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2011
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Particle Filter with State Permutations for Solving Image Jigsaw Puzzles

8 years 2 months ago
Particle Filter with State Permutations for Solving Image Jigsaw Puzzles
We deal with an image jigsaw puzzle problem, which is defined as reconstructing an image from a set of square and non-overlapping image patches. It is known that a general instance of this problem is NP-complete, and it is also challenging for humans, since in the considered setting the original image is not given. Recently a graphical model has been proposed to solve this and related problems. The target label probability function is then maximized using loopy belief propagation. We also formulate the problem as maximizing a label probability function and use exactly the same pairwise potentials. Our main contribution is a novel inference approach in the sampling framework of Particle Filter (PF). Usually in the PF framework it is assumed that the observations arrive sequentially, e.g., the observations are naturally ordered by their time stamps in the tracking scenario. Based on this assumption, the posterior density over the corresponding hidden states is estimated. In the jigsaw ...
Xingwei Yang, Nagesh Adluru, LonginJan Latecki
Added 05 Apr 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Xingwei Yang, Nagesh Adluru, LonginJan Latecki
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