Abstract
Traditional algorithm of Harris needs to select a parameter for computing interest values of pixels, and its recognition ability for some types of corners is poor. To solve this problem, this paper proposes a corner detection method which is based on local standard deviation and logarithmic computing. The method decreases effecting to the response values of corners that near the candidate interested points through computing the logarithms of gradient, so it can detect different types of corner more effectively. It can redefine the interest value function according to the statistical features of the standard deviation to decide the corners. The function could avoid selecting value of parameters by person, and it could directly judge whether a candidate interested point is a corner, to make the algorithm has a higher objectivity. The experimental results show that the method can effectively detect the corners of various types, and it can achieve a more accurate effect of positioning.
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References
Kitchen, L.: A Rosenfeld.: Gray Level Corner Detection. J Pattern Recognition Letters 3(1), 95–102 (1982)
Langridge, D.J.: Curve Encoding and the Detection of Discontinuities. J. CVGIP 20(1), 58–71 (1982)
Mediono, G., Yasumoto, Y.: Corner Detection and Curve Representation Using Cubic B-splines. J. Computer Vision Graphics and Image Processing 39(3), 267–278 (1987)
Zhong, B., Liao, W.: Corner Detection Based on Accumulative Chord Length of Refined Digital Curves. J. Journal of Computer Aided-Design and Computer Graphics 16(7), 939–943 (2004). (in Chinese)
Harris, C.G., Stephens, M.J., Kesselman, C.: A combined Corner and Edge Detector. In: Proceedings Fourth Alvey Vision Conference, Manchester (1988)
Mikolajczyk, K., Schmid, C.: Scale and Affine Invariant Interest Point Detectors. J. InternSational Journal of Computer Vision 60(1), 63–86 (2004)
Cheng, B., Tang, X.: Study of Harris scale invariant keypoint detector. J. Journal of Zhejiang University. 43(5), 855–859 (2009). (in Chinese)
Husbands, P., Mill, F., Kesselman, C.: Simulated Co-evolution as the Mechanism for Emergent Planning and Scheduling. In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the 4th International Conference on Genetic Algorithms, pp. 264–270 (1991)
Hillis, D.W.: Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure. J. Physica D: Nonlinear Phenomena 42, 228–234 (1990)
Trajkovic, M., Hedley, M.: Fast corner detection. Image and Vision Computing 16(2), 75–87 (1998)
Smith, S., Brady, M.: A new approach to low level image processing. J. International Journal of Computer Vision 23(1), 45–789 (1997)
Zhang, Q., Liu, Z., Pang, Y., et al.: Automatic registration of aerophotos based on SUSAN operator. J. Acta Geodaeticact Cartographica Sinica 32(3), 245–250 (2003). (in Chinese)
Distinctive, D.L.: Image Features from Scale-Invariant Keypoints. J. International Journal of Computer Vision 60(2), 91–110 (2004)
Wu, Y.: An image matching method based on SIFT and SUSAN features. J. Image Processing and Multimedia Technology 30(12), 33–39 (2011). (in Chinese)
Zou, B., Ruan, P., Xiang, Y., et al.: An automatic panoramic images mosaic algorithm with precise matching. J. Computer Engineering and Science 32(8), 60–63 (2010). (in Chinese)
Wang, Z., Huangfu, K., Wan, J., et al.: Multi-scale wavelet based two dimensional cornet detection. J. Journal of National University of Defense Technology 21(2), 46–49 (1999). (in Chinese)
Li, Z., Shen, Y., Kesselman, C: A robust corner detector based on curvature scale space and harris. In: Image Analysis and Signal Processing, pp. 223–226. Hubei (2011)
Xu, X., Tan, J.: An improved Multi-scale Harris deature point detection mothod. J. Jisuanji Gongcheng/ Computer Engineering 38, 174–177 (2012). (in Chinese)
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Wan, L., Yu, Z., Yang, Q. (2015). Corner Detection Algorithm with Improved Harris. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Di, K. (eds) Advances in Image and Graphics Technologies. IGTA 2015. Communications in Computer and Information Science, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47791-5_30
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DOI: https://doi.org/10.1007/978-3-662-47791-5_30
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