DEPTH PREDICTION AT HOMOGENEOUS IMAGE STRUCTURES

Date

2008-01-22

Author

Kalkan, Sinan
Wörgötter, Florentin
Kruger, Norbert

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

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This paper proposes a voting-based model that predicts depth at weakly-structured image areas from the depth that is extracted using a feature-based stereo method. We provide results, on both real and artificial scenes, that show the accuracy and robustness of our approach. Moreover, we compare our method to different dense stereo algorithms to investigate the effect of texture on performance of the two different approaches. The results confirm the expectation that dense stereo methods are suited better for textured image areas and our method for weakly-textured image areas.

URI

https://www.scitepress.org/Link.aspx?doi=10.5220/0001079005200527
https://hdl.handle.net/11511/96537

DOI

https://doi.org/10.5220/0001079005200527

Conference Name

Proceedings of the Third International Conference on Computer Vision Theory and Applications

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

S. Kalkan, F. Wörgötter, and N. Kruger, “DEPTH PREDICTION AT HOMOGENEOUS IMAGE STRUCTURES,” presented at the Proceedings of the Third International Conference on Computer Vision Theory and Applications, Funchal, Portekiz, 2008, Accessed: 00, 2022. [Online]. Available: https://www.scitepress.org/Link.aspx?doi=10.5220/0001079005200527.