Third-Order Edge Detection

This is a OpenMP C++ and CUDA GPU implementation of Third-Order Edge Detection (TOED) from the paper:
Kimia, Benjamin B., Xiaoyan Li, Yuliang Guo, and Amir Tamrakar. "Differential geometry in edge detection: accurate estimation of position, orientation and curvature." IEEE transactions on pattern analysis and machine intelligence 41, no. 7 (2018): 1573-1586.

The matlab code of the paper can be found in Yuliang's github page.

0. Updates

Jul. 23, 2023: The curvel formation code is also included in thie repo for completeness. It only accepts double precision third-order edges for now. Single precision will be made available for curvel formation in the near future.

1. Dependencies

The code has been tested in Linux-based system with the following versions of dependencies:
(1) g++ version 10.2 or higher
(2) cuda/11.1.1 or higher
Other system or lower version of gcc might work but not tested yet. Cuda version depends on the GPU. Please have it checked to use the correct cuda version.

2. Run the code

Under the repo directory, simply do

make toed

Then, run the execution with input arguments

./TOED <name_of_input_image> <number of CPU threads> <gpu id>

The argument <name_of_input_image> is mandatory while the other two are optional. So far only .pgm image file is accepted. One sample image is provided under folder ./input_images/, so you can, for example, run the code using:

./TOED ./input_images/2018.pgm 4

3. Display edges and orientations

After a successful run, lists of subpixel edges are written in text files named data_final_output_cpu.txt under ./test_files/. You can use the matlab file in ./draw_edges_by_matlab/draw_edges_from_list.m to plot the edges of the input image, or ./draw_edges_by_matlab/draw_edges_orient_from_list.m to plot the edges and its orientations of the input image.

4. Some test results

CPU: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
GPU: NVIDIA QuadroRTX 6000
Double Precision Test:
==> CPU Test (OpenMP 4 threads)
============================================

• Time of image convolution (OpenMP): 261.332 (ms)
  
• Time of NMS (OpenMP): 2.62396 (ms)
  
  

==> GPU Test
=============================================

• GPU Convolution time = 16.5035 ms
  
• GPU NMS time = 0.2988 ms
  
  

Single Precision Test:
==> CPU Test (OpenMP 4 threads)
=============================================

• Time of image convolution (OpenMP): 257.492 (ms)
  
• Time of NMS (OpenMP): 2.32577 (ms)
  
  

==> GPU Test
=============================================

• GPU Convolution time = 0.7848 ms
  
• GPU NMS time = 0.2951 ms
  

Note that the above timings could change according to the input image size, the power of CPU/GPU.