Artificial Intelligence - Special volume on computer vision
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Markov random field modeling in image analysis
Markov random field modeling in image analysis
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Robust Point Matching for Nonrigid Shapes by Preserving Local Neighborhood Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views
International Journal of Computer Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Simultaneous plane extraction and 2D homography estimation using local feature transformations
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
On the Foundations of Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards reliable matching of images containing repetitive patterns
Pattern Recognition Letters
Robust line matching through line-point invariants
Pattern Recognition
Vote based correspondence for 3D point-set registration
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering
Efficient and scalable 4th-order match propagation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
A sparse nonnegative matrix factorization technique for graph matching problems
Pattern Recognition
Robust point pattern matching based on spectral context
Pattern Recognition
Computer Vision and Image Understanding
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We present a method for matching feature points robustly across widely separated images. In general, it is difficult to match feature points correctly by using only the similarity between local descriptors. In our approach, the correspondence problem is formulated as an optimization problem with one-to-one correspondence constraints. A novel objective function is defined to preserve local image-to-image affine transformations across correspondences. This objective function enables our method to cope with significant viewpoint or scale changes between images, unlike previous methods that relied on the assumption that the distance or orientation between neighboring feature points are preserved across images. A relaxation algorithm is proposed for maximizing the objective function, which imposes one-to-one correspondence constraints, unlike conventional relaxation labeling algorithms that impose many-to-one correspondence constraints. Experimental evaluation shows that our method is robust with respect to significant viewpoint changes, scale changes, and nonrigid deformations between images, in the presence of repeated textures that make feature point matching more ambiguous. Our method is also applied to object recognition in cluttered environments, giving some promising results.