High-quality Ellipse Detection

1. Illustration

• This is the source code for the paper Arc-support Line Segments Revisited: An Efficient and High-quality Ellipse Detection. Important: Please use the citation of our IEEE TIP version instead of arXiv version.
• The main contribution of the proposed ellipse detector is to both accurately and efficiently detect ellipses in images, which is universally considered as a tough and long-standing problem in ellipse detection field. The proposed ellipse detector owns the features of high localization accuracy, efficiency, robustness, and stability, which comprehensively yields high-quality ellipse detection performance in front of real-world images.
• There are only two extrinsic parameters, namely the elliptic angular coverage $T_{ac}$ and the ratio of support inliers $T_{r}$, which enables the proposed ellipse detector to be conveniently used and applied in real applications. In addition, the specified_polarity option can help users find the polarity-specific ellipses in the image. The default parameters $T_{ac} = 165^o$ and $T_{r} = 0.6$ are used for comparison experiments in our paper.
• The source code is free for academic use. Please cite our paper if you use the source code, thanks.

2. Requirements

• MATLAB
• OpenCV (Version 2.4.9)
• 64-bit Windows Operating System

3. How to use

• Firstly, compile the file "generateEllipseCandidates.cpp" in MATLAB on your computer to generate the mex file "generateEllipseCandidates.mexw64" with the following command:

  ---

  mex generateEllipseCandidates.cpp -IF:\OpenCV\opencv2.4.9\build\include -IF:\OpenCV\opencv2.4.9\build\include\opencv -IF:\OpenCV\opencv2.4.9\build\include\opencv2 -LF:\OpenCV\opencv2.4.9\build\x64\vc11\lib -IF:\Matlab\settlein\extern\include -LF:\Matlab\settlein\extern\lib\win64\microsoft -lopencv_core249 -lopencv_highgui249 -lopencv_imgproc249 -llibmwlapack.lib

  ---

  Notably, the corresponding software paths of OpenCV and MATLAB, namely the "F:\OpenCV\opencv2.4.9" and "F:\Matlab\settlein", should be replaced to your own.

• Secondly, run the demo file "LCS_ellipse.m".

4. Examples

Some high-quality ellipse detection examples run with default parameters and on the same computer with Intel Core i7-7500U 2.7GHz CPU and 8 GB memory

4.1 Detecting all ellipses in the image

• The number of detected ellipses: 4; Running time: 0.090s; Resolution: 651 x 436

• The number of detected ellipses: 25; Running time: 0.460s; Resolution: 720 x 435

• The number of detected ellipses: 3; Running time: 0.060s; Resolution: 512 x 456

• The number of detected ellipses: 8; Running time: 0.110s; Resolution: 752 x 525

4.2 Detecting the ellipses with positive polarity

• The number of detected ellipses: 4; Running time: 0.080s; Resolution: 752 x 525

4.3 Detecting the ellipses with negative polarity

• The number of detected ellipses: 4; Running time: 0.086s; Resolution: 752 x 525

4.4 Detecting the ellipses sharing different polarity

• The number of detected ellipses: 5; Running time: 0.226s; Resolution: 1000 x 680. ($T_{ac} = 165^{o}$, $T_r = 0.5$)
  

5. Successful Application Cases Up to Now

• Car Wheel Hub Recognition
• PCB Inspection
• Object Fingerprinting
• Robot Vision

6. Citation

@article{lu2019arc,
  title={Arc-Support Line Segments Revisited: An Efficient High-Quality Ellipse Detection},
  author={Lu, Changsheng and Xia, Siyu and Shao, Ming and Fu, Yun},
  journal={IEEE Transactions on Image Processing},
  volume={29},
  pages={768--781},
  year={2020},
  publisher={IEEE}
}

7. Our Previous Work

We also proposed a circle detection method in our previous work which could detect circles from image efficiently, precisely and robustly.