角点在轮廓尺度空间的行为分析与检测研究
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摘要
图像的局部特征在保留图像重要信息的同时,又有效地减少了图像处理的数据量,极大地提高了运算速度。因此特征提取成为模式识别与计算机视觉等图像处理相关领域的基础性研究内容。在众多图像特征中,角点不仅具有最少的数据量,而且稳定性极高,这使角点检测成为特征提取的重要分支。然而目前的角点检测算法更多的是在实验层面得到解释,缺乏系统的数学理论论证。基于此,本文在轮廓尺度空间中研究角点的演化行为,为角点描述提供一种有效的表达方式。本文的研究工作如下:
①将二阶微分算子Laplacian应用到轮廓曲线上,利用角点为曲线上的不光滑点这一性质,定义曲线Laplacian变换的2-范数为角点响应(简称为Laplacian角点响应),指出该角点响应与流行的曲率角点响应能够导出相同的角点位置,定义具有一致性。
②在不同尺度下通过与Gaussian函数卷积来演化轮廓生成轮廓的Gaussian尺度空间,并定义演化轮廓的Laplacian角点响应为角点的LoG (Laplacian of Gaussian)响应。之后系统地分析了LoG角点的行为特征,以解析形式描述了单角点模型与双角点模型在尺度空间中的形态。
③首先根据LoG角点行为理论提出LoG角点检测算法,然后通过LoG算子的逼近形式DoG (Difference of Gaussian)算子构造DoG角点检测算法。最后与经典的CSS (Curvature Scale Space)角点检测算法进行对比,实验结果表明LoG与DoG算法具有更好的角点检测性能,以及更强的对噪声的鲁棒性。
Image local features retain important information, which reduce the data quantity of image processing effectively, and improve the operation speed greatly. Thus, feature extraction has become the basic research of the related areas of image processing such as pattern recognition and computer vision. Among the many image features, not only do the corners have the least amount of data, but also they are quite stable, which makes corner detection as an important branch of feature extraction. However, most of the corner detection algorithms are merely implemented in the experimental level, lacking of mathematical demonstration. Based on above, this paper will investigate the evolution behavior of corners in the contour scale space, which will provide an excellent corner description. Specifically, this research work is as follows:
①The Laplacian differential operator is applied to the contour curves. According to the property that a corner is non-smooth in the curve, the corner response is defined as the 2-norm of Laplacian of the curve (referred to as the Laplacian corner response). These responses are able to locate the same corners as the popular curvature corner response. Therefore, the definition is consistent with previous.
②The evolved contours is obtained by convolving the original contour with Gaussian function at different scales, which consist of the Gaussian scale space of contours. And the Laplacian corner reponse of the evolved contours is defined as the Laplacian of Gaussian (LoG) scale space of corners. Afterwards, the behavior of LoG corners is investigated in the form of analytical solution via single and double corner models.
③The LoG corner detection algorithm is proposed on the basis of the behavior of LoG corners. In addition, the Difference of Gaussian (DoG) operator that is the approximation of LoG operator is used to construct the DoG corner detection algorithm. Eventually, both the LoG and DoG algorithm are proved to process more superior detection performance and stronger robustness against to noise than the classic CSS(Curvature Scale Space) algorithm.
