Ensemble learning with advanced fast image filtering features for semi-global matching

For the past several years, a variety of algorithms have been focused on how to exploit two-dimensional scanline optimization to augment one-dimensional ones for semi-global matching. Different from the former contributions, an ensemble learning with advanced fast image filtering features for semi-global matching is proposed in this paper. Firstly, fewer categories of features (confidence measures) are extracted through various advanced fast image filters on the original scale of 8 directions’ semi-global matching disparity maps. Then, all the features are weaved together and divided into positive and negative samples for ensemble learning after comparing with ground truth. After that, the initial disparity map is obtained by leveraging the confidence probability of ensemble learning prediction. Finally, an efficient two-step single view disparity refinement strategy is employed, which no longer requires the right view’s disparity map for attaining the final refined results. Performance evaluations on Middlebury v.2 and v.3 stereo data sets demonstrate that the proposed algorithm outperforms other four most recent stereo matching algorithms. In addition, the presented algorithm shows relative high implementation efficiency compared with others.