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Australasian Joint Conference on Artificial Intelligence

AI 2012: AI 2012: Advances in Artificial Intelligence pp 144–155Cite as

Automatic Construction of Invariant Features Using Genetic Programming for Edge Detection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7691))

Abstract

This paper investigates automatic construction of invariant features using Genetic Programming (GP) for edge detection. Generally, basic features for edge detection, such as gradients, are further manipulated to improve detection performance. In order to improve detection performance, new features are constructed from different local features. In this study, GP is proposed to automatically construct invariant features based on basic invariant features from gradients, image quality (means and standard deviations), and histograms of images. The experimental results show that the invariant features constructed by GP combine advantages from the basic features, reduce drawbacks from basic features alone, and also improve the detection performance.

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Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, Wellington, New Zealand

    Wenlong Fu & Mark Johnston

  2. School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand

    Mengjie Zhang

Authors
  1. Wenlong Fu
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  2. Mark Johnston
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  3. Mengjie Zhang
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, NSW, Australia

    Michael Thielscher

  2. School of Computing and Mathematics, University of Western Sydney, 1797, Penrith South DC, NSW, Australia

    Dongmo Zhang

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© 2012 Springer-Verlag Berlin Heidelberg

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Fu, W., Johnston, M., Zhang, M. (2012). Automatic Construction of Invariant Features Using Genetic Programming for Edge Detection. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_13

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  • DOI: https://doi.org/10.1007/978-3-642-35101-3_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35100-6

  • Online ISBN: 978-3-642-35101-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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