①将二阶微分算子Laplacian应用到轮廓曲线上,利用角点为曲线上的不光滑点这一性质,定义曲线Laplacian变换的2-范数为角点响应(简称为Laplacian角点响应),指出该角点响应与流行的曲率角点响应能够导出相同的角点位置,定义具有一致性。
②在不同尺度下通过与Gaussian函数卷积来演化轮廓生成轮廓的Gaussian尺度空间,并定义演化轮廓的Laplacian角点响应为角点的LoG (Laplacian of Gaussian)响应。之后系统地分析了LoG角点的行为特征,以解析形式描述了单角点模型与双角点模型在尺度空间中的形态。
③首先根据LoG角点行为理论提出LoG角点检测算法,然后通过LoG算子的逼近形式DoG (Difference of Gaussian)算子构造DoG角点检测算法。最后与经典的CSS (Curvature Scale Space)角点检测算法进行对比,实验结果表明LoG与DoG算法具有更好的角点检测性能,以及更强的对噪声的鲁棒性。
Image local features retain important information, which reduce the data quantity of image processing effectively, and improve the operation speed greatly. Thus, feature extraction has become the basic research of the related areas of image processing such as pattern recognition and computer vision. Among the many image features, not only do the corners have the least amount of data, but also they are quite stable, which makes corner detection as an important branch of feature extraction. However, most of the corner detection algorithms are merely implemented in the experimental level, lacking of mathematical demonstration. Based on above, this paper will investigate the evolution behavior of corners in the contour scale space, which will provide an excellent corner description. Specifically, this research work is as follows:
①The Laplacian differential operator is applied to the contour curves. According to the property that a corner is non-smooth in the curve, the corner response is defined as the 2-norm of Laplacian of the curve (referred to as the Laplacian corner response). These responses are able to locate the same corners as the popular curvature corner response. Therefore, the definition is consistent with previous.
②The evolved contours is obtained by convolving the original contour with Gaussian function at different scales, which consist of the Gaussian scale space of contours. And the Laplacian corner reponse of the evolved contours is defined as the Laplacian of Gaussian (LoG) scale space of corners. Afterwards, the behavior of LoG corners is investigated in the form of analytical solution via single and double corner models.
③The LoG corner detection algorithm is proposed on the basis of the behavior of LoG corners. In addition, the Difference of Gaussian (DoG) operator that is the approximation of LoG operator is used to construct the DoG corner detection algorithm. Eventually, both the LoG and DoG algorithm are proved to process more superior detection performance and stronger robustness against to noise than the classic CSS(Curvature Scale Space) algorithm.
引文
[1]仇佩亮.信息论与编码[M].北京:高等教育出版, 2003.
[2] H. Wang, M. Brady. Real-time corner detection algorithm for motion estimation[J]. Image and Vision Computing, 1995, 13(9): 695-703.
[3] D. G. Lowe. Object recognition from local scale-invariant features[C]. In: Proceedings of the 7th IEEE International Conference on Computer Vision, Kerkyra, Corfu, Greece, 1999: 1150-1157.
[4] L. Sun, Y. Y. Tang, X. You. Corner detection for object recognition by using wavelet transform[C]. In: Proceedings of the 3rd IEEE International Conference on Machine Learning and Cybernetics, Shanghai, China, 2004: 4347-4351.
[5] F. Mokhtarian, A. K. Mackworth. A theory of multiscale, curvature-based shape representation for planar curves[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(8): 789-805.
[6] D. G. Lowe. Distinctive image features from scale-invariant keypoints detectors[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[7] Y. H. Gu, T. Tjahjadi. Corner-based feature extraction for object retrieval[C]. In: Proceedings of International Conference on Image Processing, Kobe, Japan, 1999: 119-123.
[8] G. Y. Tian, D. Gledhill, D. Taylor. Comprehensive interest points based imaging mosaic[J]. Pattern Recognition Letters, 2003, 24(9-10): 1171-1179.
[9] H. P. Moravec. Towards automatic visual obstacle avoidance[C]. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Pasadena, California, USA, 1977: 584.
[10] C. Harris, M. Stephens, A combined corner and edge detector[C]. Proceedings of the 4th Alvey Vision Conference, 1988: 147-151.
[11] Z. Zheng, H. Wang, EKTeoh. Analysis of Gray Level Corner Detection[J]. Pattern Recognition Letters, 1999, 20(2): 149-162.
[12] K. Mikolajczyk, C. Schmid. An affine invariant interest point detector. In: Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, 2002: 128-142.
[13] W. Forstner and E. Gulch: A fast operator for detection and precise location of distinct points, corners, and centers of circular features[C]. In: ISPRS Inter Commission Workshop on Fast Processing of Photogrammetric Data, Interlaken, 1987: 281-305.
