Layered Object Detection for Multi-Class Segmentation
Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, pp. 3113 - 3120, June, 2010
Abstract
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.
BibTeX
@conference{Yang-2010-121219,author = {Yi Yang and Sam Hallman and Deva Ramanan and Charless Fowlkes},
title = {Layered Object Detection for Multi-Class Segmentation},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {2010},
month = {June},
pages = {3113 - 3120},
}
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.