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

摘要

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. (C) 2019 SPIE and IS&T