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We propose a nonlinear fusion model to optimally reconstruct depth maps from monocular cues. The authors in [1] showed the effectiveness of estimating depth from individual cues, which saves bandwidth without compromising the quality. In this paper, we combine such depth map estimates as a Sensor Fusion problem. These depth map estimates are extracted from color, motion and texture structure of the...
We propose a method to reconstruct the depth map from multiple estimated depth maps relying on monocular cues. Based on extracted depth cues from luminance, chrominance, motion and texture, we obtain an optimal depth estimation by analytically deriving the best combinations. We first analyze a ground truth depth map to extract a set of depth cues. Then, using these depth cues, we process the colored...
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