Unsupervised Semantic Object Segmentation of Stereoscopic Video Sequences

• Venue: ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
• Year: 1999

In this paper, we present an efficient technique for unsupervised semantically meaningful object segmentation of stereoscopic video sequences. By this technique we achieve to 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 multi-resolution implementation of the RSST algorithm. Color segment fusion is employed using the depth segments as a kind of constraints. Finally experimental results are presented which demonstrate the high-quality of semantic object segmentation this technique achieves.