Outlier removal based on Chauvenet’s criterion and dense disparity refinement using least square support vector machine

Zhang Zihao; Zhan Lingli; Yuanqing Wang

doi:10.1117/1.JEI.28.2.023028

5 April 2019 Outlier removal based on Chauvenet’s criterion and dense disparity refinement using least square support vector machine

Zhang Zihao, Zhan Lingli, Yuanqing Wang

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 2, 023028 (April 2019). https://doi.org/10.1117/1.JEI.28.2.023028

Abstract

Disparity refinement based on a regression model continues to be challenging for specified function with a weak generalization ability. An invalid disparity refinement method to improve the quality of the disparity map is proposed. This method includes two committed steps: outliers disparity removal and redefinition of invalid pixels. To obtain a more accurate initial disparity map, removal of outliers based on Chauvenet’s criterion method is proposed, using the distribution of disparity values on a segmentation region. Then, the least square support vector machine model is applied to every horizontal line of the obtained initial disparity map to model the valid disparity values, corresponding image color values, and co-ordinates of pixels. Finally, invalid pixels are redefined by the regression model. Experimental results demonstrate that the dense disparity maps of the proposed method show superior performance compared with current state-of-the-art methods.

Citation Download Citation

Zhang Zihao, Zhan Lingli, and Yuanqing Wang "Outlier removal based on Chauvenet’s criterion and dense disparity refinement using least square support vector machine," Journal of Electronic Imaging 28(2), 023028 (5 April 2019). https://doi.org/10.1117/1.JEI.28.2.023028

Received: 10 September 2018; Accepted: 14 March 2019; Published: 5 April 2019

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KEYWORDS

Image segmentation

Image processing

RGB color model

Performance modeling

Systems modeling

Binary data

Reconstruction algorithms

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