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Chapter

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Title

Implicit Fitness Sharing for Evolutionary Synthesis of License Plate Detectors

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | [ P ] employee

Year of publication

2013

Chapter type

paper

Publication language

english

Keywords
EN
  • genetic programming
  • pattern recognition
  • image analysis
  • implicit fitness sharing
  • license plate recognition
Abstract

EN 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.

Pages (from - to)

376 - 386

DOI

10.1007/978-3-642-37192-9_38

URL

https://link.springer.com/chapter/10.1007/978-3-642-37192-9_38

Book

Applications of Evolutionary Computation : 16th European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013 : Proceedings

Presented on

16th European Conference on the Applications of Evolutionary Computation, EvoApplications 2013, 3-5.04.2013, Vienna, Austria

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