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Project Description

PyCV is a package of C++ and Python modules implementing various algorithms that are useful in computer vision, and augments the capabilities of OpenCV. In particular, PyCV provides implementations for:

  • Fast training and selection of Haar-like features for a weak classifier [Pham2007b]. This is currently the world’s fastest method for training a face detector. It runs in just a few hours, while most existing methods run in days or weeks.
  • Asymmetric Online Boosting [Pham2007a]: a variant of AdaBoost that learns incrementally using an asymmetric goal as the learning criterion.

Additionally, PyCV contains many useful modules for computer vision and machine learning, specially boosting techniques, Haar-like features, and face detection.

The package is primarily developed by Minh-Tri Pham, as part of his PhD research on face detection. This research is being carried out in the Centre for Multimedia & Network Technology (CeMNet), School of Computer Engineering, Nanyang Technological University, Singapore.

Copyright 2007 Nanyang Technological University, Singapore.

Founding Contributors:
 

Minh-Tri Pham <mtpham@ntu.edu.sg> – Primary author

Viet-Dung D. Hoang <hoan0008@ntu.edu.sg> – Contributing author

Tat-Jen Cham <astjcham@ntu.edu.sg> – Supervising faculty

References

[Pham2007a]Minh-Tri Pham and Tat-Jen Cham. Online Learning Asymmetric Boosted Classifiers for Object Detection. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR‘07), Minneapolis, MN, 2007.
[Pham2007b]Minh-Tri Pham and Tat-Jen Cham. Fast Training and Selection of Haar features using Statistics in Boosting-based Face Detection. In Proc. 11th IEEE International Conference on Computer Vision (ICCV‘07), Rio de Janeiro, Brazil, 2007.
Release History

Release History

0.2.2

This version

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0.2.1

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TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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