Outdoor image region recognition based on MS-OS-BOW model

To recognize accessible regions successfully and accurately is an important part of mobile robot navigation in outdoor environment. This thesis studies that the modified Mean Shift algorithm is applied for region partition on outdoor images obtained by robot vision. Then all parts of these pictures are recognized through the combination BOW model and the Opponent SIFT feature. And finally the results of region recognition are optimized through SVM and the way of using MS-OS-BOW (Mean Shift-Opponent SIFT-Bag Of Words) model to solve the problem of outdoor image region recognition is proposed. Specifically, outdoor working robots need to analyze received visual images to search for accessible path. For the outdoor images with broad angle of view and simple layout, whether it is accessible path or not can be decided by the semantics of this region. Therefore, the understanding of outdoor images can be finished by region recognition. These final experimental results demonstrate the effectiveness and practicability of outdoor image region recognition based on MS-OS-BOW model.