Disparity disambiguation by fusion of signal- and symbolic-level information

  • Authors:

  • Jarno Ralli;Javier Díaz;Sinan Kalkan;Norbert Krüger;Eduardo Ros

  • Affiliations:

  • Universidad de Granada, Departamento de Arquitectura y Tecnología de Computadores, Escuela Técnica Superior de Ingeniería Informatica y de Telecomunicacíon, Calle Perio ...;Universidad de Granada, Departamento de Arquitectura y Tecnología de Computadores, Escuela Técnica Superior de Ingeniería Informatica y de Telecomunicacíon, Calle Perio ...;Middle East Technical University, KOVAN Research Lab, Department of Computer Engineering, 06531, Ankara, Turkey;University of Southern Denmark, Cognitive Vision Lab, The Maersk Mc-Kinney Moller Institute, Niels Bohrs Alle 1, 5230, Odense M, Denmark;Universidad de Granada, Departamento de Arquitectura y Tecnología de Computadores, Escuela Técnica Superior de Ingeniería Informatica y de Telecomunicacíon, Calle Perio ...

  • Venue:
  • Machine Vision and Applications
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

We describe a method for resolving ambiguities in low-level disparity calculations in a stereo-vision scheme by using a recurrent mechanism that we call signal-symbol loop. Due to the local nature of low-level processing it is not always possible to estimate the correct disparity values produced at this level. Symbolic abstraction of the signal produces robust, high confidence, multimodal image features which can be used to interpret the scene more accurately and therefore disambiguate low-level interpretations by biasing the correct disparity. The fusion process is capable of producing more accurate dense disparity maps than the low- and symbolic-level algorithms can produce independently. Therefore we describe an efficient fusion scheme that allows symbolic- and low-level cues to complement each other, resulting in a more accurate and dense disparity representation of the scene.