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Issue No.01 - Jan. (2013 vol.35)
pp: 118-129
M. Felsberg , Dept. of Electr. Eng., Linkoping Univ., Linkoping, Sweden
F. Larsson , Dept. of Electr. Eng., Linkoping Univ., Linkoping, Sweden
J. Wiklund , Dept. of Electr. Eng., Linkoping Univ., Linkoping, Sweden
N. Wadstromer , Div. of Inf. Syst., FOI Swedish Defence Res. Agency, Linkoping, Sweden
J. Ahlberg , Termisk Systemteknik AB, Linkoping, Sweden
We propose a novel method for iterative learning of point correspondences between image sequences. Points moving on surfaces in 3D space are projected into two images. Given a point in either view, the considered problem is to determine the corresponding location in the other view. The geometry and distortions of the projections are unknown, as is the shape of the surface. Given several pairs of point sets but no access to the 3D scene, correspondence mappings can be found by excessive global optimization or by the fundamental matrix if a perspective projective model is assumed. However, an iterative solution on sequences of point-set pairs with general imaging geometry is preferable. We derive such a method that optimizes the mapping based on Neyman's chi-square divergence between the densities representing the uncertainties of the estimated and the actual locations. The densities are represented as channel vectors computed with a basis function approach. The mapping between these vectors is updated with each new pair of images such that fast convergence and high accuracy are achieved. The resulting algorithm runs in real time and is superior to state-of-the-art methods in terms of convergence and accuracy in a number of experiments.
Vectors, Cameras, Estimation, Geometry, Channel estimation, Three dimensional displays, Accuracy,surveillance, Online learning, correspondence problem, channel representation, computer vision
M. Felsberg, F. Larsson, J. Wiklund, N. Wadstromer, J. Ahlberg, "Online Learning of Correspondences between Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 1, pp. 118-129, Jan. 2013, doi:10.1109/TPAMI.2012.65
[1] R.P.S. Mahler, "Multitarget Bayes Filtering via First-Order Multitarget Moments," IEEE Trans. Aerospace and Electronic Systems, vol. 39, no. 4, pp. 1152-1178, Oct. 2003.
[2] G.H. Granlund, "An Associative Perception-Action Structure Using a Localized Space Variant Information Representation," Proc. Algebraic Frames for the Perception-Action Cycle, 2000.
[3] R.I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, second ed. Cambridge Univ. Press, 2004.
[4] B. Micusik and T. Pajdla, "Structure from Motion with Wide Circular Field of View Cameras," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 7, pp. 1135-1149, July 2006.
[5] D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[6] A. Rangarajan, H. Chui, and E. Mjolsness, "A New Distance Measure for Non-Rigid Image Matching," Proc. Second Int'l Workshop Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 734-734, 1999.
[7] E. Jonsson and M. Felsberg, "Correspondence-Free Associative Learning," Proc. 18th Int'l Conf. Pattern Recognition, 2006.
[8] E. Jonsson, "Channel-Coded Feature Maps for Computer Vision and Machine Learning," PhD thesis, Linköping Univ., Sweden, 2008.
[9] L. Bottou, "On-Line Learning and Stochastic Approximations," On-Line Learning in Neural Networks, Cambridge Univ. Press, 1998.
[10] D. Saad, "Introduction," On-Line Learning in Neural Networks, Cambridge Univ. Press, 1998.
[11] D.H. Grollman, "Teaching Old Dogs New Tricks: Incremental Multimap Regression for Interactive Robot Learning from Demonstration," PhD dissertation, Brown Univ., May 2010.
[12] D.H. Grollman and O.C. Jenkins, "Incremental Learning of Subtasks from Unsegmented Demonstration," Proc. IEEE/RSJ Int'l Conf. Intelligent Robots and Systems, Oct. 2010.
[13] D.H. Grollman and O.C. Jenkins, "Sparse Incremental Learning for Interactive Robot Control Policy Estimation," Proc. IEEE Int'l Conf. Robotics and Automation, pp. 3315-3320, May 2008.
