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Nonparametric Multiscale Energy-Based Model and Its Application in Some Imagery Problems
February 2004 (vol. 26 no. 2)
pp. 184-197
Abstract—This paper investigates the use of a nonparametric regularization energy term for devising a example-based rendering and segmentation technique. We have stated this problem in the multiresolution energy minimization framework and exploited the multiscale structure proposed by Wei and Levoy for the texture synthesis problem. In this nonparametric energy minimization framework, we also propose a computationally efficient coarse-to-fine recursive optimization method to minimize the cost function related to this hierarchical model. In this context, the formulation of our example-based regularization term also allows to directly infer an intuitive dissimilarity measure between two contour shapes. This measure is herein exploited to define an efficient shape descriptor for the contour-based shape recognition and indexing problem.
[1] 184 S. Geman and D. Geman, Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, 1984.[2] F. Heitz, P. Pérez, and P. Bouthemy, Multiscale Minimisation of Global Energy Functions in Some Visual Recovery Problems CVGIP: Image Understanding, vol. 59, no. 1, pp. 125-134, 1994.[3] E. Memin and P. Pérez, Dense Estimation and Object-Based Segmentation of the Optical Flow with Robust Techniques IEEE Trans. Image Processing, vol. 7, no. 5, pp. 703-719, 1998.[4] M. Mignotte, C. Collet, P. Pérez, and P. Bouthemy, Bayesian Inference and Optimization Strategies for Some Detection and Classification Problems in Sonar Imagery Proc. SPIE Conf. Non Linear Image Processing, Electronic Imaging, pp 14-27, Jan. 1999.[5] J. Besag, On the Statistical Analysis of Dirty Pictures J. Royal Statistical Soc., B, vol. 48, pp. 259-302, 1986.[6] C. Kervrann and F. Heitz, Statistical Model-Based Segmentation of Deformable Motion Proc. Int'l Conf. Image Processing, pp. 937-940, 1996.[7] P.B. Chou and C.M. Brown, The Theory and Practice of Bayesian Image Labeling Int'l J. Computer Vision, vol. 4, pp. 185-210, 1990.[8] A. Efros and T. Leung, “Texture Synthesis by Non-Parametric Sampling,” Proc. Seventh Int'l Conf. Computer Vision, 1999.[9] C. Regazzoni, F. Arduini, and G. Vernazza, A Multilevel GMRF-Based Approach to Image Segmentation and Restoration Signal Processing, vol. 34, pp. 43-67, 1993.[10] S. Krishnamachari and R. Chellappa, “Multiresolution Gauss-Markov Random Field Models for Texture Segmentation,” IEEE Trans. Image Processing, vol. 6, no. 2, pp. 251-267, 1997.[11] L. Wang and J. Liu, Texture Classification Using Multiresolution Pattern Recognition Letters, vol. 20, no. 2, pp. 171-182, 1999.[12] M. Mignotte, C. Collet, P. Perez, and P. Bouthemy, “Sonar Image Segmentation Using an Unsupervised Hierarchical MRF Model,” IEEE Tran. Image Processing, vol. 9, pp. 1216-1231, 2000.[13] L.-Y. Wei and M. Levoy, Fast Texture Synthesis Using Tree-Structured Vector Quantization Proc. SIGGRAPH, pp. 479-488, July 2000.[14] K. Popat and R.W. Picard, Cluster-Based Probability Model and Its Application to Image and Texture Processing IEEE Trans. Image Processing, vol. 6, no. 2, pp. 268-284, 1997.[15] H. Chen, Y.Q. Xu, H.Y. Shum, S.C. Zhu, and N.N. Zhen, Example-Based Facial Sketch Generation with Non-Parametric Sampling Proc. Int'l Conf. Computer Vision, pp. 433-438, July 2001.