RIS ID
54322
Abstract
This paper presents a novel discriminative approach for pave-ment scene understanding and obstacle detection in real-world images. It overcomes the heavy constraints in previous systems such as a simple background, a specic obstacle, etc. The approach we exploited extends the bundle method to incorporate pairwise correlations among neighboring pixels, and adopts graph-cuts as the inference engine to attain the approximation efficiently. A set of robust features on both local and multi-scale level is also introduced that captures the general statistical properties of pavements and obstacles. The proposed approach is validated on real-world image database, and outperforms the current state-of-the-art visioned-based methods
COinS
Publication Details
Jia, K., Liu, N., Wang, L. & Cheng, L. (2009). Pavement scene interpretation and obstacle detection by large margin image labeling. International Workshop on Vision and Control for Access Space (VCAS), in conjunction with the 9th Asian Conference on Computer Vision