Stationary background generation: an alternative to the difference of two images
Pattern Recognition
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video matting of complex scenes
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Photographic tone reproduction for digital images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
Fragment-based image completion
ACM SIGGRAPH 2003 Papers
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive digital photomontage
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Iterative Optimization Approach for Unified Image Segmentation and Matting
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Background Estimation as a Labeling Problem
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Exemplar-based background model initialization
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Background Initialization in Cluttered Sequences
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Background estimation from non-time sequence images
GI '08 Proceedings of graphics interface 2008
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast high dynamic range image deghosting for arbitrary scene motion
Proceedings of Graphics Interface 2012
Synthetic aperture imaging using pixel labeling via energy minimization
Pattern Recognition
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In this paper, we propose a new method, which requires no interactive operation, to estimate background from an image sequence with occluding objects. The images are taken from the same viewpoint under similar illumination conditions. Our method combines the information from input images by selecting the appropriate pixels to construct the background. We have two simple assumptions for the input image sequence: each background pixel has to be disclosed at least once and some parts of the background are never occluded. We propose a cost function that includes a data term and a smoothness term. A unique feature of our data term is that it has not only the stationary term, but also a new predicted term obtained using an image inpainting technique. The smoothness term guarantees that the output is visually smooth so that there is no need for post-processing. The cost is minimized by applying graph cuts optimization. We apply our algorithm to several complex natural scenes as well as to an image sequence with different camera exposure settings, and the results are encouraging.