Enhancing active vision system categorization capability through uniform local binary patterns

Lanihun, Olalekan, Tiddeman, Bernie, Tuci, Elio and Shaw, Patricia (2015) Enhancing active vision system categorization capability through uniform local binary patterns. Artificial Life and Intelligent Agents. In: ALIA 2014: 1st Artificial Life and Intelligent Agents symposium, 05-06 Nov 2014, Bangor, United Kingdom. ISBN 9783319180830. ISSN 1865-0929 [Conference or Workshop Item] (doi:10.1007/978-3-319-18084-7_3)

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Previous research in Neuro-Evolution controlled Active Vision Systems has shown its potential to solve various shape categorization and discrimination problems. However, minimal investigation has been done in using this kind of evolved system in solving more complex vision problems. This is partly due to variability in lighting conditions, reflection, shadowing etc., which may be inherent to these kinds of problems. It could also be due to the fact that building an evolved system for these kinds of problems may be too computationally expensive. We present an Active Vision System controlled Neural Network trained by a Genetic Algorithm that can autonomously scan through an image pre-processed by Uniform Local Binary Patterns [8]. We demonstrate the ability of this system to categorize more complex images taken from the camera of a Humanoid (iCub) robot. Preliminary investigation results show that the proposed Uniform Local Binary Pattern [8] method performed better than the gray-scale averaging method of [1] in the categorization tasks. This approach provides a framework that could be used for further research in using this kind of system for more complex image problems.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Paper published as:
Lanihun O., Tiddeman B., Tuci E., Shaw P. (2015) Enhancing Active Vision System Categorization Capability Through Uniform Local Binary Patterns. In: Headleand C., Teahan W., Ap Cenydd L. (eds) Artificial Life and Intelligent Agents. ALIA 2014. Communications in Computer and Information Science, vol 519. Springer, Cham
Research Areas: A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 21946
Notes on copyright: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-18084-7_3
Depositing User: Elio Tuci
Date Deposited: 13 Jun 2017 14:18
Last Modified: 29 Nov 2022 22:41
URI: https://eprints.mdx.ac.uk/id/eprint/21946

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