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- W2035379962 abstract "Stereo matching has been used for face recognition in the presence of pose variation. In this approach, stereo matching is used to compare two 2-D images based on correspondences that reflect the effects of viewpoint variation and allow for occlusion. We show how to use stereo matching to derive image descriptors that can be used to train a classifier. This improves face recognition performance, producing the best published results on the CMU PIE dataset. We also demonstrate that classification based on stereo matching can be used for general object classification in the presence of pose variation. In preliminary experiments we show promising results on the 3D object class dataset, a standard, challenging 3D classification data set." @default.
- W2035379962 created "2016-06-24" @default.
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- W2035379962 date "2011-11-01" @default.
- W2035379962 modified "2023-10-18" @default.
- W2035379962 title "Trainable 3D recognition using stereo matching" @default.
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- W2035379962 doi "https://doi.org/10.1109/iccvw.2011.6130301" @default.
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