E Microsoft Research Asia Xian Jiaotong University Univ of California SC No49 Zhichun Road Xian China 710049 Santa Cruz CA 95064 Beijing China 100080 sj nnzheng aiarxjtueducn taosoeucscedu hshummicrosoftcom Abstract In this paper we propose a Bayesia ID: 26627 Download Pdf
E Microsoft Research Asia Xian Jiaotong University Univ of California SC No49 Zhichun Road Xian China 710049 Santa Cruz CA 95064 Beijing China 100080 sj nnzheng aiarxjtueducn taosoeucscedu hshummicrosoftcom Abstract In this paper we propose a Bayesia
R China Beijing PR China Abstract We study visual attention by detecting a salient object in an input image We formulate salient object detec tion as an image segmentation problem where we separate the salient object from the image background We prop
In b only 2 yellow lines are drawn to indicate the foreground and another blue line to indicate the background All these lines are far away from th etrue object boundary In c an accurate boundary can be obtained by simply clicking and dragging a few
R China jiansun weiweiz xitang and hshum microsoftcom Abstract In this paper we introduce background cut a high quality and real time foreground layer extraction algorithm From a single video sequence with a moving foreground object and stationary
Dilip. . Krishnan. Depth Qualifying Examination Presentation . Sep 13, 2010. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. A. A. Overview of Talk.
We 64257rst conduct a theoretical study of the descriptive Markov random 64257eld model and the gen erative waveletsparse coding model from the perspective of entropy and complexity The competition between the two types of models de64257nes the conc
The dark channel prior is a kind of s tatistics of the hazefree outdoor images It is based on a key observation most local patches in hazefree outdoor images contain some pixels which have very low intensities in at least one color channel Using th
Freeman Massachusetts Institute of Technology Computer Science a nd Arti64257cial Intelligence Laboratory 1 Introduction This document provides a detailed description of the deconv olu tion algorithms used in the paper A Levin R Fergus F Dur and and
ntuagr Alan Yuille Department of Statistics and Psychology University of California at Los Angeles yuillestatuclaedu Abstract A combination of techniques that is becoming increasingly popular is the construction of partbased object represen tations u
Black Department of Computer Science Brown University Providence RI USA rothblack csbrownedu Abstract We develop a framework for learning generic expressive image priors that capture the statistics of natural scenes and can be used for a variety of
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E Microsoft Research Asia Xian Jiaotong University Univ of California SC No49 Zhichun Road Xian China 710049 Santa Cruz CA 95064 Beijing China 100080 sj nnzheng aiarxjtueducn taosoeucscedu hshummicrosoftcom Abstract In this paper we propose a Bayesia
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