Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Evolving Task Specific Image Operator
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
Function choice, resiliency and growth in genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A new accurate and flexible model based multi-corner detector for measurement and recognition
Pattern Recognition Letters
Synthesis of interest point detectors through genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Corner validation based on extracted corner properties
Computer Vision and Image Understanding
Visual mapping by a robot rover
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
Faster and Better: A Machine Learning Approach to Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Crossover Operator in Genetic Programming for Object Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
This paper introduces GP- (Genetic Programming-) based robust corner detectors for scaled and rotated images. Previous Harris, SUSAN and FAST corner detectors are highly efficient for well-defined corners, but frequently mis-detect as corners the corner–like edges which are often generated in rotated images. It is very difficult to avoid incorrectly detecting as corners many edges which have characteristics similar to corners. In this paper, we have focused on this challenging problem and proposed using Genetic Programming to do automated generation of corner detectors that work robustly on scaled and rotated images. Various terminal sets are presented and tested to capture the key properties of corners. Combining intensity-related information, several mask sizes, and amount of contiguity of neighboring pixels of similar intensity, allows a well-devised terminal set to be proposed. This method is then compared to three existing corner detectors on test images and shows superior results.