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Component Optimization for Image Understanding: A Bayesian Approach
May 2006 (vol. 28 no. 5)
pp. 684-693
In this paper, the optimizations of three fundamental components of image understanding: segmentation/annotation, 3D sensing (stereo) and 3D fitting, are posed and integrated within a Bayesian framework. This approach benefits from recent advances in statistical learning which have resulted in greatly improved flexibility and robustness. The first two components produce annotation (region labeling) and depth maps for the input images, while the third module integrates and resolves the inconsistencies between region labels and depth maps to fit most likely 3D models. To illustrate the application of these ideas, we have focused on the difficult problem of fitting individual tree models to tree stands which is a major challenge for vision-based forestry inventory systems.

[1] J. Besag, “On the Statistical Analysis of Dirty Pictures,” J. Royal Statistical Soc. B, vol. 23, pp. 259-302, 1986.
[2] G. Celeux, D. Chauveau, and J. Diebolt, “Stochastic Versions of the EM Algorithm,” J. Statistical Computation and Simulation, vol. 55, pp. 287-314, 1996.
[3] H. Cheng and C.A. Bouman, “Multiscale Bayesian Segmentation Using a Trainable Context Model,” IEEE Trans. Image Processing, vol. 10, no. 2, pp. 460-474, Apr. 2001.
[4] L. Cheng and T. Caelli, “Bayesian Stereo Matching,” Computer Vision and Image Understanding, in press.
[5] L. Cheng, T. Caelli, and V. Ochoa, “A Trainable Hierarchical Hidden Markov Tree Model for Color Image Annotation,” Proc. Int'l Conf. Pattern Reconition, 2002.
[6] A. Gelman, J. Carlin, H. Stern, and D. Rubin, Bayesian Data Analysis. London: Chapman and Hall, 1997.
[7] W. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice. London: Chapman and Hall, 1996.
[8] M. Gillis and D. Leckie, “Forest Inventory Update in Canada,” The Forestry Chronicle, vol. 72, no. 2, pp. 138-156, 1996.
[9] F. Gougeon, “A Crown-Following Approach to the Automatic Delineation of Individual Tree Crowns in High Spatial Resolution Aerial Images,” Canadian J. Remote Sensing, vol. 21, pp. 274-284, 1995.
[10] R. Hummel and S. Zucker, “On the Foundations of Relaxation Labeling Processes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 5, no. 3, pp. 267-287, 1983.
[11] M. Larsen and M. Rudemo, “Optimizing Templates for Finding Trees in Aerial Photographs,” Pattern Recognition Letters, vol. 19, pp. 1153-1162, 1998.
[12] S. Nielsen, “The Stochastic EM Algorithm: Estimation and Asymptotic Results,” Bernoulli, vol. 6, pp. 457-489, 2000.
[13] A. Pinz, “A Computer Vision System for the Recognition of Trees in Aerial Photographs,” Multisource Data Integration in Remote Sensing, J.C. Tilton, ed., pp. 111-124, 1991.
[14] R. Pollock, “The Automatic Recognition of Individual Trees in Aerial Images of Forests Based on a Synthetic Tree Crown Image Model,” PhD dissertation, Dept. of Computer Science, Univ. of British Columbia, Vancouver, Canada, 1996.
[15] C. Robert, The Bayesian Choice: from Decision-Theoretic Motivations to Computational Implementation, second ed. New York: Springer-Verlag, 2001.
[16] D. Scharstein and R. Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,” Int'l J. Computer Vision, vol. 47, pp. 7-42, 2002.
[17] J. Sun, N. Zheng, and H. Shum, “Stereo Matching Using Belief Propagation,” IEEE Trans. Pattern Recognition and Machine Intelligence, vol. 25, no. 7, pp. 787-800, July 2003.
[18] A. Gray, Modern Differential Geometry of Curves and Surfaces with Mathematica, second ed. CRC Press, 1997.
[19] J. Yedidia, W. Freeman, and Y. Weiss, “Understanding Belief Propagtation and Its Generalizations,” Exploring Artificial Intelligence in the New Millennium, G. Lakemeyer, ed., pp. 236-239, 2003.

Index Terms:
Segmentation, stereo, 3D fitting, scene analysis, image understanding, forestry inventory.
Li Cheng, Terry Caelli, Arturo Sanchez-Azofeifa, "Component Optimization for Image Understanding: A Bayesian Approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 684-693, May 2006, doi:10.1109/TPAMI.2006.92
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