Sven Sickert a9c5c11eb0 fixed 'multiple defintions' in felzenszwalb segmentation 8 anos atrás
..
COPYING 2fd42714e0 initialization 12 anos atrás
Makefile 2fd42714e0 initialization 12 anos atrás
README 2fd42714e0 initialization 12 anos atrás
convolve.h 2fd42714e0 initialization 12 anos atrás
disjoint-set.cpp a9c5c11eb0 fixed 'multiple defintions' in felzenszwalb segmentation 8 anos atrás
disjoint-set.h a9c5c11eb0 fixed 'multiple defintions' in felzenszwalb segmentation 8 anos atrás
filter.h a9c5c11eb0 fixed 'multiple defintions' in felzenszwalb segmentation 8 anos atrás
image.h 2fd42714e0 initialization 12 anos atrás
imconv.h 2fd42714e0 initialization 12 anos atrás
imutil.h 2fd42714e0 initialization 12 anos atrás
misc.h 2fd42714e0 initialization 12 anos atrás
pnmfile.h 2fd42714e0 initialization 12 anos atrás
segment-graph.h a9c5c11eb0 fixed 'multiple defintions' in felzenszwalb segmentation 8 anos atrás
segment-image.h a9c5c11eb0 fixed 'multiple defintions' in felzenszwalb segmentation 8 anos atrás
segment.cpp 8137977a2f fixed namespace usage 11 anos atrás

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.