Matrix analysis
Integer and combinatorial optimization
Integer and combinatorial optimization
Solution of the concave linear complementarity problem
Recent advances in global optimization
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A bipartite matching approach to feature correspondence in stereo vision
Pattern Recognition Letters
Reconstruction of 3D-Curves from 2D-Images Using Affine Shape Methods for Curves
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Contextual Inference in Contour-Based Stereo Correspondence
International Journal of Computer Vision
Multi-view correspondence by enforcement of rigidity constraints
Image and Vision Computing
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Efficient Random Sampling for Nonrigid Feature Matching
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
A quadratic programming based cluster correspondence projection algorithm for fast point matching
Computer Vision and Image Understanding
A fast and robust feature-based 3D algorithm using compressed image correlation
Pattern Recognition Letters
Texture-independent feature-point matching (TIFM) from motion coherence
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Descriptor-free smooth feature-point matching for images separated by small/mid baselines
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Shapes as empirical distributions
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Lamp: linear approach for matching points
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Reweighted random walks for graph matching
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A new approach to corner matching from image sequence using fuzzy similarity index
Pattern Recognition Letters
Image registration using triangular mesh
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Optimal multi-frame correspondence with assignment tensors
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Feature matching and pose estimation using newton iteration
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A probabilistic model for correspondence problems using random walks with restart
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Fast and scalable approximate spectral graph matching for correspondence problems
Information Sciences: an International Journal
Robust point matching revisited: a concave optimization approach
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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
Robust anatomical correspondence detection by graph matching with sparsity constraint
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
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We propose a new methodology for reliably solving the correspondence problem between sparse sets of points of two or more images. This is a key step in most problems of computer vision and, so far, no general method exists to solve it. Our methodology is able to handle most of the commonly used assumptions in a unique formulation, independent of the domain of application and type of features. It performs correspondence and outlier rejection in a single step and achieves global optimality with feasible computation. Feature selection and correspondence are first formulated as an integer optimization problem. This is a blunt formulation, which considers the whole combinatorial space of possible point selections and correspondences. To find its global optimal solution, we build a concave objective function and relax the search domain into its convex-hull. The special structure of this extended problem assures its equivalence to the original one, but it can be optimally solved by efficient algorithms that avoid combinatorial search. This methodology can use any criterion provided it can be translated into cost functions with continuous second derivatives.