Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Feature extraction using an unsupervised neural network
Neural Computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning-integrated interactive image segmentation
Advances in evolutionary computing
Gender classification based on feature selection using genetic algorithms
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
A Real-Time Evolutionary Object Recognition System
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Evolving object detectors with a GPU accelerated vision system
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
Towards automated learning of object detectors
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
| Hi-index | 0.14 |
Describes results obtained from applying genetic algorithms to the problem of detecting targets in image data. The method described is a two-layered approach, with the first layer providing a focus-of-attention function for the second layer. The first layer is called a Screener and selects subimages from the original image data to be processed by the second layer, called the Classifier. The Screener reduces the computational load of the system. Each layer consists of a set of linear operators (filters) applied directly to the image data. A genetic algorithm is applied to populations of filters based on fitness criteria. The authors note that the statistical classifier chosen for the Classifier stage drives the evolution of filters that are useful for that classifier to make good discriminations.