Deep Human Parsing with Active Template Regression
作者： Xiaodan Liang , Si Liu , Xiaohui Shen , Jianchao Yang , Luoqi Liu
DOI: 10.1109/TPAMI.2015.2408360
关键词: Parsing 、 Artificial intelligence 、 Normalization (statistics) 、 Pixel 、 Artificial neural network 、 Smoothing 、 Computer science 、 Body region 、 Convolutional neural network 、 Feature extraction 、 Shape analysis (digital geometry) 、 Pattern recognition
摘要: In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an active template regression (ATR) problem, where the normalized mask of each fashion/body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human …
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