[14] P. R. Beaudet. Rotationally invariant image operators[C]. In: Proceedings of the 4thInternational Joint Conference on Pattern Recognition, Tokyo, Japan, 1978: 579?583.
[15] R. Deriche, G. Giraudon. Accurate corner detection: an analytical study[C]. In: Proceedings of the 3rd IEEE International Conference on Computer Vision, Osaka, Japan, 1990: 66-70.
[16] L. Kitchen, A. Rosenfeld. Gray-level corner detection[J]. Pattern Recognition Letters, 1982, 1(2) 95-102.
[17] H. Wang, J. M. Brady. Corner detection with subpixel accuracy[J]. Technical Report OUEL 1925/92, Dept. Engineering Science, University of Oxford, 1992.
[18] S. M. Smith, J. M. Brady. SUSAN-A new approach to low level image processing[J]. International Journal of Computer Vision, 1997, 23(1): 45-78.
[19] A. Rattarangsi, R. T. Chin, Scale-based detection of corners of planar curves[J]. IEEE Transactions on Pattern analysis and Machine Intelligence, 1992, 14(2): 430-449.
[20] F. Mokhtarian, R. Suomela. Robust image corner detection through curvature scale space[J]. IEEE Transactions on Pattern analysis and Machine Intelligence, 1998, 20 (12): 1376-1381.
[21] G. Cong., S. D. Ma. Corner enhancement in curvature scale space[J]. Pattern Recognition, 1998, 31(10): 1491-1501.
[22] B. K. Ray, R. Pandyan. ACORD-an adaptive corner detector for planar curves[J]. Pattern Recognistion, 2003, 36(3): 703-708.
[23] X. C. He, N. H. C. Yung, Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support[C]. In: Proceedings of the 17th IEEE International Conference on Pattern Recognition, Washington, DC, USA, 2004: 791-794.
[24] M. Awrangjeb, G. Lu, and M. Murshed. An affine resilient curvature scale-space corner detector[C]. In: Proceedings of the 32nd IEEE International Conference on Acoustics, Speech, and Signal Processing, 2007: 1233-1236.
[25] X. Zhang, M. Lei, D. Yang, etc.. Multi-scale curvature product for robust image corner detection incurvature scale space[J]. Pattern Recognistion Letters, 2007, 28(5): 545-554.
[26] C. H. Chen, J. S. Lee, Y. N. Sun. Wavelet transformation for gray-level corner detection[J]. Pattern Recognition, 1995, 28 (4-5): 853?861.
[27] A. Quddus, M. Gabbouj. Wavelet-based corner detection technique using optimal scale[J]. Pattern Recognition Letters, 2002, 23 (1-3): 215-220.
[28] X. T. Gao, F. Sattar, A. Quddus, etc.. Multiscale contour corner detection based on local natural scale and wavelet transform[J]. Image and Vision Computing, 2007, 25(6): 890-898 .
[29] D. M. Tsai, H. T. Hou, H. J. Su. Boundary-based corner detection using eigenvalues of covariance matrices[J]. Pattern Recognition Letters, 1999, 20 (1): 31-40.
[30] C. H. Yeh. Wavelet-based corner detection using eigenvectors of covariance matrices[J].Pattern Recognition Letters, 2003, 24 (15): 2797-2806
[31] F. Shen, H. Wang. Corner detection based on modified Hough transform[J]. Pattern Recognition Letters, 2002, 23 (8): 1039-1049.
[32] M. Awrangjeb, G. Lu. Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique[J]. IEEE Transactions on Multimedia, 2008, 10(6): 1059-1072.
[33] D. Chetverikov, Z. Szabo. A Simple and Efficient Algorithm for Detection of High Curvature Points in Planar Curves. In: Proceedings of the 23rd Workshop of 23rd Workshop of the Austrian Pattern Recognition Group, 1999: 175-184.
[34] A. Masood, M. Sarfraz. Corner detection by sliding rectangles along planar curves[J]. Computers & Graphics, 2007, 31(3): 440-448.
