W. Freeman and E. Pasztor. Learning to estimate scenes from images. In M. Kearns, S. Solla, and D Cohn, editors, Adv. Neural Information Processing Systems, volume 11. MIT Press, 1999.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... . g, g. g, g) To enforce integrability of the gradient fields the fourway potential is set to zero when gi, gj, gu, gI violate the integrability constraint (cf. 3] The graphical model defined by equation 4 has many loops. Nevertheless motivated by the recent results on similar graphs [2, 3] we ran the max product belief propagation algorithm on it. The max product algorithm finds a gradient field gi that Input I Output I1 Output 12 Figure 4: Output of the algorithm on synthetic images. The algorithm effectively searches over an exponentially large number of possible ....

....the two layers. Since equation 4 is completely symmetric in f and g we break the symmetry by requiring that the gradient in a single location gio belong to layer 1. In order to run BP we need to somehow discretize the space of possible gradients at each pixel. Similar to the approach taken in [2] we use the local potentials to sample a small number of candidate gradients at each pixel. Since the local potential penalizes non zero gradients, the most probable candidates are gi = Ix, Iv) and gi = 0, 0) We also added two more candidates at each pixel gi = Ix, 0) and gi = 0, Iy) With ....

W.T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cobh, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

....length of the path traversed from one IPF update site to the next. 8 3 Loopy Extension to IPF BP Recently, a number of groups have shown that BP works surprisingly well in many practical problems where the graphical model involved contains loops [Frey and MacKay, 1997, McEliece et al. 1998, Freeman and Pasztor, 1998, Murphy et al. 1999] Efforts to understand how and why loopy BP works were spear headed by Weiss [Weiss, 2000] but the breakthrough came when Yedidia et al. showed that the fixed points of loopy BP correspond exactly to the stationary points of the Bethe free energy [Yedidia et al. 2000] The ....

Freeman, W. and Pasztor, E. (1998). Learning to estimate scenes from images. In Advances in Neural Information Processing Systems, volume 11.

.... a process that is often laborious and difficult to automate fully. Our aim, therefore, is to develop a paradigm which retains the probabilistic setting while avoiding the use of explicit models to describe target objects. The use of exemplars offers an alternative that can tackle this problem [6, 8, 10, 11, 12]. Exemplar based models can be constructed very directly from training sets, without the need to set up complex intermediate representations, such as parameterized contour models or 3D articulated models. Existing tracking algorithms that use exemplar based models have certain limitations. ....

W. Freeman and E. Pasztor. Learning to estimate scenes from images. In Advances in Neural Information Processing Systems 11. MIT Press, 1999.

.... strategy for approximate inference (Murphy et al. 1999) In particular, it was shown that the celebrated method of turbo decoding is equivalent to loopy BP on an appropriate graphical model (McEliece et al. 1998) Frey and MacKay, 1997) Other applications can be found in image analysis (Freeman and Pasztor, 1998), Frey, 1999) An important drawback of loopy BP is that it can easily fail to converge (e.g. it may get stuck in limit cycles) Important progress in understanding the convergence properties and the quality of the approximation was made in (Weiss, 2000) But the most important breakthrough ....

Freeman, W. and Pasztor, E. (1998). Learning to estimate scenes from images. In NIPS, volume 11.

....unwrapped graph: a graph that has the same local topology as the original MRF but is singly connected. To the best of my knowledge, no similar result can be shown for the MF iterations. I think that the tremendous success of BP in applications such as error correcting codes and image processing [2] stems not only from the use of a more complicated free energy but also from the algorithm it uses to optimize the free energy. Acknowledgements Support by MURI ARO DAAH04 96 1 0341, MURI N00014 00 1 0637 and NSF IIS 9988642 is gratefully acknowledged. I thank P. Dayan, K. Murphy, J. Yedidia and ....

