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

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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.

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Authors and Affiliations

  1. Departamento de Arquitectura y Tecnología de Computadores, Escuela Técnica Superior de Ingeniería Informatica y de Telecomunicacíon, Universidad de Granada, Calle Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Jarno Ralli, Javier Díaz & Eduardo Ros

  2. KOVAN Research Lab, Department of Computer Engineering, Middle East Technical University, 06531, Ankara, Turkey

    Sinan Kalkan

  3. Cognitive Vision Lab, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Niels Bohrs Alle 1, 5230, Odense M, Denmark

    Norbert Krüger

Authors
  1. Jarno Ralli
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  2. Javier Díaz
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  3. Sinan Kalkan
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  4. Norbert Krüger
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  5. Eduardo Ros
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Correspondence to Jarno Ralli.

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Ralli, J., Díaz, J., Kalkan, S. et al. Disparity disambiguation by fusion of signal- and symbolic-level information. Machine Vision and Applications 23, 65–77 (2012). https://doi.org/10.1007/s00138-010-0266-z

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  • DOI: https://doi.org/10.1007/s00138-010-0266-z

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