Abstract
This Paper describes a technique for model-based Object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In Order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line Segments. The Problem is then formulated as that of finding the best describing match between a hypothesized Object and the image. A special form of template matthing is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with Standard techniques indicate the scope for further research in this direction.
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References
Agrawal, P.K., Sharir, M., Toledo, S.: Applications of parametric searching in geometric optimization. In: Proc. of 3rd. ACM SIAM Symp. on Discrete Algorithms, pp. 72–82 (1992)
Akutsu, T., Tamaki, H., Tokuyama, T.: Distribution of distances and triangles in a point set and algorithms for computing the largest common point sets. In: Proc. 13th. Annual ACM Symp. on Computational Geometry, Centre Universitaire Méditerranéen, Nice, France, pp. 314–323 (1997)
Alt, H., Behrends, B., Blömer, J.: Measuring the resemblance of polygonal shapes. In: Proc. of 7th. Annual ACM Symposium on Computational Geometry, pp. 186–193 (1991)
Ballard, D., Brown, C.M.: Computer Vision. Prenctice Hall, Englewood Cliffs (1982)
Beveridge, R.J.: Local Search Algorithms for Geometric Object Recognition: Optimal Correspondence and Pose. PhD thesis, University of Massachusetts, Amherst (May 1993)
Chakraborty, S., Deb, K.: Analytic curve detection from a noisy binary egde map using genetic algorithm. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 129–138. Springer, Heidelberg (1998)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Grimson, W.E.L.: Object Recognition by Computer: The Role of Geometric Constraints. MIT Press, Cambridge (1990)
Grimson, W.E.L., Huttenlocher, D.P.: On the sensitivity of the Hough transform for object recognition. IEEE Trans. Pattern Anal. Machine Intell. PAMI-12, 255–274 (1990)
Eric, W., Grimson, L.: The effect of indexing on the complexity of object recognition. Technical Report A.I. Memo No. 1226, Artificial Intelligence Laboratory, MIT (1990)
Hill, A., Taylor, C.J.: Model-based image interpretation using genetic algorithms. Image and Vision Computing 10, 295–300 (1992)
Huttenlocher, D.P., Kedem, K., Sharir, M.: The upper envelope of voronoi surfaces and its applications. Discrete and Computational Geometry 9, 267–291 (1993)
Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. Pat. Anal. and Mach. Intel. 15, 850–863 (1993)
Huttenlocher, D.P., Ullman, S.: Recognizing solid objects by alignment with an image. Inter. Journal of Computer Vision 5(2), 195–212 (1990)
Kälviäinen, H., Hirvonen, P., Xu, L., Oja, E.: Houghtool–a software package for Hough transform calculation. In: Proceedings of the 9th Scandinavian Conference on Image Analysis, June 1995, pp. 841–844 (1995), http://www.lut/dep.fi/tite/XHoughtool/xhoughtool.html
Kälviäinen, H., Xu, L., Oja, E.: Recent versions of the Hough transform and the Randomized Hough transform: Overview and comparisons. Technical Report 37, Department of Information Technology, Lappeenranta University of Technology, Finland (1993)
Lamdan, Y., Wolfson, H.J.: Geometric Hashing: A general and efficient modelbased recognition scheme. In: International Conference on Computer Vision, pp. 238–249 (1988)
Liu, Z.Q., Caelli, T.M.: Multiobjective pattern recognition and detection in noisy backgrounds using a hierarchical approach. Computer Vision, Graphics, and Image Processing 44, 296–306 (1988)
Loader, C.: Local search algorithms for 2d geometric object recognition. Master’s thesis, Department of Computer Science. The University of Western Australia (1995)
Mergalit, A., Rosenfeld, A.: Using probabilistic domain knowledge to reduce the expected computational cost of template matching. Computer Vision, Graphics, and Image Processing 51, 219–234 (1990)
Pope, A.R.: Model-based object recognition: A survey of recent research. Technical Report TR-94-04, Department of Computer Science, University of British Columbia (January 1994)
Princen, J., Illingworth, J., Kittler, J.: A formal definition of the Hough transform: properties and relationships. J. Math. Imaging Vision 1, 153–168 (1992)
Roth, G., Levine, M.D.: Geometric primitive extraction using a genetic algorithm. IEEE Trans. Pattern Anal. Machine Intell. PAMI-16(9), 901–905 (1994)
Rucklidge, W.J.: Locating objects using the Hausdor_ distance. In: Proc. of 5th International Conference on Computer Vision, pp. 457–464 (1995)
Sarachik, K.B.: Limitations of geometric Hashing in the presence of gaussian noise. Technical Report A.I. Memo No. 1395, Artificial Intelligence Laboratory, MIT (1992)
Swets, D.L., Punch, B., John, W.: Genetic algorithms for object recognition in a complex scene. In: Proceedings of the International Conference on Image Processing, Washington, D.C, October 1995, pp. 595–598 (1995)
Xu, L., Oja, E.: Randomized Hough transform (RHT): Basic mechanisms, algorithms, and computational complexities. CVGIP: Image Understanding 57(2), 131–154 (1993)
Yaroslavsky, L.P.: Digital Picture Processing. Springer, Berlin (1985)
Yuen, K.S.Y., Lam, L.T.S., Leung, D.N.K.: Connective Hough transform. Image and Vision Computing 11(5) (1993)
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Chakraborty, S., De, S., Deb, K. (1999). Model-Based Object Recognition from a Complex Binary Imagery Using Genetic Algorithm. In: Poli, R., Voigt, HM., Cagnoni, S., Corne, D., Smith, G.D., Fogarty, T.C. (eds) Evolutionary Image Analysis, Signal Processing and Telecommunications. EvoWorkshops 1999. Lecture Notes in Computer Science, vol 1596. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704703_12
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DOI: https://doi.org/10.1007/10704703_12
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