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CVPR

2011

IEEE

2011

IEEE

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 ...

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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