Abstract
Straight line detection is a basic problem in image processing and has been extensively studied from different aspects, but most of the existing algorithms need to know the number of straight lines in an image in advance. However, the Bayesian Ying-Yang (BYY) harmony learning can make model selection automatically during parameter learning for the Gaussian mixture modeling, which can be further applied to detecting the correct number of straight lines automatically by representing the straight lines with Gaussians or Gaussian functions. In this paper, a gradient BYY harmony learning algorithm is proposed to detect the straight lines automatically from an image as long as the pre-assumed number of straight lines is larger than the true one. It is demonstrated by the simulation and real image experiments that this gradient BYY harmony learning algorithm can not only determine the number of straight lines automatically, but also detect the straight lines accurately against noise.
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References
Ballard, D.: Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition 13(2), 111–122 (1981)
Illingworth, J., Kittler, J.: A Survey of the Hough Transform. Computer Vision, Graphics and, Image Processing 44, 87–116 (1988)
Xu, L., Oja, E., Kultanen, P.: A New Curve Detection Method: Randomized Hough Transform (RHT). Pattern Recognition Letter 11, 331–338 (1990)
Olson, C.F.: Constrained Hough Transform for Curve Detection. Computer Vision and Image Understanding 73(3), 329–345 (1999)
Olson, C.F.: Locating Geometric Primitives by Pruning The Parameter Space. Pattern Recognition 34(6), 1247–1256 (2001)
Liu, Z.Y., Qiong, H., Xu, L.: Multisets Mixture Learning-Based Ellipse Detection. Pattern Recognition 39, 731–735 (2006)
Xu, L.: Best Harmony, Unified RPCL and Automated Model Selection for Unsupervised and Supervised Learning on Gaussian Mixtures, Three-Layer Nets and ME-RBF-SVM Models. International Journal of Neural Systems 11(1), 43–69 (2001)
Xu, L.: BYY Harmony Learning, Structural RPCL, and Topological Self-Organzing on Mixture Modes. Neural Networks 15, 1231–1237 (2002)
Ma, J., Wang, T., Xu, L.: A Gradient BYY Harmony Learning Rule on Gaussian Mixture with Automated Model Selection. Neurocomputing 56, 481–487 (2004)
Ma, J., Gao, B., Wang, Y., Cheng, Q.: Conjugate and Natural Gradient Rules for BYY Harmony Learning on Gaussian Mixture with Automated Model Selection. International Journal of Pattern Recognition and Artificial Intelligence 19, 701–713 (2005)
Ma, J., Wang, L.: BYY Harmony Learning on Finite Mixture: Adaptive Gradient Implementation and A Floating RPCL Mechanism. Neural Processing Letters 24(1), 19–40 (2006)
Ma, J., Liu, J.: The BYY Annealing Learning Algorithm for Gaussian Mixture with Automated Model Selection. Pattern Recognition 40, 2029–2037 (2007)
Ma, J., He, X.: A Fast Fixed-Point BYY Harmony Learning Algorithm on Gaussian Mixture with Automated Model Selection. Pattern Recognition Letters 29(6), 701–711 (2008)
Lu, Z., Cheng, Q., Ma, J.: A gradient BYY Harmony Learning Algorithm on Mixture of Experts for Curve Detection. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 250–257. Springer, Heidelberg (2005)
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Chen, G., Li, L., Ma, J. (2008). A Gradient BYY Harmony Learning Algorithm for Straight Line Detection. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_69
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DOI: https://doi.org/10.1007/978-3-540-87732-5_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
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