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Deqing Sun (Project leader)
Alumni
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Jonas Wulff
Ph.D. Student
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Michael Black
Director
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Erik Sudderth
Brown University
3 results

2013


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A fully-connected layered model of foreground and background flow

Sun, D., Wulff, J., Sudderth, E., Pfister, H., Black, M.

In IEEE Conf. on Computer Vision and Pattern Recognition, (CVPR 2013), pages: 2451-2458, Portland, OR, June 2013 (inproceedings)

Abstract
Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another. Traditional layered motion methods, however, employ fairly weak models of scene structure, relying on locally connected Ising/Potts models which have limited ability to capture long-range correlations in natural scenes. To address this, we formulate a fully-connected layered model that enables global reasoning about the complicated segmentations of real objects. Optimization with fully-connected graphical models is challenging, and our inference algorithm leverages recent work on efficient mean field updates for fully-connected conditional random fields. These methods can be implemented efficiently using high-dimensional Gaussian filtering. We combine these ideas with a layered flow model, and find that the long-range connections greatly improve segmentation into figure-ground layers when compared with locally connected MRF models. Experiments on several benchmark datasets show that the method can recover fine structures and large occlusion regions, with good flow accuracy and much lower computational cost than previous locally-connected layered models.

pdf Supplemental Material Project Page [BibTex]

2013

pdf Supplemental Material Project Page [BibTex]

2012


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Layered segmentation and optical flow estimation over time

Sun, D., Sudderth, E., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 1768-1775, IEEE, 2012 (inproceedings)

Abstract
Layered models provide a compelling approach for estimating image motion and segmenting moving scenes. Previous methods, however, have failed to capture the structure of complex scenes, provide precise object boundaries, effectively estimate the number of layers in a scene, or robustly determine the depth order of the layers. Furthermore, previous methods have focused on optical flow between pairs of frames rather than longer sequences. We show that image sequences with more frames are needed to resolve ambiguities in depth ordering at occlusion boundaries; temporal layer constancy makes this feasible. Our generative model of image sequences is rich but difficult to optimize with traditional gradient descent methods. We propose a novel discrete approximation of the continuous objective in terms of a sequence of depth-ordered MRFs and extend graph-cut optimization methods with new “moves” that make joint layer segmentation and motion estimation feasible. Our optimizer, which mixes discrete and continuous optimization, automatically determines the number of layers and reasons about their depth ordering. We demonstrate the value of layered models, our optimization strategy, and the use of more than two frames on both the Middlebury optical flow benchmark and the MIT layer segmentation benchmark.

pdf sup mat poster Project Page [BibTex]

2012

pdf sup mat poster Project Page [BibTex]

2010


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Layered image motion with explicit occlusions, temporal consistency, and depth ordering

Sun, D., Sudderth, E., Black, M. J.

In Advances in Neural Information Processing Systems 23 (NIPS), pages: 2226-2234, MIT Press, 2010 (inproceedings)

Abstract
Layered models are a powerful way of describing natural scenes containing smooth surfaces that may overlap and occlude each other. For image motion estimation, such models have a long history but have not achieved the wide use or accuracy of non-layered methods. We present a new probabilistic model of optical flow in layers that addresses many of the shortcomings of previous approaches. In particular, we define a probabilistic graphical model that explicitly captures: 1) occlusions and disocclusions; 2) depth ordering of the layers; 3) temporal consistency of the layer segmentation. Additionally the optical flow in each layer is modeled by a combination of a parametric model and a smooth deviation based on an MRF with a robust spatial prior; the resulting model allows roughness in layers. Finally, a key contribution is the formulation of the layers using an image dependent hidden field prior based on recent models for static scene segmentation. The method achieves state-of-the-art results on the Middlebury benchmark and produces meaningful scene segmentations as well as detected occlusion regions.

main paper supplemental material paper and supplemental material in one pdf file Project Page [BibTex]

2010