 2012, Expert Syst. Appl..

[17]  Erik Valdemar Cuevas Jiménez,et al.  Multi-circle detection on images using artificial bee colony (ABC) optimization , 2012, Soft Comput..

[18]  Guili Xu,et al.  An efficient curve detection algorithm , 2016 .

[19]  Dong Liu,et al.  A robust circle detection algorithm based on top-down least-square fitting analysis , 2014, Computers & electrical engineering.

[20]  Dario Cazzato,et al.  Randomized circle detection with isophotes curvature analysis , 2015, Pattern Recognit..

[21]  Yanhui Guo,et al.  A novel Hough transform based on eliminating particle swarm optimization and its applications , 2008, Pattern Recognit..

[22]  Lianyuan Jiang,et al.  Efficient randomized Hough transform for circle detection using novel probability sampling and feature points , 2012 .

[23]  Bidyut Baran Chaudhuri,et al.  A survey of Hough Transform , 2015, Pattern Recognit..

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

[25]  Rudolf Scitovski,et al.  Multiple circle detection based on center-based clustering , 2015, Pattern Recognit. Lett..

[26]  Min Liu,et al.  Power histogram for circle detection on images , 2015, Pattern Recognit..

[27]  Thanh Phuong Nguyen,et al.  Line and circle detection using dense one-to-one Hough transforms on greyscale images , 2016, EURASIP J. Image Video Process..

[28]  Ali Özgün Ok,et al.  A New Approach for the Extraction of Aboveground Circular Structures From Near-Nadir VHR Satellite Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Kuo-Liang Chung,et al.  A Pruning-and-Voting Strategy to Speed up the Detection for Lines, Circles, and Ellipses , 2008, J. Inf. Sci. Eng..

[30]  Feng Zhang,et al.  Circle detection using scan lines and histograms , 2013 .

[31]  Ajith Abraham,et al.  Automatic circle detection on digital images with an adaptive bacterial foraging algorithm , 2010, Soft Comput..

[32]  Emre Baseski,et al.  Circular Oil Tank Detection From Panchromatic Satellite Images: A New Automated Approach , 2015, IEEE Geoscience and Remote Sensing Letters.

[33]  Yongqiang Ye,et al.  Fast circle detection algorithm based on sampling from difference area , 2018 .