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README - A-KAZE Features

Version: 1.5.0 Date: 11-12-2014

You can get the latest version of the code from github: https://github.com/pablofdezalc/akaze

OTHER IMPLEMENTATIONS

You can try the CUDA A-KAZE implementation from Niklas Bergström: https://github.com/nbergst/akaze

There is also a very fast CPU A-KAZE version from Hideaki Suzuki https://github.com/h2suzuki/fast_akaze

CHANGELOG

Version: 1.5.0 Changes:

Version: 1.4.0 Changes:

Version: 1.3.0 Changes:

Version: 1.2.0 Changes:

Version: 1.1.0 Changes:

What is this file?

This file explains how to make use of source code for computing A-KAZE features and two practical image matching applications

Library Dependencies

The code is mainly based on the OpenCV library using the C++ interface

In order to compile the code, the following libraries to be installed on your system:

If you want to use OpenMP parallelization you will need to install OpenMP in your system In Linux you can do this by installing the gomp library

You will also need doxygen in case you need to generate the documentation

Tested compilers:

Tested systems:

Getting Started

Compiling:

  1. $ mkdir build
  2. $ cd build>
  3. $ cmake ..
  4. $ make

Additionally you can also install the library in /usr/local/akaze/lib by typing: $ sudo make install

If the compilation is successful you should see three executables in the folder bin:

Additionally, the library libAKAZE[.a, .lib] will be created in the folder lib.

If there is any error in the compilation, perhaps some libraries are missing. Please check the Library dependencies section.

Examples: To see how the code works, examine the three examples provided.

MATLAB interface

A mex interface for computing AKAZE features is supplied, in the file mex/akaze.cpp.

To be able to use it, first compile the library as explained above. Then, you will need to compile the mex from Matlab.

The following is an example for compiling the mex on Windows 64 bit, Visual Studio 10 and OpenCV 2.4.8. from the mex folder, type in MATLAB:

mex akaze.cpp -I'..\src\lib\' -L'..\build\lib\Release\' -I'<path_to_opencv>\build\include' -L'<path_to_opencv>\build\x64\vc10\lib' -lopencv_calib3d248 -lopencv_contrib248 -lopencv_core248 -lopencv_highgui248 -lopencv_imgproc248 -lAKAZE

The following is an example for compiling the mex on Linux, Ubuntu 12.10, 64 bit, gcc 4.6.4 and OpenCV 2.4.8. from the mex folder, type in MATLAB:

For other platforms / compilers / OpenCV versions, change the above line accordingly. mex akaze.cpp -I'../src/lib/' -L'../build/lib/' -lAKAZE -L'/usr/local/lib/' -lopencv_imgproc -lopencv_core -lopencv_calib3d -lopencv_highgui -lgomp

On Windows, you'll need to make sure that the corresponding OpenCV bin folder is added to your system path before staring MATLAB. e.g.:

PATH=<path_to_opencv>\build\x64\vc10\bin

Once the mex file is compiled successfully, type:

akaze

to display function help.

Documentation

In the working folder, type: doxygen

The documentation will be generated in the ./doc folder

Computing A-KAZE Features

For running the program you need to type in the command line the following arguments: ./akaze_features img.jpg [options]

The [options] are not mandatory. In case you do not specify additional options, default arguments will be used. Here is a description of the additional options:

Important Things:

Image Matching Example with A-KAZE Features

The code contains one program to perform image matching between two images. If the ground truth transformation is not provided, the program estimates a fundamental matrix or a planar homography using RANSAC between the set of correspondences between the two images.

For running the program you need to type in the command line the following arguments: ./akaze_match img1.jpg img2.pgm homography.txt [options]

The [options] are not mandatory. In case you do not specify additional options, default arguments will be used.

The datasets folder contains the Iguazu dataset described in the paper and additional datasets from Mikolajczyk et al. evaluation. The Iguazu dataset was generated by adding Gaussian noise of increasing standard deviation.

For example, with the default configuration parameters used in the current code version you should get the following results:

./akaze_match ../../datasets/iguazu/img1.pgm 
              ../../datasets/iguazu/img4.pgm 
              ../../datasets/iguazu/H1to4p
              --descriptor 4

Number of Keypoints Image 1: 1823
Number of Keypoints Image 2: 2373
A-KAZE Features Extraction Time (ms): 411.231
Matching Descriptors Time (ms): 19.5631
Number of Matches: 1283
Number of Inliers: 1136
Number of Outliers: 147
Inliers Ratio: 88.5425

Image Matching Comparison between A-KAZE, ORB and BRISK (OpenCV)

The code contains one program to perform image matching between two images, showing a comparison between A-KAZE features, ORB and BRISK. All these implementations are based on the OpenCV library.

The program assumes that the ground truth transformation is provided

For running the program you need to type in the command line the following arguments:

./akaze_compare img1.jpg img2.pgm homography.txt [options]

For example, running kaze_compare with the first and third images from the boat dataset you should get the following results:

./akaze_compare ../../datasets/boat/img1.pgm 
                ../../datasets/boat/img3.pgm
                ../../datasets/boat/H1to3p
                --dthreshold 0.004

ORB Results
**************************************
Number of Keypoints Image 1: 1511
Number of Keypoints Image 2: 1517
Number of Matches: 489
Number of Inliers: 469
Number of Outliers: 20
Inliers Ratio: 95.91
ORB Features Extraction Time (ms): 272.932

BRISK Results
**************************************
Number of Keypoints Image 1: 3449
Number of Keypoints Image 2: 3028
Number of Matches: 158
Number of Inliers: 116
Number of Outliers: 42
Inliers Ratio: 73.4177
BRISK Features Extraction Time (ms): 188.407

A-KAZE Results
**************************************
Number of Keypoints Image 1: 2129
Number of Keypoints Image 2: 1668
Number of Matches: 764
Number of Inliers: 688
Number of Outliers: 76
Inliers Ratio: 90.0524
A-KAZE Features Extraction Time (ms): 292.35

A-KAZE features is open source and you can use that freely even in commercial applications. The code is released under BSD license. While A-KAZE is a bit slower compared to ORB and BRISK, it provides much better performance. In addition, for images with small resolution such as 640x480 the algorithm can run in real-time. In the next future we plan to release a GPGPU implementation.

Citation

If you use this code as part of your work, please cite the following papers:

  1. Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, J. Nuevo and Adrien Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013

  2. KAZE Features. Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision (ECCV), Fiorenze, Italy, October 2012

Contact Info

Important: If you work in a research institution, university, company or you are a freelance and you are using KAZE or A-KAZE in your work, please send me an email!! I would like to know the people that are using KAZE and A-KAZE around the world!!"

In case you have any question, find any bug in the code or want to share some improvements, please contact me:

Pablo F. Alcantarilla email: pablofdezalc@gmail.com