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Alexander Freytag f53f1fbc34 added default values for input variables 12 years ago
COPYING 9a56179e76 added felzenszwalb code 12 years ago
Makefile 9a56179e76 added felzenszwalb code 12 years ago
README 9a56179e76 added felzenszwalb code 12 years ago
convolve.h 9a56179e76 added felzenszwalb code 12 years ago
disjoint-set.h 9a56179e76 added felzenszwalb code 12 years ago
filter.h 9a56179e76 added felzenszwalb code 12 years ago
image.h 9a56179e76 added felzenszwalb code 12 years ago
imconv.h 9a56179e76 added felzenszwalb code 12 years ago
imutil.h 9a56179e76 added felzenszwalb code 12 years ago
misc.h 9a56179e76 added felzenszwalb code 12 years ago
pnmfile.h 9a56179e76 added felzenszwalb code 12 years ago
segment-graph.h 9a56179e76 added felzenszwalb code 12 years ago
segment-image.h 9a56179e76 added felzenszwalb code 12 years ago
segment.cpp 9a56179e76 added felzenszwalb code 12 years ago
segmentFelzenszwalb.cpp f53f1fbc34 added default values for input variables 12 years ago
segmentFelzenszwalb.m 836d1e0baa initial version of mex interface for felzenszwalb segm 12 years ago

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.