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Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
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Gradient domain high dynamic range compression
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
IEEE Computer Graphics and Applications
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2003 Papers
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Interactive digital photomontage
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An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Background Estimation as a Labeling Problem
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Automatic HDRI generation of dynamic environments
SIGGRAPH '05 ACM SIGGRAPH 2005 Sketches
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Image and depth from a conventional camera with a coded aperture
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Extracting the essence from sets of images
Computational Aesthetics'07 Proceedings of the Third Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
ACM SIGGRAPH 2010 papers
Background estimation using graph cuts and inpainting
Proceedings of Graphics Interface 2010
Fast high dynamic range image deghosting for arbitrary scene motion
Proceedings of Graphics Interface 2012
How Not to Be Seen — Object Removal from Videos of Crowded Scenes
Computer Graphics Forum
Automatic noise modeling for ghost-free HDR reconstruction
ACM Transactions on Graphics (TOG)
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We address the problem of reconstructing the background of a scene from a set of photographs featuring several occluding objects. We assume that the photographs are obtained from the same viewpoint and under similar illumination conditions. Our approach is to define the background as a composite of the input photographs. Each possible composite is assigned a cost, and the resulting cost function is minimized. We penalize deviations from the following two model assumptions: background objects are stationary, and background objects are more likely to appear across the photographs. We approximate object stationariness using a motion boundary consistency term, and object likelihood using probability density estimates. The penalties are combined using an entropy-based weighting function. Furthermore, we constraint the solution space in order to avoid composites that cut through objects. The cost function is minimized using graph cuts, and the final result is composed using gradient domain fusion. We demonstrate the application of our method to the recovering of clean, unoccluded shots of crowded public places, as well as to the removal of ghosting artifacts in the reconstruction of high dynamic range images from multi-exposure sequences. Our contribution is the definition of an automatic method for consistent background estimation from multiple exposures featuring occluders, and its application to the problem of ghost removal in high dynamic range image reconstruction.