Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
The fuzzy logic approach to the car number plate locating problem
IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
Ideal Evaluation from Coevolution
Evolutionary Computation
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Adaptive Car Plate Recognition in QoS-Aware Security Network
SSIRI '08 Proceedings of the 2008 Second International Conference on Secure System Integration and Reliability Improvement
License Plate Recognition Based on Genetic Algorithm
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Evolutionary tuning of compound image analysis systems for effective license plate recognition
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
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A genetic programming algorithm for synthesis of object detection systems is proposed and applied to the task of license plate recognition in uncontrolled lighting conditions. The method evolves solutions represented as data flows of high-level parametric image operators. In an extended variant, the algorithm employs implicit fitness sharing, which allows identifying the particularly difficult training examples and focusing the training process on them. The experiment, involving heterogeneous video sequences acquired in diverse conditions, demonstrates that implicit fitness sharing substantially improves the predictive performance of evolved detection systems, providing maximum recognition accuracy achievable for the considered setup and training data.