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In this paper, we propose to use contexts of superpixels as a prior to improve semantic segmentation by the CRF framework. A graphical model is constructed on over-segmented images. Our main contribution is to take the concept of “superpixel embedding” into consideration, which is formalized as a potential item for optimizing the energy of the whole graph. We also introduce two ways of calculating...
We present a structured learning approach to semantic annotation of RGB-D images. Our method learns to reason about spatial relations of objects and fuses low-level class predictions to a consistent interpretation of a scene. Our model incorporates color, depth and 3D scene features, on which an energy function is learned to directly optimize object class prediction using the loss-based maximum-margin...
Semantic interpretation of the environment can significantly improve the capabilities of our autonomous robots. This work is focused on automatic semantic label propagation in video of indoor environments acquired by a mobile robot. Using a small number of training examples, we propose a new approach to recognize and label dominant background regions of interest, such as floor, wall and doors, and...
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