Share This Author
Region filling and object removal by exemplar-based image inpainting
- A. Criminisi, P. Pérez, K. Toyama
- Computer ScienceIEEE Transactions on Image Processing
- 1 September 2004
The simultaneous propagation of texture and structure information is achieved by a single, efficient algorithm that combines the advantages of two approaches: exemplar-based texture synthesis and block-based sampling process.
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
The set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences are reported, finding that different algorithms worked best for different sub-regions, but that no single algorithm ranked in the top for all sub-Regions simultaneously.
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.
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
- J. Shotton, Ben Glocker, C. Zach, S. Izadi, A. Criminisi, A. Fitzgibbon
- Environmental Science, Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 1 June 2013
We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image. Our approach employs a regression forest that is capable of inferring…
Object categorization by learned universal visual dictionary
- J. Winn, A. Criminisi, T. Minka
- Computer ScienceTenth IEEE International Conference on Computer…
- 17 October 2005
An optimally compact visual dictionary is learned by pair-wise merging of visual words from an initially large dictionary, and a novel statistical measure of discrimination is proposed which is optimized by each merge operation.
Object removal by exemplar-based inpainting
- A. Criminisi, P. Pérez, K. Toyama
- Computer ScienceIEEE Computer Society Conference on Computer…
- 18 June 2003
A best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting, which demonstrates the effectiveness of the algorithm in removing large occluding objects as well as thin scratches.
Geodesic star convexity for interactive image segmentation
- Varun Gulshan, C. Rother, A. Criminisi, A. Blake, Andrew Zisserman
- Computer Science, MathematicsIEEE Computer Society Conference on Computer…
- 13 June 2010
A new shape constraint for interactive image segmentation is introduced, an extension of Veksler's star-convexity prior, in two ways: from a single star to multiple stars and from Euclidean rays to Geodesic paths.
Deep Neural Decision Forests
- P. Kontschieder, M. Fiterau, A. Criminisi, S. R. Bulò
- Computer ScienceIEEE International Conference on Computer Vision…
- 7 December 2015
A novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner by introducing a stochastic and differentiable decision tree model.
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning
- A. Criminisi, J. Shotton, E. Konukoglu
- Computer ScienceFound. Trends Comput. Graph. Vis.
- 14 March 2012
A unified, efficient model of random decision forests which can be applied to a number of machine learning, computer vision, and medical image analysis tasks is presented and relative advantages and disadvantages discussed.