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"GrabCut": interactive foreground extraction using iterated graph cuts
TLDR
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. Expand
TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation
TLDR
A new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently, is proposed, which is used for automatic visual recognition and semantic segmentation of photographs. Expand
Fast cost-volume filtering for visual correspondence and beyond
TLDR
This paper proposes a generic and simple framework comprising three steps: constructing a cost volume, fast cost volume filtering and winner-take-all label selection, and achieves state-of-the-art results that achieve disparity maps in real-time, and optical flow fields with very fine structures as well as large displacements. Expand
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context
TLDR
A new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently, which gives competitive and visually pleasing results for objects that are highly textured, highly structured, and even articulated. Expand
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
TLDR
A set of energy minimization benchmarks are described and used to compare the solution quality and runtime of several common energy minimizations algorithms and a general-purpose software interface is provided that allows vision researchers to easily switch between optimization methods. Expand
Learning 6D Object Pose Estimation Using 3D Object Coordinates
TLDR
This work addresses the problem of estimating the 6D Pose of specific objects from a single RGB-D image by presenting a learned, intermediate representation in form of a dense 3D object coordinate labelling paired with a dense class labelling. Expand
Bayesian color constancy revisited
TLDR
This paper introduces a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation, which shows that automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Expand
Optimizing Binary MRFs via Extended Roof Duality
TLDR
An efficient implementation of the "probing" technique is discussed, which simplifies the MRF while preserving the global optimum, and a new technique which takes an arbitrary input labeling and tries to improve its energy is presented. Expand
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
TLDR
It is demonstrated that this generative model for cosegmentation has the potential to improve a wide range of research: Object driven image retrieval, video tracking and segmentation, and interactive image editing. Expand
"GrabCut": interactive foreground extraction using iterated graph cuts
TLDR
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. Expand
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