A Data-Driven Regularization Model for Stereo and Flow
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2015-12-15T02:48:28Z.
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| Subjects: | |
| Online Access: | Get fulltext |