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- W2020565862 abstract "In this paper, a hierarchical model-based approach to frame rate-up conversion is presented. Given a sequence of consecutive video frames, a Spatio-Temporal Conditional Random Field (ST-CRF) is trained to capture both the motion and shape characteristics of objects within consecutive frames. A hierarchical tree is then constructed via hierarchical segmentation that sub-divides frames into regions based on color intensity and regional velocity. A hierarchical sampling approach is then introduced to construct new intermediate frames between adjacent video frames, where estimated intermediate frames are constructed at each level of a hierarchical tree constructed such that the probability of the ST-CRF is maximized. Preliminary results using videos with different motion characteristics show that the proposed approach has potential for producing intermediate frames with high visual quality." @default.
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- W2020565862 date "2011-12-01" @default.
- W2020565862 modified "2023-09-23" @default.
- W2020565862 title "A Novel Hierarchical Model-Based Frame Rate Up-Conversion via Spatio-temporal Conditional Random Fields" @default.
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- W2020565862 doi "https://doi.org/10.1109/ism.2011.44" @default.
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