Patent
Image segmentation using spatial-color gaussian mixture models
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TLDR
In this paper, a spatial-color Gaussian mixture model (SCGMM) image segmentation technique is proposed for segmenting images, which specifies foreground objects in the first frame of an image sequence.Abstract:
A spatial-color Gaussian mixture model (SCGMM) image segmentation technique for segmenting images. The SCGMM image segmentation technique specifies foreground objects in the first frame of an image sequence, either manually or automatically. From the initial segmentation, the SCGMM segmentation system learns two spatial-color Gaussian mixture models (SCGMM) for the foreground and background objects. These models are built into a first-order Markov random field (MRF) energy function. The minimization of the energy function leads to a binary segmentation of the images in the image sequence, which can be solved efficiently using a conventional graph cut procedure.read more
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References
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Proceedings ArticleDOI
Adaptive background mixture models for real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Journal ArticleDOI
Fast approximate energy minimization via graph cuts
TL;DR: This work presents two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves, namely expansion moves and swap moves that allow important cases of discontinuity preserving energies.
Journal ArticleDOI
"GrabCut": interactive foreground extraction using iterated graph cuts
TL;DR: A more powerful, iterative version of the optimisation of the graph-cut approach is developed and the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result.
Journal ArticleDOI
Pfinder: real-time tracking of the human body
TL;DR: Pfinder is a real-time system for tracking people and interpreting their behavior that uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions.
Proceedings ArticleDOI
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
Yuri Boykov,Marie-Pierre Jolly +1 more
TL;DR: In this paper, the user marks certain pixels as "object" or "background" to provide hard constraints for segmentation, and additional soft constraints incorporate both boundary and region information.