Simone Giardino

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Recent local state-of-the-art stereo algorithms based on variable cost aggregation strategies allow for inferring disparity maps comparable to those yielded by algorithms based on global optimization schemes. Unfortunately, thought these results are excellent, they are obtained at the expense of high computational requirements that are comparable or even(More)
We propose a method to perform automatic segmentation of 3D scenes based on a standard classifier, whose learning model is continuously improved by means of new samples, and a grouping stage, that enforces local consistency among classified labels. The new samples are automatically delivered to the system by a feedback loop based on a feature selection(More)
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