Evolutionary active vision system: From 2D to 3D

Olalekan Adebayo Lanihun, Bernie Tiddeman, Patricia Shaw, Elio Tuci

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Abstract

Biological vision incorporates intelligent cooperation between the sensory and the motor systems, which is facilitated by the development of motor skills that help to shape visual information that is relevant to a specific vision task. In this article, we seek to explore an approach to active vision inspired by biological systems, which uses limited constraints for motor strategies through progressive adaptation via an evolutionary method. This type of approach gives freedom to artificial systems in the discovery of eye-movement strategies that may be useful to solve a given vision task but are not known to us. In the experiment sections of this article, we use this type of evolutionary active vision system for more complex natural images in both two-dimensional (2D) and three-dimensional (3D) environments. To further improve the results, we experiment with the use of pre-processing the visual input with both the uniform local binary patterns (ULBP) and the histogram of oriented gradients (HOG) for classification tasks in the 2D and 3D environments. The 3D experiments include application of the active vision system to object categorisation and indoor versus outdoor environment classification. Our experiments are conducted on the iCub humanoid robot simulator platform.
Original languageEnglish
Pages (from-to)3-24
Number of pages22
JournalAdaptive Behavior
Volume29
Issue number1
Early online date03 Oct 2019
DOIs
Publication statusPublished - 01 Feb 2021

Keywords

  • Active vision system
  • Neural network
  • Evolutionary robotics
  • Uniform local binary patterns
  • Histogram of oriented gradients
  • Humanoid robot
  • evolutionary robotics
  • histogram of oriented gradients
  • humanoid robot
  • uniform local binary patterns
  • neural network

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