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Bayesian Genetic Programming for Edge Detection

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Fu, W., Zhang, M. and Johnston, Mark (2019) Bayesian Genetic Programming for Edge Detection. Soft Computing, 23 (12). pp. 4097-4112. ISSN 1432-7643 Online: 1433-7479

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Official URL: https://link.springer.com/article/10.1007/s00500-0...

Abstract

In edge detection, designing new techniques to combine local features is expected to improve detection performance. However, how to effectively design combination techniques remains an open issue. In this study, an automatic design approach is proposed to combine local edge features using Bayesian programs (models) evolved by genetic programming (GP). Multivariate density is used to estimate prior probabilities for edge points and non-edge points. Bayesian programs evolved by GP are used to construct composite features after estimating the relevant multivariate density. The results show that GP has the ability to effectively evolve Bayesian programs. These evolved programs have higher detection accuracy than the combination of local features by directly using the multivariate density (of these local features) in a simple Bayesian model. From evolved Bayesian programs, the proposed GP system has potential to effectively select features to construct Bayesian programs for performance improvement.

Item Type:	Article
Additional Information:	Staff and students at the University of Worcester can access the full-text via the UW online library search. External users should check availability with their local library or Interlibrary Requests Service.
Uncontrolled Discrete Keywords:	genetic programming, edge detection, Bayesian model, feature construction
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics
Divisions:	College of Health, Life and Environmental Sciences > School of Science and the Environment
Related URLs:	Publisher http://library.worc.ac.uk/
Depositing User:	Mark Johnston
Date Deposited:	27 Apr 2018 12:46
Last Modified:	17 Jun 2020 17:21
URI:	https://eprints.worc.ac.uk/id/eprint/6465

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