Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of 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
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational model for visual selection
Neural Computation
Probabilistic Models of Appearance for 3-D Object Recognition
International Journal of Computer Vision
Measurement of Image Velocity
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
Automated Scene Matching in Movies
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Hierarchical building recognition
Image and Vision Computing
Flexible Spatial Configuration of Local Image Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA-SIFT: a more distinctive representation for local image descriptors
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
Sparse flexible models of local features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Landmark Selection for Vision-Based Navigation
IEEE Transactions on Robotics
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We introduce a new method that characterizes quantitatively local image descriptors in terms of their distinctiveness and robustness to geometric transformations and brightness deformations. The quantitative characterization of these properties is important for recognition systems based on local descriptors because it allows for the implementation of a classifier that selects descriptors based on their distinctiveness and robustness properties. This classification results in: (a) recognition time reduction due to a smaller number of descriptors present in the test image and in the database of model descriptors; (b) improvement of the recognition accuracy since only the most reliable descriptors for the recognition task are kept in the model and test images; and (c) better scalability given the smaller number of descriptors per model. Moreover, the quantitative characterization of distinctiveness and robustness of local descriptors provides a more accurate formulation of the recognition process, which has the potential to improve the recognition accuracy. We show how to train a multi-layer perceptron that quickly classifies robust and distinctive local image descriptors. A regressor is also trained to provide quantitative models for each descriptor. Experimental results show that the use of these trained models not only improves the performance of our recognition system, but it also reduces significantly the computation time for the recognition process.