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

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.