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Improving Active Vision System Categorization Capability Through Histogram of Oriented Gradients

  • Olalekan Lanihun
  • Bernie Tiddeman
  • Elio Tuci
  • Patricia Shaw
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9287)

Abstract

In the previous work of Mirolli et al. [1], an active vision system controlled by a genetic algorithm evolved neural network was used in simple letter categorization system, using gray-scale average noise filtering of an artificial eye retina. Lanihun et al. [2] further extends on this work by using Uniform Local Binary Patterns (ULBP) [4] as a pre-processing technique, in order to enhance the robustness of the system in categorizing objects in more complex images taken from the camera of a Humanoid (iCub) robot . In this paper we extend on the work in [2], using Histogram of Oriented Gradients (HOG) [5] to improve the performance of this system for the same iCub image problem. We demonstrate this ability by performing comparative experiments among the three methods. Preliminary results show that the proposed HOG method performed better than the ULBP and the gray-scale averaging [1] methods. The approach of better pre-processing with HOG gives a representation that could translate to improve motor responses in enhancing categorization capability for robotic vision control systems.

Keywords

Categorization Active vision system Neural network Genetic algorithm Histogram of oriented gradients 

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References

  1. 1.
    Mirolli, M., Ferrauto, T., Nolfi, S.: Categorisation through Evidence Accumulation in an Active Vision System. Connection Science 22, 331–354 (2010)CrossRefGoogle Scholar
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    Lanihun, O., Tiddeman, B., Tuci, E., Shaw, P.: Enhancing active vision system categorization capability through uniform local binary patterns. In: Headleand, C.J., Teahan, W.J., Ap Cenydd, L. (eds.) ALIA 2014. CCIS, vol. 519, pp. 31–43. Springer, Heidelberg (2015) CrossRefGoogle Scholar
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    James, D., Tucker, P.: Evolving a neural network active vision system for shape discrimination. In: Genetic and Evolutionary Computation Conference (2005)Google Scholar
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    Ojala, T., Pietikinen, M., Maenpaa, T.: Multi-Resolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions, Pattern Analysis and Machine Intelligence 29, 51–59 (2002)Google Scholar
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    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. Computer Vision and Pattern Recognition 1, 886–893 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Olalekan Lanihun
    • 1
  • Bernie Tiddeman
    • 1
  • Elio Tuci
    • 1
  • Patricia Shaw
    • 1
  1. 1.Department of Computer ScienceAberystwyth UniversityAberystwythUK

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