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

Date

2012-01-01

Author

Ralli, Jarno
Diaz, Javier
Kalkan, Sinan
Krueger, Norbert
Ros, Eduardo

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Hardware and Architecture, Software, Computer Vision and Pattern Recognition, Computer Science Applications

URI

https://hdl.handle.net/11511/45941

Journal

MACHINE VISION AND APPLICATIONS

DOI

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

Collections

Department of Computer Engineering, Article

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Citation Formats

J. Ralli, J. Diaz, S. Kalkan, N. Krueger, and E. Ros, “Disparity disambiguation by fusion of signal- and symbolic-level information,” MACHINE VISION AND APPLICATIONS, pp. 65–77, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/45941.