From surfaces to objects: computer vision and three dimensional scene analysis
From surfaces to objects: computer vision and three dimensional scene analysis
Segmentation of range images as the search for geometric parametric models
International Journal of Computer Vision
Perceptual organization in computer vision: status, challenges, and potential
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Surface-normal estimation with neighborhood reorganization for 3D reconstruction
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Contour grouping and abstraction using simple part models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Scene parsing using a prior world model
International Journal of Robotics Research
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Object segmentation of unknown objects with arbitrary shape in cluttered scenes is an ambitious goal in computer vision and became a great impulse with the introduction of cheap and powerful RGB-D sensors. We introduce a framework for segmenting RGB-D images where data is processed in a hierarchical fashion. After pre-clustering on pixel level parametric surface patches are estimated. Different relations between patch-pairs are calculated, which we derive from perceptual grouping principles, and support vector machine classification is employed to learn Perceptual Grouping. Finally, we show that object hypotheses generation with Graph-Cut finds a globally optimal solution and prevents wrong grouping. Our framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. We also tackle the problem of segmenting objects when they are partially occluded. The work is evaluated on publicly available object segmentation databases and also compared with state-of-the-art work of object segmentation.