Selection and Fusion of Multiple Stereo Algorithms for Accurate Disparity Segmentation

Date

2009-01-01

Author

Bilgin, Arda
Ulusoy, İlkay

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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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Citation Formats

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.