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Unsupervised Clustering in Hough Space for Identification of Partially Occluded Objects
September 1999 (vol. 21 no. 9)
pp. 946-950

Abstract—An automated approach for template-free identification of partially occluded objects is presented. The contour of each relevant object in the analyzed scene is modeled with an approximating polygon whose edges are then projected into the Hough space. A structurally adaptive self-organizing map neural network generates clusters of collinear and/or parallel edges, which are used as the basis for identifying the partially occluded objects within each polygonal approximation. Results on a number of cases under different conditions are provided.

[1] J.R. Ullman, “Edge Replacement in the Recognition of Occluded Objects,” Pattern Recognition, vol. 26, no. 12, pp. 1,771-1,784, 1993.
[2] B. Bhanu and J.C. Ming, “Recognition of Occluded Objects: A Cluster Structure Algorithm,” Pattern Recognition, vol. 20, no. 2, pp. 199-211, 1987.
[3] M.W. Koch and R.L. Kashyap, “Using Polygons to Recognize and Locate Partially Occluded Objects,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, no. 4, pp. 483-494, Apr. 1987.
[4] O. Yáñez-Suárez and M.R. Azimi-Sadjadi, “Identification and Measurement of Fiber Glass Particles in Electron Microscopy Imagery Using a High-Order Correlation Process,” Review of Progress in Quantitative Non-Destructive Evaluation, D.O. Thompson and D.E. Chimenti, eds., vol. 16B, pp. 1,463-1,470. New York: Plenum Press, 1997.
[5] R.O. Duda and P.E. Hart, "Use of the Hough transforms to detect lines and curves in pictures," Comm. ACM, vol. 15, no. 1, pp. 11-15, 1972
[6] I.D. Svalbe, “Natural Representations for Straight Lines and the Hough Transform on Discrete Arrays,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 9, Sept. 1989.
[7] O. Yáñez-Suárez and M.R. Azimi-Sadjadi, “Entropy-Driven Structural Adaptation in Sample-Space Self-Organizing Feature Maps for Pattern Classification,” Proc. Int'l Conf. Neural Networks, no. 1, pp. 287-291, Houston, Tex., 1997.
[8] A.P. Dempster, N.M. Laird, and D.B. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm,” J. Royal Statistical Soc. B., vol. 39, pp. 1-38, 1977.
[9] J.S.J. Lee, R.M. Haralick, and L.G. Shapiro, “Morphological Edge Detection,” Int'l J. Robotics and Automation, vol. RA-3, no. 2, pp. 142-156, 1987.
[10] A.K. Jain, Fundamentals of Digital Image Processing. Prentice Hall, 1989.
[11] T. Pavlidis and S.L. Horowitz, “Segmentation of Plane Curves,” IEEE Trans. Computers, vol. 23, no. 8, pp. 860-870, Aug. 1974.
[12] C.S. Fahn, “An Adaptive Reduction Procedure for the Piecewise Linear Approximation of Digitized Curves,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 9, Sept. 1989.
[13] J. Yuan and C.Y. Suen, “An Optimal O(n) Algorithm for Identifying Line Segments from a Sequence of Chain Codes,” Pattern Recognition, vol. 28, no. 5, pp. 635-646, 1995.
[14] X. Xie, R. Sudhakar, and H. Zuang, “Corner Detection by a Cost Minimization Approach,” Pattern Recognition, vol. 26, no. 8, pp. 1,235-1,243, 1993.
[15] T. Kohonen, Self-Organizing Maps, first ed. Berlin: Springer Verlag, 1995.
[16] N.K. Bose and P. Liang, Neural Network Fundamentals with Graphs, Algorithms, and Applications, first ed. New York, NY: McGraw-Hill, 1996.
[17] G. Deco and D. Obradovic, An Information-Theoretic Approach to Neural Computing, first ed., pp. 7-22. New York: Springer-Verlag, 1997.

Index Terms:
Image analysis, occluded objects, unsupervised clustering SOM network, Hough space.
Oscar Yáñez-Suárez, Mahmood R. Azimi-Sadjadi, "Unsupervised Clustering in Hough Space for Identification of Partially Occluded Objects," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 946-950, Sept. 1999, doi:10.1109/34.790436
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