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Selection and Fusion of Multiple Stereo Algorithms for Accurate Disparity Segmentation
Date
2009-01-01
Author
Bilgin, Arda
Ulusoy, İlkay
Metadata
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Fusion of multiple stereo algorithms is performed in order to obtain accurate disparity segmentation in this study. Reliable disparity map of real-time stereo images is estimated and disparity segmentation is performed for object detection purpose. First, stereo algorithms which have high performance in real-time applications are chosen among the algorithms in the literature and three of them are implemented. Then, the results of these algorithms are fused to gain better performance in disparity estimation. In fusion process, if a pixel has the same disparity, value in all algorithms, that disparity value is assigned to the pixel. Other pixels are labelled as unknown disparity. Then, unknown disparity values are estimated by a refinement procedure where neighbourhood disparity information is used. Finally, the resultant disparity map is segmented by using mean shift segmentation.
Subject Keywords
Image segmentation
,
Object detection
,
Performance gain
URI
https://hdl.handle.net/11511/95200
DOI
https://doi.org/10.1109/siu.2009.5136420
Conference Name
IEEE 17th Signal Processing and Communications Applications Conference
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
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Selection and fusion of multiple stereo algorithms for accurate disparity segmentation
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A. Bilgin and İ. Ulusoy, “Selection and Fusion of Multiple Stereo Algorithms for Accurate Disparity Segmentation,” presented at the IEEE 17th Signal Processing and Communications Applications Conference, Antalya, Türkiye, 2009, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/95200.
