Layered Object Detection for Multi-Class Segmentation

author: Sam Hallman, Department of Computer Science, University of California, Irvine
published: July 19, 2010,   recorded: June 2010,   views: 6484
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Description

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.

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Download slides icon Download slides: cvpr2010_hallman_lodm_01.ppt (10.6 MB)

Download article icon Download article: cvpr2010_hallman_lodm_01.pdf (6.9 MB)


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Comment1 tom chen, July 22, 2010 at 2:25 a.m.:

Sam seems much more intelligent than his dad.

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