W.T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

....t where distance is measured along the quadtree. However, the optimality is not in the sense of the original probability distribution but rather in terms of the quadtree approximation. In particular, this leads to rather noticeable artifacts along the quadtree boundaries [10] Freeman and Pasztor [2] have recently experimented with combining quadtree propagation with grid propagation and have obtained encouraging results. 5 Discussion The appeal of local, parallel computations as metaphors for early visual computation goes back to the Gestalt psychologists, and was a driving force behind ....

W.T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

.... the same algorithm is not guaranteed to converge in multiply connected networks, and even if it does, it will not calculate the correct beliefs [20] Several groups have recently reported excellent experimental results by running algorithms equivalent to Pearl s algorithm on networks with loops [9, 18, 7]. Perhaps the most dramatic instance of this performance is in an error correcting code scheme known as Turbo codes [3] These codes have been described as the most exciting and potentially important development in coding theory in many years [17] and have recently been shown [13, 16] to ....

W.T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

....be difficult to capture analytically. This motivates a learning based approach: in a training set, learn the fine details that correspond to different image regions seen at a low resolution; then use those learned relationships to predict fine details in other images. For the past several years [5, 6], we have been exploring this approach for enlarging images. To motivate why this approach should work at all, note that a collection of image pixels are special signals which have much less variability than would a corresponding set of completely random variables. Researchers have studied these ....

W. T. Freeman and E. C. Pasztor. Learning to estimate scenes from images. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Adv. Neural Information Processing Systems, volume 11, Cambridge, MA, 1999. MIT Press. See also http://www.merl.com/reports/TR99-05/.

....in [18] investigates the quality of the approximation when it is applied to a particular loopy belief network. Several groups have recently reported excellent experimental results by using this approximation scheme by running algorithms equivalent to Pearl s algorithm on networks with loops [8, 17, 7]. Perhaps the most dramatic instance of this performance is in an error correcting code scheme known as Turbo codes [3] These codes have been described as the most exciting and potentially important development in coding theory in many years [16] and have recently been shown [12, 15] to ....

W.T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

.... the same algorithm is not guaranteed to converge in multiply connected networks, and even if it does, it will not calculate the correct beliefs [20] Several groups have recently reported excellent experimental results by running algorithms equivalent to Pearl s algorithm on networks with loops [9, 18, 7]. Perhaps the most dramatic instance of this performance is in an error correcting code scheme known as Turbo codes [3] These codes have been described as the most exciting and potentially important development in coding theory in many years [17] and have recently been shown [13, 16] to ....

W.T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

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W. Freeman and E. Pasztor. Learning to estimate scenes from images. In M. Kearns, S. Solla, and D Cohn, editors, Adv. Neural Information Processing Systems, volume 11. MIT Press, 1999.

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Freeman, W. T. and E. C. Pasztor: 1999, `Learning to estimate scenes from images'. In: M. S. Kearns, S. A. Solla, and D. A. Cohn #eds.#: Adv. Neural Information Processing Systems, Vol. 11. Cambridge, MA. See also http:##www.merl.com#reports#TR99-05#.

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William T. Freeman and Egon C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

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W. Freeman and E. Pasztor. Learning to estimate scenes from images. In M. Kearns, S. Solla, and D. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

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W. Freeman and E. Pasztor. Learning to estimate scenes from images. Advances in Neural Information Processing Systems (NIPS), 11, December 1998.

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W.T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

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W.T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

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W. Freeman and E. Pasztor. Learning to estimate scenes from images. In M. Kearns, S. Solla, and D. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

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W.T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

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W. T. Freeman and E.C. Pasztor. Learning to estimate scenes from images. In NIPS, volume 11, 1999.

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W. T. Freeman and E. C. Pasztor. Learning to estimate scenes from images. In M. S. Keaarns, S.A. Solla, and D. A. Cohn, editors Adv Neural Information Processing Systems, volume 11, Cambridge, MA, 1999, MIT Press.

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William T. Freeman and Egon C. Pasztor. Learning to estimate scenes from images. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Adv. Neural Information Processing Systems 11. MIT Press, 1999.

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