[35] D. M. Tsai. Boundary-based corner detection using neural networks[J]. Pattern Recognition. 1997, 30(1): 85-97.
[36] H. Freeman, L. S. Davis. A corner-finding algorithm for chain-coded curves[J]. IEEE Transactions on Computers, 1977, 26(3): 297-303.
[37] H. L. Beus, S. S. H.Tiu. An improved corner detection algorithm based on chain-coded plane curves. Pattern Recognition[J], 1987, 20(3): 291-296.
[38] H. C. Liu, M. D. Srinath. Corner detection from chain-code[J]. Pattern Recognition, 1990, 23(1-2): 51-68.
[39] F. Arrebola, F. Sandoval. Corner detection and curve segmentation by multi-resolution chain-code linking[J]. Pattern Recognition, 2005, 38 (10): 1596-1614.
[40] T. Lindeberg. Scale-space for discrete signals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(3): 234-254.
[41] T. Lindeberg, J. Eklundh. Scale-space primal sketch:construction and experiments[J]. Image Vision Computing, 1992, 10(1): 3-18
[42] T. Lindeberg. Scale-space theory in computer vision[M]. Kluwer Academic Publishers, Dordrecht, Netherlands: 1994.
[43] T. Lindeberg Feature Detection with Automatic Scale Selection[J]. International Journal of Computer Vision, 1998, 30(2): 77-116.
[44] H. Asada, M. Brady. The curvature primal sketch[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986, 8(1):2-14.
[45] F. Mokhtarian, M. Bober. Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Kluwer Academic Publishers, Dordrecht, Netherlands, 2003
[46] B. Zhong, K. K. Ma, W. Liao. Scale-Space Behavior of Planar-Curve Corners[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(8): 1517-1524.
[47] B. Zhong, W. Liao. Direct Curvature Scale Space: Theory and Corner Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(3): 508-512.
[48]钟宝江.图象轮廓处理技术的基础算法研究[D].南京航空航天大学博士学位论文, 2006.
[49] D. Marr, E. C. Hildreth. Theory of edge detection[J]. Proceedings of the Royal Society of London Series B, Biological Sciences, 1980, 207(1167): 187-217.
[50] J. F. Canny. A Computational Approach to Edge Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698.
[51] F. Mohanna, F. Mokhtarian. Performance Evaluation of Corner Detection Algorithms under Similarity and Affine Transforms[C]. In: Proceedings of 12th British Machine Vision Conference, Manchester, U.K., 2001:353–362.
[52] D. Parks, J. P. Gravel. Corner Detection[EB/OL]. http://www.cim.mcgill.ca/~dparks/CornerDetector/index.htm, 2005-05-01/2010-04-28.
[53] D. Chetverikov, Z. Szabo. Corner detection-Standard images[EB/OL]. http://visual.ipan.sztaki.hu/corner/corner_click.html, 2008-11-26/2010-04-28.
[2] H. Wang, M. Brady. Real-time corner detection algorithm for motion estimation[J]. Image and Vision Computing, 1995, 13(9): 695-703.
[3] D. G. Lowe. Object recognition from local scale-invariant features[C]. In: Proceedings of the 7th IEEE International Conference on Computer Vision, Kerkyra, Corfu, Greece, 1999: 1150-1157.
[4] L. Sun, Y. Y. Tang, X. You. Corner detection for object recognition by using wavelet transform[C]. In: Proceedings of the 3rd IEEE International Conference on Machine Learning and Cybernetics, Shanghai, China, 2004: 4347-4351.
[5] F. Mokhtarian, A. K. Mackworth. A theory of multiscale, curvature-based shape representation for planar curves[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(8): 789-805.
[6] D. G. Lowe. Distinctive image features from scale-invariant keypoints detectors[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[7] Y. H. Gu, T. Tjahjadi. Corner-based feature extraction for object retrieval[C]. In: Proceedings of International Conference on Image Processing, Kobe, Japan, 1999: 119-123.
