Share This Author
"GrabCut": interactive foreground extraction using iterated graph cuts
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.
TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation
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.
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context
- J. Shotton, J. Winn, C. Rother, A. Criminisi
- Computer ScienceInternational Journal of Computer Vision
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.
Fast cost-volume filtering for visual correspondence and beyond
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.
Learning 6D Object Pose Estimation Using 3D Object Coordinates
- Eric Brachmann, Alexander Krull, Frank Michel, S. Gumhold, J. Shotton, C. Rother
- Computer ScienceECCV
- 6 September 2014
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.
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
- R. Szeliski, R. Zabih, C. Rother
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 June 2008
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.
- Alexander Kirillov, Kaiming He, Ross B. Girshick, C. Rother, Piotr Dollár
- Computer ScienceIEEE/CVF Conference on Computer Vision and…
- 3 January 2018
A novel panoptic quality (PQ) metric is proposed that captures performance for all classes (stuff and things) in an interpretable and unified manner and is performed a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task.
Bayesian color constancy revisited
- P. Gehler, C. Rother, A. Blake, T. Minka, T. Sharp
- Mathematics, Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 23 June 2008
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.
Optimizing Binary MRFs via Extended Roof Duality
- C. Rother, V. Kolmogorov, V. Lempitsky, M. Szummer
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 17 June 2007
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.
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
- C. Rother, T. Minka, A. Blake, V. Kolmogorov
- Computer ScienceIEEE Computer Society Conference on Computer…
- 17 June 2006
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.