Enhancing Active Vision System Categorization Capability Through Uniform Local Binary Patterns [chapter]

Olalekan Lanihun, Bernie Tiddeman, Elio Tuci, Patricia Shaw
2015 Communications in Computer and Information Science  
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 partly due to variability in lighting conditions, reflection, shadowing etc, which may be inherent to these kind of problems. It could also be due to the fact that building an evolved system for these kind of
more » ... oblems 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 Pattern, [8] method. 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.
doi:10.1007/978-3-319-18084-7_3 fatcat:dc23idvqy5fepb7tygy7oq3hcq