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This work proposes a general multi-layer framework for image labeling, which targets the challenging problem of classifying the occluded parts of the 3D scene depicted in a 2D image. Our framework is based on the mixed graphical models, which explicitly encode causal relationship between the visible and occluded regions. Unlike other image labeling techniques where a single label is determined for...
Image semantic segmentation is a pixel-wise label assigning problem. Recent image semantic segmentation methods based on Fully Convolutional Networks (FCNs) combined with Conditional Random Fields (CRFs). These systems all employ CRFs with only unary and pairwise potentials, which can merely refine simple-structured delineation situation. In this paper, we propose a more delicate CRF-inference structure...
The paper proposes a fast and accurate semantic segmentation approach for a large Polarimetric SAR (PolSAR) image using Conditional Random Fields (CRFs). It efficiently incorporates the polarimetric signatures, texture and intensity features into a unite CRFs model, and employs a fast max-margin training method for parameters learning. Experiments on RadarSat-2 PolSAR data in Flevoland test site demonstrate...
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