[16] A. Hertzmann, N. Oliver, B. Curless, and S.M. Seitz, Curve Analogies Proc. Eurographics Workshop Rendering, pp. 233-245, June 2002.[17] P. M. Jodoin, E. Epstein, M. Granger-Piché, and V. Ostromoukhov, Hatching by Example: A Statistical Approach Proc. Non-Photorealistic Animation and Rendering, June 2002.[18] M. Mignotte, C. Collet, P. Pérez, and P. Bouthemy, Unsupervised Hierarchical Markovian Segmentation of Sonar Images Proc. IEEE Int'l Conf. Image Processing, Oct. 1997.[19] T.F. Chan et al. Image Inpainting http://www.math.ucla.edu/ ~imagers/htmls inp.html, 2003.[20] C. Reynolds, Stylized Depiction in Computer Graphics, NPR (Non Photo-Realistic Rendering), Painterly and Toon Rendering http://www.red3d.com/cwrnpr/, 2003.[21] A. Hertzmann, C.E. Jacobs, N. Olivier, B. Curless, and D.H. Salesin, Image Analogies Proc. SIGGRAPH, pp. 327-340, July 2001.[22] S. Arya, D.M. Mount, N.S. Netanyahu, R. Silverman, and A.Y. Wu, An Optimal Algorithm for Aproximate Nearest Neighbor Searching in Fixed Dimensions J. ACM, vol. 45, no. 6, pp. 891-923, 1998.[23] R.C. Veltkamp and M. Hagedoorn, State of the Art in Shape Matching Technical Report UU-CS-1999-27, Utrecht, 1999.[24] A.K. Jain, Y. Zhong, and S. Lakshmanan, Object Matching Using Deformable Templates IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 3, pp. 267-278, Mar. 1996.[25] M. Mignotte, C. Collet, P. Pérez, and P. Bouthemy, Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of Shadow Shapes in Sonar Imagery IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 2, pp. 129-141, Feb. 2000.[26] M. Mignotte, A New And Simple Shape Descriptor Based on a Non-Parametric Multiscale Model Proc. IEEE Int'l Conf. Image Processing, vol. I, pp. 445-448, Sept. 2002.[27] P.F.M. Nacken, Chamfer Metrics in Mathematical Morphology J. Math. Imaging and Vision, vol. 4, pp. 233-253, 1994.[28] D. Gavrila and V. Philomin, Real-Time Object Detection for Smart Vehicles Proc. Seventh IEEE Int'l Conf. Computer Vision, pp. 87-93, 1999.[29] M. Mignotte, Bayesian Rendering with Non-Parametric Multiscale Prior Model Proc. IEEE Int'l Conf. Pattern Recognition, vol. 1, pp. 247-250, Aug. 2002.[30] A.P. Dempster, N.M. Laird, and D.B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm Royal Statistical Soc., pp. 1-38, 1976.[31] B. Braathen, P. Masson, and W. Pieczynski, Global and Local Methods of Unsupervised Bayesian Segmentation of Images Graphics and Vision, vol. 2, no. 1, pp. 39-52, 1993.[32] J.G. Snodgrass and M. Verderwart, A Standardized Set of 260 Pictures: Norms for Name Agreement, Familiarity and Visual Complexity J. Experimental Psychology: Human Learning and Memory, vol. 6, pp. 174-215, 1980. http://www.cog.brown.edu/~tarrstimuli.html .[33] J. Canny, A Computational Approach Edge Detection IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-697, 1986.[34] R.C. Gonzales and R.W. Woods, Digital Image Processing. Addison-Wesley, 1992.[35] G. Mori, S. Belongie, and J. Malik, Shape Contexts Enable Efficient Retrieval of Similar Shapes Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 723-730, Dec. 2001.
Index Terms:
Nonparametric multiscale energy-based (or multiresolution example-based) model, inpainting, Non-Photorealistic Rendering (NPR), segmentation, contour-based shape recognition, shape indexing.
Citation:
Max Mignotte, "Nonparametric Multiscale Energy-Based Model and Its Application in Some Imagery Problems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 184-197, Jan. 2004, doi:10.1109/TPAMI.2004.1262180