[8] G. Y. Tian, D. Gledhill, D. Taylor. Comprehensive interest points based imaging mosaic[J]. Pattern Recognition Letters, 2003, 24(9-10): 1171-1179.
[9] H. P. Moravec. Towards automatic visual obstacle avoidance[C]. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Pasadena, California, USA, 1977: 584.
[10] C. Harris, M. Stephens, A combined corner and edge detector[C]. Proceedings of the 4th Alvey Vision Conference, 1988: 147-151.
[11] Z. Zheng, H. Wang, EKTeoh. Analysis of Gray Level Corner Detection[J]. Pattern Recognition Letters, 1999, 20(2): 149-162.
[12] K. Mikolajczyk, C. Schmid. An affine invariant interest point detector. In: Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, 2002: 128-142.
[13] W. Forstner and E. Gulch: A fast operator for detection and precise location of distinct points, corners, and centers of circular features[C]. In: ISPRS Inter Commission Workshop on Fast Processing of Photogrammetric Data, Interlaken, 1987: 281-305.
[14] P. R. Beaudet. Rotationally invariant image operators[C]. In: Proceedings of the 4thInternational Joint Conference on Pattern Recognition, Tokyo, Japan, 1978: 579?583.
[15] R. Deriche, G. Giraudon. Accurate corner detection: an analytical study[C]. In: Proceedings of the 3rd IEEE International Conference on Computer Vision, Osaka, Japan, 1990: 66-70.
[16] L. Kitchen, A. Rosenfeld. Gray-level corner detection[J]. Pattern Recognition Letters, 1982, 1(2) 95-102.
[17] H. Wang, J. M. Brady. Corner detection with subpixel accuracy[J]. Technical Report OUEL 1925/92, Dept. Engineering Science, University of Oxford, 1992.
[18] S. M. Smith, J. M. Brady. SUSAN-A new approach to low level image processing[J]. International Journal of Computer Vision, 1997, 23(1): 45-78.
[19] A. Rattarangsi, R. T. Chin, Scale-based detection of corners of planar curves[J]. IEEE Transactions on Pattern analysis and Machine Intelligence, 1992, 14(2): 430-449.
[20] F. Mokhtarian, R. Suomela. Robust image corner detection through curvature scale space[J]. IEEE Transactions on Pattern analysis and Machine Intelligence, 1998, 20 (12): 1376-1381.
[21] G. Cong., S. D. Ma. Corner enhancement in curvature scale space[J]. Pattern Recognition, 1998, 31(10): 1491-1501.
[22] B. K. Ray, R. Pandyan. ACORD-an adaptive corner detector for planar curves[J]. Pattern Recognistion, 2003, 36(3): 703-708.
[23] X. C. He, N. H. C. Yung, Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support[C]. In: Proceedings of the 17th IEEE International Conference on Pattern Recognition, Washington, DC, USA, 2004: 791-794.
[24] M. Awrangjeb, G. Lu, and M. Murshed. An affine resilient curvature scale-space corner detector[C]. In: Proceedings of the 32nd IEEE International Conference on Acoustics, Speech, and Signal Processing, 2007: 1233-1236.
[25] X. Zhang, M. Lei, D. Yang, etc.. Multi-scale curvature product for robust image corner detection incurvature scale space[J]. Pattern Recognistion Letters, 2007, 28(5): 545-554.
[26] C. H. Chen, J. S. Lee, Y. N. Sun. Wavelet transformation for gray-level corner detection[J]. Pattern Recognition, 1995, 28 (4-5): 853?861.
[27] A. Quddus, M. Gabbouj. Wavelet-based corner detection technique using optimal scale[J]. Pattern Recognition Letters, 2002, 23 (1-3): 215-220.
[28] X. T. Gao, F. Sattar, A. Quddus, etc.. Multiscale contour corner detection based on local natural scale and wavelet transform[J]. Image and Vision Computing, 2007, 25(6): 890-898 .
