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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
Metadata
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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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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.