IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation of local geometry in the visual system
Biological Cybernetics
Recognizing corners by fitting parametric models
International Journal of Computer Vision
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Axiomatic Approach to Corner Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A new accurate and flexible model based multi-corner detector for measurement and recognition
Pattern Recognition Letters
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Synthesis of interest point detectors through genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Evolution to Learn How to Perform Interest Point Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Use of power law models in detecting region of interest
Pattern Recognition
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpretability based interest points detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Interest point detection using imbalance oriented selection
Pattern Recognition
Registration of Challenging Image Pairs: Initialization, Estimation, and Decision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Top-points as interest points for image matching
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
The estimation of hölderian regularity using genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Optimization of the hölder image descriptor using a genetic algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Interest point detection through multiobjective genetic programming
Applied Soft Computing
Evolutionary purposive or behavioral vision for camera trajectory estimation
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Evolving estimators of the pointwise Hölder exponent with Genetic Programming
Information Sciences: an International Journal
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In this paper, a multiobjective (MO) learning approach to image feature extraction is described, where Pareto-optimal interest point (IP) detectors are synthesized using genetic programming (GP). IPs are image pixels that are unique, robust to changes during image acquisition, and convey highly descriptive information. Detecting such features is ubiquitous to many vision applications, e.g. object recognition, image indexing, stereo vision, and content based image retrieval. In this work, candidate IP operators are automatically synthesized by the GP process using simple image operations and arithmetic functions. Three experimental optimization criteria are considered: 1) the repeatability rate; 2) the amount of global separability between IPs; and 3) the information content captured by the set of detected IPs. The MO-GP search considers Pareto dominance relations between candidate operators, a perspective that has not been contemplated in previous research devoted to this problem. The experimental results suggest that IP detection is an illposed problem for which a single globally optimum solution does not exist. We conclude that the evolved operators outperform and dominate, in the Pareto sense, all previously man-made designs.