Unsupervised semantic object segmentation of stereoscopic video sequences
Anastasios D. Doulamis, Nikolaos D. Doulamis, Klimis Ntalianis, Stefanos Kollias · 2003
In this paper, we present an efficient technique for unsupervised semantically meaningful object segmentation of stereoscopic video sequences. Using this technique we extract semantic objects using the additional information a stereoscopic pair of frames provides. Each pair is analyzed and the disparity field, occluded areas and depth map are estimated. The key algorithm, which is applied on the stereo pair of images and performs the segmentation, is a powerful low-complexity multiresolution implementation of the RSST algorithm. Color segment fusion is employed using the depth segments as a kind of constraint. Finally experimental results are presented which demonstrate the high-quality of semantic object segmentation this technique achieves.