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• W2116773539 abstract "We present a model of edge and region grouping using a conditional random field built over a scale-invariant representation of images to integrate multiple cues. Our model includes potentials that capture low-level similarity, mid-level curvilinear continuity and high-level object shape. Maximum likelihood parameters for the model are learned from human labeled groundtruth on a large collection of horse images using belief propagation. Using held out test data, we quantify the information gained by incorporating generic mid-level cues and high-level shape." @default.
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• W2116773539 date "2005-12-05" @default.
• W2116773539 modified "2023-09-24" @default.
• W2116773539 title "Cue Integration for Figure/Ground Labeling" @default.
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