Glass object segmentation by label transfer on joint depth and appearance manifolds
We address the glass object localization problem with a RGB-D camera. Our approach uses a nonparametric, data-driven label transfer scheme for local glass boundary estimation. A weighted voting scheme based on a joint feature manifold is adopted to integrate depth and appearance cues, and we learn a distance metric on the depth-encoded feature manifold. Local boundary evidence is then integrated into a MRF framework for spatially coherent glass object detection and segmentation. The efficacy of...[Show more]
|Collections||ANU Research Publications|
|Source:||2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings|
|01_Wang_Glass_object_segmentation_by_2013.pdf||2.79 MB||Adobe PDF||Request a copy|
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