Abstract
The growing study in RGB-D sensor and 3D point cloud have made new progress in obstacle avoidance for the visually impaired. However, it remains a challenging problem due to the difficulty in design a robust and real-time algorithm. In this paper, we focus on scene segmentation and labeling. As man-made indoor scene contains many planar area and structure, plane segmentation and classification is important for further scene analysis. This work propose a multiscale-voxel strategy to reduce the effects of noise and improve plane segmentation. Then the segmentation result is combined with depth data and color data to apply graph-based image segmentation algorithm. After that, a cascaded decision tree is trained to classify different segments into different semantical type. The method is tested on part of the NYU Depth Dataset. Experimental results show that the proposed method combines the advantages of depth data and the geometry characteristics of the scene, and improves scene segmentation and obstacle detection.
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Wang, Z., Liu, H., Wang, X., Qian, Y. (2014). Segment and Label Indoor Scene Based on RGB-D for the Visually Impaired. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8325. Springer, Cham. https://doi.org/10.1007/978-3-319-04114-8_38
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DOI: https://doi.org/10.1007/978-3-319-04114-8_38
Publisher Name: Springer, Cham
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