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- W2895574518 abstract "Channel mismatch (the result of swapping left and right views) is a 3D-video artifact that can cause major viewer discomfort. This work presents a novel high-accuracy method of channel-mismatch detection. In addition to the features described in our previous work, we introduce a new feature based on a convolutional neural network; it predicts channel-mismatch probability on the basis of the stereoscopic views and corresponding disparity maps. A logistic-regression model trained on the described features makes the final prediction. We tested this model on a set of 900 stereoscopic-video scenes, and it outperformed existing channel-mismatch detection methods that previously served in analyses of full-length stereoscopic movies." @default.
- W2895574518 created "2018-10-12" @default.
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- W2895574518 date "2018-06-01" @default.
- W2895574518 modified "2023-09-26" @default.
- W2895574518 title "CHANNEL-MISMATCH DETECTION ALGORITHM FOR STEREOSCOPIC VIDEO USING CONVOLUTIONAL NEURAL NETWORK" @default.
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- W2895574518 doi "https://doi.org/10.1109/3dtv.2018.8478542" @default.
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