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Integration of Stereoscopic and Perspective Cues for Slant Estimation in Natural and Artificial Systems

  • Eris Chinellato
  • Angel P. del Pobil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)

Abstract

Within the framework of a model of vision-based robotic grasping inspired on neuroscience data, we deal with the problem of object orientation estimation by analyzing human psychophysical data in order to reproduce them in an artificial setup. A set of ANN is implemented which, on the one hand, allows to replicate some neuroscientific findings and, on the other hand, constitutes a tool for slant estimation that can improve the reliability of artificial vision systems, namely those dedicated to analyze visual data inherent to the interaction robot-environment, such as in grasping actions. The implementation confirms the hypothesis that integration of monocular and binocular data for the extraction of action-related object properties can provide an artificial system with improved pose estimation capabilities.

Keywords

Binocular Disparity Orientation Estimation Robotic Application Vertical Disparity Vergence Angle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Eris Chinellato
    • 1
  • Angel P. del Pobil
    • 1
  1. 1.Robotic Intelligence Lab, Universitat Jaume I, Castellón de la PlanaSpain

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