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IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
On representation and matching of multi-coloured objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Fast Radial Symmetry for Detecting Points of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Multi-View Stereo via Volumetric Graph-Cuts
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Diffusion Distance for Histogram Comparison
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Solving Markov Random Fields using Second Order Cone Programming Relaxations
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Fast Active Appearance Model Search Using Canonical Correlation Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A Linear Programming Approach to Max-Sum Problem: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate Labeling via Graph Cuts Based on Linear Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graphical model framework for coupling MRFs and deformable models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Detecting symmetry and symmetric constellations of features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Evaluation of fast 2d and 3d medical image retrieval approaches based on image miniatures
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Image segmentation or registration approaches that rely on a local search paradigm (e.g, Active Appearance Models, Active Contours) require an initialization that provides for considerable overlap or a coarse localization of the object to be segmented or localized. In this paper we propose an approach that does not need such an initialization, but localizes anatomical structures in a global manner by formulating the localization task as the solution of a Markov Random Field (MRF). During search Sparse MRF Appearance Models (SAMs) relate a priori information about the geometric configuration of landmarks and local appearance features to a set of candidate points in the target image. They encode the correspondence probabilities as an MRF, and the search in the target image is equivalent to solving the MRF. The resulting node labels define a mapping of the modeled object (e.g. a sequence of vertebrae) to the target image interest points. The local appearance information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow (GVF). Alternatively, arbitrary interest points can be used. Experimental results are reported for two data-sets showing the applicability to complex medical data. The approach does not require initialization and finds the most plausible match of the query structure in the entire image. It provides for precise, reliable and fast localization of the structure.