Layered Object Detection for Multi-Class Segmentation

Yi Yang, Sam Hallman, Deva Ramanan, Charless C. Fowlkes

CVPR 2010 pp. 3113-3120

doi:10.1109/CVPR.2010.5540070 /cvpr/2010/yang2010cvpr-layered/

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.

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Cite

Text

Yang et al. "Layered Object Detection for Multi-Class Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5540070

Markdown

[Yang et al. "Layered Object Detection for Multi-Class Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/yang2010cvpr-layered/) doi:10.1109/CVPR.2010.5540070

BibTeX

@inproceedings{yang2010cvpr-layered,
  title     = {{Layered Object Detection for Multi-Class Segmentation}},
  author    = {Yang, Yi and Hallman, Sam and Ramanan, Deva and Fowlkes, Charless C.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2010},
  pages     = {3113-3120},
  doi       = {10.1109/CVPR.2010.5540070},
  url       = {https://mlanthology.org/cvpr/2010/yang2010cvpr-layered/}
}