Layered Object Detection for Multi-Class Segmentation

被引：34

作者：

Yang, Yi ^{[1
]}

Hallman, Sam ^{[1
]}

Ramanan, Deva ^{[1
]}

Fowlkes, Charless ^{[1
]}

机构：

[1] Univ Calif Irvine, Dept Comp Sci, Irvine, CA 92717 USA

来源：

2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2010年

关键词：

D O I：

10.1109/CVPR.2010.5540070

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We formulate a layered model for object detection and multi-class segmentation. Our system uses the output of a bank of object detectors in order to define shape priors for support masks and then estimates appearance, depth ordering and labeling of pixels in the image. We train our system on the PASCAL segmentation challenge dataset and show good test results with state of the art performance in several categories including segmenting humans.

引用

页码：3113 / 3120

页数：8

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