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Parallelization of a color-entropy preprocessed ChanVese model for face contour detection on multi-core CPU and GPU

Xiaohua Shi Fredrick Park Lina Wang Jack Xin Yingyong Qi

Geometric Modeling and Processing mathscidoc:1912.43900

Parallel Computing, 49, 28-49, 2015.11
Face tracking is an important computer vision technology that has been widely adopted in many areas, from cell phone applications to industry robots. In this paper, we introduce a novel way to parallelize a face contour detecting application based on the color-entropy preprocessed ChanVese model utilizing a total variation G-norm. This particular application is a complicated and unsupervised computational method requiring a large amount of calculations. Several core parts therein are difficult to parallelize due to heavily correlated data processing among iterations and pixels.
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@inproceedings{xiaohua2015parallelization,
  title={Parallelization of a color-entropy preprocessed ChanVese model for face contour detection on multi-core CPU and GPU},
  author={Xiaohua Shi, Fredrick Park, Lina Wang, Jack Xin, and Yingyong Qi},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210529351825464},
  booktitle={Parallel Computing},
  volume={49},
  pages={28-49},
  year={2015},
}
Xiaohua Shi, Fredrick Park, Lina Wang, Jack Xin, and Yingyong Qi. Parallelization of a color-entropy preprocessed ChanVese model for face contour detection on multi-core CPU and GPU. 2015. Vol. 49. In Parallel Computing. pp.28-49. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210529351825464.
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