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[22] McLaughlin, R. A. and Alder, M. D. The Hough Transform versus the UpWrite, TR97-02, CIIPS, The University of Western Australia, 1997. [23] Milisavljevi , N. Comparison of Three Methods for Shape Recognition in the Case of Mine Detection, c Pattern Recognition Letters, 20(11 13), pp. 1079 1083, 1999. [24] Haig, T., Attikiouzel, Y. and Alder, M. D. Border Following: New Definition Gives Improved Border, IEE Proceedings-I, 139(2), pp. 206 211, 1992. [25] McLaughlin, R. A. Randomized Hough Transform: Improved Ellipse Detection with Comparison, TR97-01, CIIPS, The University of Western Australia, 1997. [26] Xu, L. Randomized Hough Transform (RHT): Basic Mechanisms, Algorithms and Computational Complexities, CVGIP: Image Understanding, 57(2), pp. 131 154, 1993. [27] Xu, L., Oja, E. and Kultanen, P. A New Curve Detection Method: Randomized Hough Transform (RHT), Pattern Recognition Letters, 11, pp. 331 338, 1990. [28] Duda, O. and Hart, P. E. Use of the Hough Transform to Detect Lines and Curves in Pictures, Communications of the Association for Computing Machinery, 15(1), pp. 11 15, 1972. [29] K lvi inen, H., Hirvonen, P., Xu, L. and Oja, E. Comparisons of Probabilistic and Non-Probabilistic Hough Transforms, Proceedings of 3rd European Conference on Computer Vision, Stockholm, Sweden, pp. 351 360, 1994. [30] Leavers, V. F. Shape Detection in Computer Vision Using the Hough Transform, Springer, London, 1992. [31] Yuen, H. K., Illingworth, J. and Kittler, J. Detecting Partially Occluded Ellipses using the Hough Transform, Image and Vision Computing, 7(1), pp. 31 37, 1989. [32] Capineri, L., Grande, P. and Temple, J. A. G. Advanced Image-Processing Technique for Real-Time Interpretation of Ground Penetrating Radar Images, International Journal on Imaging Systems and Technology, 9, pp. 51 59, 1998. [33] Milisavljevi , N., Bloch, I. and Acheroy, M. Application of the Randomized Hough Transform to c Humanitarian Mine Detection, Proceedings of the 7th IASTED International Conference on Signal and Image Procesing (SIP2001), Honolulu, Hawaii, USA, pp. 149 154, 2001. [34] Banks, E. Antipersonnel Landmines Recognising and Disarming, Brassey s, London-Washington, 1997. [35] Milisavljevi , N. and Bloch, I. Sensor Fusion in Anti-Personnel Mine Detection Using a Two-Level Belief c Function Model, IEEE Transactions On Systems, Man, and Cybernetics C, 33(2), pp. 269 283, 2003. [36] Milisavljevi , N., Bloch, I., van den Broek, S. P. and Acheroy, M. Improving Mine Recognition c through Processing and Dempster Shafer Fusion of Ground-Penetrating Data, Pattern Recognition, 36(5), pp. 1233 1250, 2003. [37] Dubois, D., Grabisch, M., Prade, H. and Smets, P. Assessing the Value of a Candidate, Proceedings of 15th Conference on Uncertainty in Artificial Intelligence (UAI 99), Stockholm, Sweden, pp. 170 177, 1999. [38] Smets, P. Belief Functions: the Disjunctive Rule of Combination and the Generalized Bayesian Theorem, International Journal of Approximate Reasoning, 9, pp. 1 35, 1993. [39] Schubert, J. On Nonspecific Evidence, International Journal of Intelligent Systems, 8, pp. 711 725, 1993. [40] Smets, P. Constructing the Pignistic Probability Function in a Context of Uncertainty, Uncertainty in Artificial Intelligence, 5, pp. 29 39, 1990. [41] Milisavljevi , N., Bloch, I. and Acheroy, M. Characterization of Mine Detection Sensors in Terms of Belief c Functions and their Fusion, First Results, Proceedings of 3rd International Conference on Information Fusion (FUSION 2000), II, pp. ThC3.15 ThC3.22, 2000.
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This chapter presents a new method of grouping and matching line segments to recognize objects. We propose a dynamic programming-based formulation extracting salient line patterns by defining a robust and stable geometric representation that is based on perceptual organizations. As the end point proximity, we detect several junctions from image lines. We then search for junction groups by using the collinear constraint between the junctions. Junction groups similar to the model are searched in the scene, based on a local comparison. A DP-based search algorithm reduces the time complexity for the search of the model lines in the scene. The system is able to find reasonable line groups in a short time.
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