Alexander Freytag
8ab6d02b44
changes on readme file
|
%!s(int64=11) %!d(string=hai) anos | |
|---|---|---|
| data | %!s(int64=11) %!d(string=hai) anos | |
| COPYING | %!s(int64=12) %!d(string=hai) anos | |
| Makefile | %!s(int64=12) %!d(string=hai) anos | |
| README | %!s(int64=11) %!d(string=hai) anos | |
| compileFelzenszwalbSegmentation.m | %!s(int64=11) %!d(string=hai) anos | |
| convolve.h | %!s(int64=12) %!d(string=hai) anos | |
| demoFelzenszwalbSegmentation.m | %!s(int64=11) %!d(string=hai) anos | |
| disjoint-set.h | %!s(int64=12) %!d(string=hai) anos | |
| filter.h | %!s(int64=12) %!d(string=hai) anos | |
| image.h | %!s(int64=12) %!d(string=hai) anos | |
| imconv.h | %!s(int64=12) %!d(string=hai) anos | |
| imutil.h | %!s(int64=12) %!d(string=hai) anos | |
| misc.h | %!s(int64=12) %!d(string=hai) anos | |
| pnmfile.h | %!s(int64=12) %!d(string=hai) anos | |
| segment-graph.h | %!s(int64=11) %!d(string=hai) anos | |
| segment-image-labelOutput.h | %!s(int64=11) %!d(string=hai) anos | |
| segment-image.h | %!s(int64=11) %!d(string=hai) anos | |
| segment.cpp | %!s(int64=12) %!d(string=hai) anos | |
| segmentFelzenszwalb.cpp | %!s(int64=11) %!d(string=hai) anos | |
| segmentFelzenszwalb.m | %!s(int64=12) %!d(string=hai) anos |
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