[29] D. M. Tsai, H. T. Hou, H. J. Su. Boundary-based corner detection using eigenvalues of covariance matrices[J]. Pattern Recognition Letters, 1999, 20 (1): 31-40.
[30] C. H. Yeh. Wavelet-based corner detection using eigenvectors of covariance matrices[J].Pattern Recognition Letters, 2003, 24 (15): 2797-2806
[31] F. Shen, H. Wang. Corner detection based on modified Hough transform[J]. Pattern Recognition Letters, 2002, 23 (8): 1039-1049.
[32] M. Awrangjeb, G. Lu. Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique[J]. IEEE Transactions on Multimedia, 2008, 10(6): 1059-1072.
[33] D. Chetverikov, Z. Szabo. A Simple and Efficient Algorithm for Detection of High Curvature Points in Planar Curves. In: Proceedings of the 23rd Workshop of 23rd Workshop of the Austrian Pattern Recognition Group, 1999: 175-184.
[34] A. Masood, M. Sarfraz. Corner detection by sliding rectangles along planar curves[J]. Computers & Graphics, 2007, 31(3): 440-448.
[35] D. M. Tsai. Boundary-based corner detection using neural networks[J]. Pattern Recognition. 1997, 30(1): 85-97.
[36] H. Freeman, L. S. Davis. A corner-finding algorithm for chain-coded curves[J]. IEEE Transactions on Computers, 1977, 26(3): 297-303.
[37] H. L. Beus, S. S. H.Tiu. An improved corner detection algorithm based on chain-coded plane curves. Pattern Recognition[J], 1987, 20(3): 291-296.
[38] H. C. Liu, M. D. Srinath. Corner detection from chain-code[J]. Pattern Recognition, 1990, 23(1-2): 51-68.
[39] F. Arrebola, F. Sandoval. Corner detection and curve segmentation by multi-resolution chain-code linking[J]. Pattern Recognition, 2005, 38 (10): 1596-1614.
[40] T. Lindeberg. Scale-space for discrete signals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(3): 234-254.
[41] T. Lindeberg, J. Eklundh. Scale-space primal sketch:construction and experiments[J]. Image Vision Computing, 1992, 10(1): 3-18
[42] T. Lindeberg. Scale-space theory in computer vision[M]. Kluwer Academic Publishers, Dordrecht, Netherlands: 1994.
[43] T. Lindeberg Feature Detection with Automatic Scale Selection[J]. International Journal of Computer Vision, 1998, 30(2): 77-116.
[44] H. Asada, M. Brady. The curvature primal sketch[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986, 8(1):2-14.
[45] F. Mokhtarian, M. Bober. Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Kluwer Academic Publishers, Dordrecht, Netherlands, 2003
[46] B. Zhong, K. K. Ma, W. Liao. Scale-Space Behavior of Planar-Curve Corners[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(8): 1517-1524.
[47] B. Zhong, W. Liao. Direct Curvature Scale Space: Theory and Corner Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(3): 508-512.
[48]钟宝江.图象轮廓处理技术的基础算法研究[D].南京航空航天大学博士学位论文, 2006.
[49] D. Marr, E. C. Hildreth. Theory of edge detection[J]. Proceedings of the Royal Society of London Series B, Biological Sciences, 1980, 207(1167): 187-217.
[50] J. F. Canny. A Computational Approach to Edge Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698.
[51] F. Mohanna, F. Mokhtarian. Performance Evaluation of Corner Detection Algorithms under Similarity and Affine Transforms[C]. In: Proceedings of 12th British Machine Vision Conference, Manchester, U.K., 2001:353–362.
[52] D. Parks, J. P. Gravel. Corner Detection[EB/OL]. http://www.cim.mcgill.ca/~dparks/CornerDetector/index.htm, 2005-05-01/2010-04-28.
[53] D. Chetverikov, Z. Szabo. Corner detection-Standard images[EB/OL]. http://visual.ipan.sztaki.hu/corner/corner_click.html, 2008-11-26/2010-04-28.