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• W2083542343 abstract "We formulate a layered model for object detection and image segmentation. We describe a generative probabilistic model that composites the output of a bank of object detectors in order to define shape masks and explain the appearance, depth ordering, and labels of all pixels in an image. Notably, our system estimates both class labels and object instance labels. Building on previous benchmark criteria for object detection and image segmentation, we define a novel score that evaluates both class and instance segmentation. We evaluate our system on the PASCAL 2009 and 2010 segmentation challenge data sets and show good test results with state-of-the-art performance in several categories, including segmenting humans." @default.
• W2083542343 created "2016-06-24" @default.
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• W2083542343 date "2012-09-01" @default.
• W2083542343 modified "2023-10-12" @default.
• W2083542343 title "Layered Object Models for Image Segmentation" @default.
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• W2083542343 doi "https://doi.org/10.1109/tpami.2011.208" @default.
• W2083542343 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22813957" @default.
• W2083542343 hasPublicationYear "2012" @default.
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