Alexander Freytag
962eb0a405
clean-up, code adaptation towards matlab-suitable data sizes, code commentations, stable version
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11 anni fa | |
|---|---|---|
| COPYING | 12 anni fa | |
| Makefile | 12 anni fa | |
| README | 11 anni fa | |
| compileFelzenszwalbSegmentation.m | 11 anni fa | |
| convolve.h | 12 anni fa | |
| disjoint-set.h | 12 anni fa | |
| filter.h | 12 anni fa | |
| image.h | 12 anni fa | |
| imconv.h | 12 anni fa | |
| imutil.h | 12 anni fa | |
| misc.h | 12 anni fa | |
| pnmfile.h | 12 anni fa | |
| segment-graph.h | 11 anni fa | |
| segment-image-labelOutput.h | 11 anni fa | |
| segment-image.h | 11 anni fa | |
| segment.cpp | 12 anni fa | |
| segmentFelzenszwalb.cpp | 11 anni fa | |
| segmentFelzenszwalb.m | 12 anni fa |
README
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 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!