[14] L. Csató and M. Opper, "Sparse On-Line Gaussian Processes," Neural Computation, vol. 14, no. 3, pp. 641-668, 2002.
[15] S. Schaal, C.G. Atkeson, and S. Vijayakumar, "Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning," Applied Intelligence, vol. 17, pp. 49-60, 2002.
[16] F. Larsson, E. Jonsson, and M. Felsberg, "Simultaneously Learning to Recognize and Control a Low-Cost Robotic Arm," Image and Vision Computing, vol. 27, no. 11, pp. 1729-1739, 2009.
[17] O. Javed, K. Shafique, Z. Rasheed, and M. Shah, "Modeling Inter-Camera Space-Time and Appearance Relationships for Tracking across Non-Overlapping Views," Computer Vision and Image Understanding, vol. 109, no. 2, pp. 146-162, 2008.
[18] A. Gilbert and R. Bowden, "Incremental, Scalable Tracking of Objects Inter Camera," Computer Vision and Image Understanding, vol. 111, pp. 43-58, July 2008.
[19] S. Khan and M. Shah, "Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1355-1360, Oct. 2003.
[20] L. Lee, R. Romano, and G. Stein, "Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 758-767, Aug. 2000.
[21] H.P. Snippe and J.J. Koenderink, "Discrimination Thresholds for Channel-Coded Systems," Biological Cybernetics, vol. 66, pp. 543-551, 1992.
[22] A. Pouget, P. Dayan, and R.S. Zemel, "Inference and Computation with Population Codes," Ann. Rev. Neuroscience, vol. 26, pp. 381-410, 2003.
[23] R.S. Zemel, P. Dayan, and A. Pouget, "Probabilistic Interpretation of Population Codes," Neural Computation, vol. 10, no. 2, pp. 403-430, 1998.
[24] M. Felsberg, P.-E. Forssén, and H. Scharr, "Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 2, pp. 209-222, Feb. 2006.
[25] M. Kass and J. Solomon, "Smoothed Local Histogram Filters," ACM SIGGRAPH Papers, Article 100, 2010, .
[26] P.-E. Forssén and G. Granlund, "Robust Multi-Scale Extraction of Blob Features," Proc. 13th Scandinavian Conf. Image Analysis, 2003.
[27] C.M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
[28] B. Johansson, T. Elfving, V. Kozlov, Y. Censor, P.-E. Forssén, and G. Granlund, "The Application of An Oblique-Projected Landweber Method to a Model of Supervised Learning," Math. and Computer Modelling, vol. 43, pp. 892-909, 2006.
[29] S.S. Haykin, Neural Networks: A Comprehensive Foundation. Prentice Hall, 1999.
[30] S. Wegenkittl, "Generalized Phi-Divergence and Frequency Analysis in Markov Chains," PhD dissertation, Univ. of Salzburg, 1998.
[31] Y.-D. Kim, A. Cichocki, and S. Choi, "Nonnegative Tucker Decomposition with Alpha-Divergence," Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, 2008.
[32] A. Cichocki, A.-H. Phan, and C. Caiafa, "Flexible HALS Algorithms for Sparse Non-Negative Matrix/Tensor Factorization," Proc. IEEE Int'l Workshop Machine Learning for Signal Processing, 2008.
[33] R. Jiménz and Y. Shao, "On Robustness and Efficiency of Minimum Divergence Estimators," TEST, vol. 10, no. 2, pp. 241-248, 12 2001.
[34] M. Felsberg and F. Larsson, "Learning Higher-Order Markov Models for Object Tracking in Image Sequences," Proc. Fifth Int'l Symp. Advances in Visual Computing, pp. 184-195, 2009.
[35] S. Vijayakumar and S. Schaal, "Locally Weighted Projection Regression: An o(n) Algorithm for Incremental Real Time Learning in High Dimensional Space," Proc. 17th Int'l Conf. Machine Learning, pp. 1079-1086, 2000.
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