Corner Localization in Digital Images Based on Radon Transform

doi:10.16182/j.issn1004731x.joss.17-0420

Abstract

Abstract: Location accuracy is one of the key indexes of measuring the performance of feature point.. High location accuracy feature point detection has important implications for the applications such as 3D reconstruction, motion estimation, target tracking and recognition, image registration, etc. A corner location algorithm based on Radon transform is proposed.Edge binary images are obtained with traditional method first, ideal straight lines and curves are extracted then, potential corner locations are calculated next, fake corners are rejected finally. Experimental results on both simulation and real images show that, this method performs better than the existing methods on interest point localization accuracy. Meanwhile, the strategy makes this method robust to image noises.

Key words: corner, localization accuracy, Radon transform, potential corner, ideal straight line

CLC Number:

TN911.73

Wang Xuguang, Zhang Qin, Su Jie. Corner Localization in Digital Images Based on Radon Transform[J]. Journal of System Simulation, 2019, 31(10): 2164-2173.

References

[1] Prakhya S M, Liu B, Lin W, et al.B-SHOT: A Binary 3D Feature Descriptor for Fast Keypoint Matching on 3D Point Clouds[J]. Autonomous Robots (S1573-7527), 2017, 41(7): 1-20.
[2] Quan L, Tang K.Polynomial Local Shape Descriptor on Interest Points for 3D Part-in-whole Matching[J]. Computer-Aided Design (S0010-4485), 2015, 59(C): 119-139.
[3] Wang Z, Wang Z, Liu H, et al.Scale-invariant Feature Matching Based on Pairs of Feature Points[J]. IET Computer Vision (S1751-9632), 2015, 9(6): 789-796.
[4] Cohen E A, Kim D, Ober R J.Cramér-Rao Lower Bound for Point Based Image Registration With Heteroscedastic Error Model for Application in Single Molecule Microscopy[J]. IEEE Transactions on Medical Imaging (S0278-0062), 2015, 34(12): 2632-2644.
[5] Yang G, Stewart C V, Sofka M, et al.Registration of Challenging Image Pairs: Initialization, Estimation, and Decision[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2007, 29(11): 1973-89.
[6] Yu X, Yang J, Wang T, et al.Key Point Detection by Max Pooling for Tracking[J]. IEEE Transactions on Cybernetics (S2168-2267), 2015, 45(3): 430-438.
[7] Limberger F A, Oliveira M M.Real-time Detection of Planar Regions in Unorganized Point Clouds[J]. Pattern Recognition (S0031-3203), 2015, 48(6): 2043-2053.
[8] Weng R, Lu J, Tan Y P.Robust Point Set Matching for Partial Face Recognition[J]. IEEE Transactions on Image Processing (S1057-7149), 2016, 25(3): 1163-1176.
[9] Miao Z, Jiang X.Interest Point Detection Using Rank Order LoG Filter[J]. Pattern Recognition (S0031-3203), 2013, 46(11): 2890-2901.
[10] Shi J, Tomasi C.Good Features to Track[C]. International Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE, 1994: 593-600.
[11] Harris C.A Combined Corner and Edge Detector[C]. Alvey Vision Conference, UK: The Plessey Company, 1988, 15(50): 10-5244.
[12] Loog M, Lauze F.The Improbability of Harris Interest Points[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2010, 32(6): 1141-1147.
[13] Mikolajczyk K, Schmid C.Scale & Affine Invariant Interest Point Detectors[J]. International Journal of Computer Vision (S0920-5691), 2004, 60(1): 63-86.
[14] Lowe D G.Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision (S0920-5691), 2004, 60(2): 91-110.
[15] Bay H, Tuytelaars T, Gool L V.SURF: Speeded Up Robust Features[C]. European Conference on Computer Vision. Graz, Austria: Springer, 2006: 404-417.
[16] Miao Z, Jiang X, Yap K.Contrast Invariant Interest Point Detection by Zero-Norm LoG Filter[J]. IEEE Transactions on Image Processing (S1057-7149), 2016, 25(1): 331-342.
[17] Matas J, Chum O, Urban M, et al.Robust Wide-baseline Stereo from Maximally Stable External Regions[J]. Image & Vision Computing (S0262-8856), 2004, 22(10): 761-767.
[18] Kimmel R, Zhang C, Bronstein A M, et al.Are MSER Features Really Interesting?[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2011, 33(11): 2316-2320.
[19] Forssen P E.Maximally Stable Colour Regions for Recognition and Matching[C]. IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA: IEEE, 2007: 1-8.
[20] Deng H, Zhang W, Mortensen E, et al.Principal Curvature-Based Region Detector for Object Recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA: IEEE, 2007: 1-8.
[21] Kim J, Grauman K.Boundary Preserving Dense Local Regions[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2014: 17(5): 931-943.
[22] Smith S M, Brady J M.SUSAN - A New Approach to Low Level Image Processing[J]. International Journal of Computer Vision (S0920-5691), 2015, 23(1): 45-78.
[23] Smith S M, Brady J M.SUSAN - A New Approach to Low Level Image Processing[J]. International Journal of Computer Vision (S0920-5691), 1997, 23(1): 45-78.
[24] Rosten E, Porter R, Drummond T.Faster and Better: A Machine Learning Approach to Corner Detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2008, 32(1): 105-119.
[25] Kadir T, Brady M.Saliency, Scale and Image Description[J]. International Journal of Computer Vision (S0920-5691), 2001, 45(2): 83-105.
[26] Mokhtarian F, Suomela R.Robust Image Corner Detection Through Curvature Scale Space[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 1998, 20(12): 1376-1381.
[27] He X C, Yung N H C. Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support[C]. International Conference on Pattern Recognition. Cambridge, UK: IEEE, 2004: 791-794.
[28] Awrangjeb M, Lu G.An Improved Curvature Scale-Space Corner Detector and a Robust Corner Matching Approach for Transformed Image Identification[J]. IEEE Transactions on Image Processing (S1057-7149), 2008, 17(12): 2425-2441.
[29] 王旭光. 图像特征点检测与匹配研究[D]. 北京: 中国科学院自动化研究所, 2009.
Wang Xuguang.A Study of Image Feature Point Detection and Matching[D]. Beijing: Institute of Automation, Chinese Academy of Sciences, 2009.