Probabilistic Region Matching in Narrow-Band Endoscopy for Targeted Optical Biopsy
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Efficient Random Sampling for Nonrigid Feature Matching
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Scale-invariant proximity graph for fast probabilistic object recognition
Proceedings of the ACM International Conference on Image and Video Retrieval
Feature correspondence with constrained global spatial structures
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Improving shape retrieval by spectral matching and meta similarity
IEEE Transactions on Image Processing
Detecting mutually-salient landmark pairs with MRF regularization
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Improving data association by joint modeling of pedestrian trajectories and groupings
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Reweighted random walks for graph matching
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Shape google: Geometric words and expressions for invariant shape retrieval
ACM Transactions on Graphics (TOG)
Geodesic Methods in Computer Vision and Graphics
Foundations and Trends® in Computer Graphics and Vision
Versatile spectral methods for point set matching
Pattern Recognition Letters
Reducing ambiguity in object recognition using relational information
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Learning sparse features on-line for image classification
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Graph based spatial position mapping of low-grade gliomas
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence
International Journal of Computer Vision
Discrete minimum distortion correspondence problems for non-rigid shape matching
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Structured Learning and Prediction in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Graph matching via sequential monte carlo
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Finding correspondence from multiple images via sparse and low-rank decomposition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Real-Time exact graph matching with application in human action recognition
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Vote based correspondence for 3D point-set registration
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Balanced feature matching in probabilistic framework and its application on object localisation
International Journal of Computer Applications in Technology
A linear programming based method for joint object region matching and labeling
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Computer Vision and Image Understanding
Partial correspondence based on subgraph matching
Neurocomputing
Detecting bipedal motion from correlated probabilistic trajectories
Pattern Recognition Letters
A sparse nonnegative matrix factorization technique for graph matching problems
Pattern Recognition
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In this paper we present a new approach for establishing correspondences between sparse image features related by an unknown non-rigid mapping and corrupted by clutter and occlusion, such as points extracted from a pair of images containing a human figure in distinct poses. We formulate this matching task as an energy minimization problem by defining a complex objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general a NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show on several matching tasks that our learned model yields results superior to those of state-of-the-art methods.