Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Shape Recovery Using Distributed Aspect Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Artificial Intelligence - Special volume on computer vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
A computational model for visual selection
Neural Computation
Probabilistic Models of Appearance for 3-D Object Recognition
International Journal of Computer Vision
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Measurement of Image Velocity
Perceptual Organization for Artificial Vision Systems
Perceptual Organization for Artificial Vision Systems
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Appearance and Topology for Wide Baseline Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
On the Representation and Matching of Qualitative Shape at Multiple Scales
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Automated Scene Matching in Movies
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Object Recognition Using Appearance-Based Parts and Relations
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pruning Local Feature Correspondences Using Shape Context
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
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
A SIFT Descriptor with Global Context
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Real-Time Non-Rigid Surface Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Distinctiveness, Detectability, and Robustness of Local Image Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Flexible spatial models for grouping local image features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multi-scale phase-based local features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
The quantitative characterization of the distinctiveness and robustness of local image descriptors
Image and Vision Computing
Registering aerial video images using the projective constraint
IEEE Transactions on Image Processing
The relative potential field as a novel physics-inspired method for image analysis
WSEAS Transactions on Computers
Scale invariant gabor descriptor-based noncooperative iris recognition
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Bag of spatio-visual words for context inference in scene classification
Pattern Recognition
In defence of RANSAC for outlier rejection in deformable registration
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Detection and segmentation of approximate repetitive patterns in relief images
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Hough Pyramid Matching: Speeded-Up Geometry Re-ranking for Large Scale Image Retrieval
International Journal of Computer Vision
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Local image features have been designed to be informative and repeatable under rigid transformations and illumination deformations. Even though current state-of-the-art local image features present a high degree of repeatability, their local appearance alone usually does not bring enough discriminative power to support a reliable matching, resulting in a relatively high number of mismatches in the correspondence set formed during the data association procedure. As a result, geometric filters, commonly based on global spatial configuration, have been used to reduce this number of mismatches. However, this approach presents a trade off between the effectiveness to reject mismatches and the robustness to non-rigid deformations. In this paper, we propose two geometric filters, based on semilocal spatial configuration of local features, that are designed to be robust to non-rigid deformations and to rigid transformations, without compromising its efficacy to reject mismatches. We compare our methods to the Hough transform, which is an efficient and effective mismatch rejection step based on global spatial configuration of features. In these comparisons, our methods are shown to be more effective in the task of rejecting mismatches for rigid transformations and non-rigid deformations at comparable time complexity figures. Finally, we demonstrate how to integrate these methods in a probabilistic recognition system such that the final verification step uses not only the similarity between features, but also their semi-local configuration.