Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
High-quality video view interpolation using a layered representation
ACM SIGGRAPH 2004 Papers
Depth-of-field-based alpha-matte extraction
APGV '05 Proceedings of the 2nd symposium on Applied perception in graphics and visualization
Recognition of human behavior by space-time silhouette characterization
Pattern Recognition Letters
Improved GrabCut Segmentation via GMM Optimisation
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Image segmentation based on GrabCut framework integrating multiscale nonlinear structure tensor
IEEE Transactions on Image Processing
An O-FDP Framework in 3D Model Based Reconstruction
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
Key components for an advanced segmentation system
IEEE Transactions on Multimedia
Grab-carry-release: manipulating physical objects in a real scene through a smart phone
SIGGRAPH Asia 2011 Emerging Technologies
Automatic objects segmentation with RGB-D cameras
Journal of Visual Communication and Image Representation
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Popular state of the art segmentation methods such as Grab cut include a matting technique to calculate the alpha values for boundaries of segmented regions. Conventional Grabcut relies only on color information to achieve segmentation. Recently, there have been attempts to improve Grabcut using motion in video sequences. However, in stereo or multi-view analysis, there is additional information that could be also used to improve segmentation. Clearly, depth based approaches bear the potential discriminative power of ascertaining whether the object is nearer of farer. In this work, we propose and evaluate a Grabcut segmentation technique based on combination of color and depth information. We show the usefulness of the approach when stereo information is available and evaluate it using standard datasets against state of the art results.