###################################################################
# #
# 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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Python实现基于多约束和改进纹理特征的带钢表面缺陷显着性检测 (2000个子文件)
svmtrain.c 12KB
imtransform2_c.c 11KB
svmpredict.c 8KB
svm_model_matlab.c 8KB
meanShift1.c 5KB
histc2c.c 4KB
read_sparse.c 4KB
nlfiltersep_sum.c 2KB
nlfiltersep_max.c 2KB
assignToBins1.c 2KB
ktHistcRgb_c.c 2KB
ktComputeW_c.c 2KB
fernsInds1.c 1KB
svm.cpp 86KB
fibheap.cpp 20KB
gradientMex.cpp 18KB
edgeBoxesMex.cpp 18KB
spDetectMex.cpp 15KB
convConst.cpp 10KB
edgesDetectMex.cpp 9KB
rgbConvertMex.cpp 8KB
imResampleMex.cpp 8KB
dijkstra1.cpp 6KB
imPadMex.cpp 5KB
acfDetect1.cpp 4KB
forestFindThr.cpp 4KB
mexFeatureDistance.cpp 3KB
opticalFlowHsMex.cpp 3KB
binaryTreeTrain1.cpp 3KB
chnsTestCpp.cpp 2KB
edgesNmsMex.cpp 2KB
forestInds.cpp 2KB
mexLBP.cpp 2KB
menu.css 1KB
m2html.css 1KB
m2html.css 1KB
m2html.css 1KB
m2html.css 1002B
m2html.css 1002B
svm.h 4KB
fibheap.h 3KB
svm_model_matlab.h 201B
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
behaviorData.html 7KB
imRectRot.html 6KB
menu.html 6KB
fevalDistr.html 6KB
bbGt.html 6KB
convTri.html 6KB
menu.html 6KB
gradientHist.html 6KB
imagesAlign.html 5KB
bbNms.html 5KB
Contents.html 5KB
binaryTreeTrain.html 5KB
nonMaxSupr.html 5KB
hog.html 5KB
opticalFlow.html 5KB
meanShiftIm.html 5KB
fevalImages.html 5KB
adaBoostTrain.html 5KB
rgbConvert.html 5KB
ticStatus.html 5KB
fernsRegTrain.html 5KB
rbfComputeBasis.html 5KB
seqIo.html 5KB
imtransform2.html 5KB
jitterImage.html 5KB
overview.html 5KB
overview.html 5KB
imagesAlignSeq.html 5KB
fhog.html 5KB
maskGaussians.html 5KB
kmeans2.html 5KB
histcImLoc.html 5KB
kernelTracker.html 5KB
histc2.html 5KB
bbApply.html 4KB
imwrite2.html 4KB
forestTrain.html 4KB
convBox.html 4KB
montage2.html 4KB
rotationMatrix.html 4KB
pca.html 4KB
imMlGauss.html 4KB
Contents.html 4KB
chnsScaling.html 4KB
textureMap.html 4KB
meanShift.html 4KB
fevalArrays.html 4KB
pdist2.html 4KB
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