Target detection in SAR imagery by genetic programming
Advances in Engineering Software
The Boru Data Crawler for Object Detection Tasks in Machine Vision
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Programming for Feature Detection and Image Segmentation
Selected Papers from AISB Workshop on Evolutionary Computing
Genetic programming for image analysis
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Object detection using neural networks and genetic programming
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Refining fitness functions and optimising training data in GP for object detection
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Localisation fitness in GP for object detection
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
GP for object classification: brood size in brood recombination crossover
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Analysis of building blocks with numerical simplification in genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Eliminating useless object detectors evolved in multiple-objective genetic programming
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Hi-index | 0.00 |
This paper describes a domain independent approach to the use of genetic programming for object detection problems. Rather than using raw pixels or high level domain specific features, this approach uses domain independent statistical features as terminals in genetic programming. Besides position invariant statistics such as mean and standard deviation, this approach also uses position dependent pixel statistics such as moments and local region statistics as terminals. Based on an existing fitness function which uses linear combination of detection rate and false alarm rate, we introduce a new measure called "false alarm area" to the fitness function. In addition to the standard arithmetic operators, this approach also uses a conditional operator if in the function set. This approach is tested on two object detection problems. The experiments suggest that position dependent pixel statistics computed from local (central) regions and nonlinear condition functions are effective to object detection problems. Fitness functions with false alarm area can reflect the smoothness of evolved genetic programs. This approach works well for the detecting small regular multiple class objects on a relatively uncluttered background.