###################################################################
# #
# Structured Edge Detection Toolbox V3.0 #
# Piotr Dollar (pdollar-at-microsoft.com) #
# #
###################################################################
1. Introduction.
Very fast edge detector (up to 60 fps depending on parameter settings) that achieves excellent accuracy. Can serve as input to any vision algorithm requiring high quality edge maps. Toolbox also includes the Edge Boxes object proposal generation method and fast superpixel code.
If you use the Structured Edge Detection Toolbox, we appreciate it if you cite an appropriate subset of the following papers:
@inproceedings{DollarICCV13edges,
author = {Piotr Doll\'ar and C. Lawrence Zitnick},
title = {Structured Forests for Fast Edge Detection},
booktitle = {ICCV},
year = {2013},
}
@article{DollarARXIV14edges,
author = {Piotr Doll\'ar and C. Lawrence Zitnick},
title = {Fast Edge Detection Using Structured Forests},
journal = {ArXiv},
year = {2014},
}
@inproceedings{ZitnickECCV14edgeBoxes,
author = {C. Lawrence Zitnick and Piotr Doll\'ar},
title = {Edge Boxes: Locating Object Proposals from Edges},
booktitle = {ECCV},
year = {2014},
}
###################################################################
2. License.
This code is published under the MSR-LA Full Rights License.
Please read license.txt for more info.
###################################################################
3. Installation.
a) This code is written for the Matlab interpreter (tested with versions R2013a-2013b) and requires the Matlab Image Processing Toolbox.
b) Additionally, Piotr's Matlab Toolbox (version 3.26 or later) is also required. It can be downloaded at:
http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html.
c) Next, please compile mex code from within Matlab (note: win64/linux64 binaries included):
mex private/edgesDetectMex.cpp -outdir private [OMPPARAMS]
mex private/edgesNmsMex.cpp -outdir private [OMPPARAMS]
mex private/spDetectMex.cpp -outdir private [OMPPARAMS]
mex private/edgeBoxesMex.cpp -outdir private
Here [OMPPARAMS] are parameters for OpenMP and are OS and compiler dependent.
Windows: [OMPPARAMS] = '-DUSEOMP' 'OPTIMFLAGS="$OPTIMFLAGS' '/openmp"'
Linux V1: [OMPPARAMS] = '-DUSEOMP' CFLAGS="\$CFLAGS -fopenmp" LDFLAGS="\$LDFLAGS -fopenmp"
Linux V2: [OMPPARAMS] = '-DUSEOMP' CXXFLAGS="\$CXXFLAGS -fopenmp" LDFLAGS="\$LDFLAGS -fopenmp"
To compile without OpenMP simply omit [OMPPARAMS]; note that code will be single threaded in this case.
d) Add edge detection code to Matlab path (change to current directory first):
>> addpath(pwd); savepath;
e) Finally, optionally download the BSDS500 dataset (necessary for training/evaluation):
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/
After downloading BSR/ should contain BSDS500, bench, and documentation.
f) A fully trained edge model for RGB images is available as part of this release. Additional models are available online, including RGBD/D/RGB models trained on the NYU depth dataset and a larger more accurate BSDS model.
###################################################################
4. Getting Started.
- Make sure to carefully follow the installation instructions above.
- Please see "edgesDemo.m", "edgeBoxesDemo" and "spDemo.m" to run demos and get basic usage information.
- For a detailed list of functionality see "Contents.m".
###################################################################
5. History.
Version 3.01 (09/08/2014)
- spAffinities: minor fix (memory initialization)
- edgesDetect: minor fix (multiscale / multiple output case)
Version 3.0 (07/23/2014)
- added Edge Boxes code corresponding to ECCV paper
- added Sticky Superpixels code
- edge detection code unchanged
Version 2.0 (06/20/2014)
- second version corresponding to arXiv paper
- added sharpening option
- added evaluation and visualization code
- added NYUD demo and sweep support
- various tweaks/improvements/optimizations
Version 1.0 (11/12/2013)
- initial version corresponding to ICCV paper
###################################################################
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epicflowepicflowepicflow 源码
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epicflowepicflowepicflow 源码 (798个子文件)
image.c 22KB
solver.c 20KB
variational_aux.c 15KB
io.c 12KB
imtransform2_c.c 11KB
variational.c 6KB
meanShift1.c 5KB
histc2c.c 4KB
nlfiltersep_sum.c 2KB
nlfiltersep_max.c 2KB
assignToBins1.c 2KB
ktHistcRgb_c.c 2KB
ktComputeW_c.c 2KB
fernsInds1.c 1KB
Changelog 2KB
COPYING 34KB
conv.cpp 32KB
deep_matching.cpp 31KB
maxfilter.cpp 27KB
hog.cpp 24KB
fibheap.cpp 20KB
gradientMex.cpp 18KB
edgeBoxesMex.cpp 18KB
epic_aux.cpp 16KB
spDetectMex.cpp 15KB
main.cpp 10KB
io.cpp 10KB
convConst.cpp 10KB
edgesDetectMex.cpp 9KB
epic.cpp 9KB
rgbConvertMex.cpp 8KB
imResampleMex.cpp 8KB
image.cpp 6KB
dijkstra1.cpp 6KB
epicflow.cpp 6KB
imPadMex.cpp 5KB
acfDetect1.cpp 4KB
forestFindThr.cpp 4KB
pixel_desc.cpp 3KB
opticalFlowHsMex.cpp 3KB
binaryTreeTrain1.cpp 3KB
chnsTestCpp.cpp 2KB
edgesNmsMex.cpp 2KB
forestInds.cpp 2KB
menu.css 1KB
m2html.css 1KB
m2html.css 1KB
m2html.css 1KB
m2html.css 1002B
m2html.css 1002B
Thumbs.db 27KB
deepmatching-static 3.13MB
epicflow-static 2.1MB
flow.flo 253KB
simulinkicon.gif 977B
simulinkicon.gif 977B
simulinkicon.gif 977B
simulinkicon.gif 977B
simulinkicon.gif 977B
matlabicon.gif 574B
matlabicon.gif 574B
matlabicon.gif 574B
matlabicon.gif 574B
matlabicon.gif 574B
demoicon.gif 214B
demoicon.gif 214B
demoicon.gif 214B
demoicon.gif 214B
demoicon.gif 214B
new.gif 116B
new.gif 116B
GPL 15KB
array_types.h 7KB
conv.h 5KB
array_types.h 4KB
deep_matching.h 4KB
maxfilter.h 4KB
fibheap.h 3KB
image.h 3KB
std.h 3KB
epic_aux.h 3KB
image.h 2KB
hog.h 2KB
variational_aux.h 2KB
epic.h 2KB
pixel_desc.h 1KB
variational.h 1KB
io.h 1KB
io.h 839B
solver.h 434B
sse.hpp 3KB
wrappers.hpp 2KB
Contents.html 10KB
acfTrain.html 9KB
chnsCompute.html 9KB
chnsPyramid.html 9KB
menu.html 8KB
Contents.html 8KB
acfReadme.html 8KB
Contents.html 8KB
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- 飞翔在蓝天2017-10-16博主,这个可以再windows下运行么?一运行有错误
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