Alexander Freytag
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| README | před 12 roky | |
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| segment-image-labelOutput.h | před 12 roky | |
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README
==================== C++ =================================
Implementation of the segmentation algorithm described in:
Efficient Graph-Based Image Segmentation
Pedro F. Felzenszwalb and Daniel P. Huttenlocher
International Journal of Computer Vision, 59(2) September 2004.
The program [segment.cpp, note by A. Freytag] takes a color image (PPM format) and produces a segmentation
with a random color assigned to each region.
1) Type "make" to compile "segment".
2) Run "segment sigma k min input output".
The parameters are: (see the paper for details)
sigma: Used to smooth the input image before segmenting it.
k: Value for the threshold function.
min: Minimum component size enforced by post-processing.
input: Input image.
output: Output image.
Typical parameters are sigma = 0.5, k = 500, min = 20.
Larger values for k result in larger components in the result.
NOTE ( by Alexander Freytag )
- only images with less then std::numeric_limits::max() pixels are supported properly!
=============== MATLAB ==============
[Compilation]
- run compileFelzenszwalbSegmentation.m
[Demo]
- run demoFelzenszwalbSegmentation.m (requires matlabs GUI to show images and segmentation results)
- demo file shows how to setup variables, and how to call the underlying mex functions
Implementation of the segmentation algorithm described in:
Efficient Graph-Based Image Segmentation
Pedro F. Felzenszwalb and Daniel P. Huttenlocher
International Journal of Computer Vision, 59(2) September 2004.
The program [segment.cpp, note by A. Freytag] takes a color image (PPM format) and produces a segmentation
with a random color assigned to each region.
1) Type "make" to compile "segment".
2) Run "segment sigma k min input output".
The parameters are: (see the paper for details)
sigma: Used to smooth the input image before segmenting it.
k: Value for the threshold function.
min: Minimum component size enforced by post-processing.
input: Input image.
output: Output image.
Typical parameters are sigma = 0.5, k = 500, min = 20.
Larger values for k result in larger components in the result.
NOTE ( by Alexander Freytag )
- only images with less then std::numeric_limits::max() pixels are supported properly!
=============== MATLAB ==============
[Compilation]
- run compileFelzenszwalbSegmentation.m
[Demo]
- run demoFelzenszwalbSegmentation.m (requires matlabs GUI to show images and segmentation results)
- demo file shows how to setup variables, and how to call the underlying mex functions