A Data-Driven Regularization Model for Stereo and Flow

3DV, pp. 277-284, 2014.

Cited by: 18|Views23
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Abstract:

Data-driven techniques can reliably build semantic correspondence among images. In this paper, we present a new regularization model for stereo or flow through transferring the shape information of the disparity or flow from semantically matched patches in the training database. Compared to previous regularization models based on image ap...More

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