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github | juliacamps/OR-master | vl_test_dsift.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_dsift.m | 2,048 | utf_8 | fbbfb16d5a21936c1862d9551f657ccc | function results = vl_test_dsift(varargin)
% VL_TEST_DSIFT
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = rgb2gray(single(I)) ;
function test_fast_slow(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize... |
github | juliacamps/OR-master | vl_test_alldist2.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_alldist2.m | 2,284 | utf_8 | 89a787e3d83516653ae8d99c808b9d67 | function results = vl_test_alldist2(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
% TODO: test integer classes
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist2(zeros(15,12), zeros(15,0), 'kl2'), ...
... |
github | juliacamps/OR-master | vl_test_imsmooth.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_imsmooth.m | 1,837 | utf_8 | 718235242cad61c9804ba5e881c22f59 | function results = vl_test_imsmooth(varargin)
% VL_TEST_IMSMOOTH
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
I = max(min(vl_imdown(I),1),0) ;
s.I = single(I) ;
function test_pad_by_continuity(s)
% Convolving a constant signal padded with continuity does not change... |
github | juliacamps/OR-master | vl_test_phow.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_phow.m | 549 | utf_8 | f761a3bb218af855986263c67b2da411 | function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color',... |
github | juliacamps/OR-master | vl_test_kmeans.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_kmeans.m | 2,788 | utf_8 | 14374b7dbae832fc3509e02caf00cdf5 | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
function s = setup()
randn('sta... |
github | juliacamps/OR-master | vl_test_hikmeans.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_hikmeans.m | 463 | utf_8 | dc3b493646e66316184e86ff4e6138ab | function results = vl_test_hikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[tree, assign] = vl_hikmeans(s.data,3,100) ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
function test_elka... |
github | juliacamps/OR-master | vl_test_aib.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_aib.m | 1,277 | utf_8 | 78978ae54e7ebe991d136336ba4bf9c6 | function results = vl_test_aib(varargin)
% VL_TEST_AIB
vl_test_init ;
function s = setup()
s = [] ;
function test_basic(s)
Pcx = [.3 .3 0 0
0 0 .2 .2] ;
% This results in the AIB tree
%
% 1 - \
% 5 - \
% 2 - / \
% - 7
% 3 - \ /
% 6 - /
% 4 - /
%
% coded by the map [5 ... |
github | juliacamps/OR-master | vl_test_imarray.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_imarray.m | 795 | utf_8 | c5e6a5aa8c2e63e248814f5bd89832a8 | function results = vl_test_imarray(varargin)
% VL_TEST_IMARRAY
vl_test_init ;
function test_movie_rgb(s)
A = rand(23,15,3,4) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_indexed(s)
cmap = get(0,'DefaultFigureColormap') ;
A = uint8(size(cmap,1)*rand(23,15,4)) ;
A = min(A,size(cmap,1)-1) ;
B = vl_imarray(A,'m... |
github | juliacamps/OR-master | vl_test_homkermap.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_homkermap.m | 1,903 | utf_8 | c157052bf4213793a961bde1f73fb307 | function results = vl_test_homkermap(varargin)
% VL_TEST_HOMKERMAP
vl_test_init ;
function check_ker(ker, n, window, period)
args = {n, ker, 'window', window} ;
if nargin > 3
args = {args{:}, 'period', period} ;
end
x = [-1 -.5 0 .5 1] ;
y = linspace(0,2,100) ;
for conv = {@single, @double}
x = feval(conv{1}, x) ;... |
github | juliacamps/OR-master | vl_test_slic.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_slic.m | 211 | utf_8 | 9077cfa77eb7b8d43880ba62408291f8 | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1, 'verbose') ;
|
github | juliacamps/OR-master | vl_test_ikmeans.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_ikmeans.m | 466 | utf_8 | 1ee2f647ac0035ed0d704a0cd615b040 | function results = vl_test_ikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[centers, assign] = vl_ikmeans(s.data,100) ;
assign_ = vl_ikmeanspush(s.data, centers) ;
vl_assert_equal(assign,assign_) ;
function test_elk... |
github | juliacamps/OR-master | vl_test_mser.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_mser.m | 242 | utf_8 | 1ad33563b0c86542a2978ee94e0f4a39 | function results = vl_test_mser(varargin)
% VL_TEST_MSER
vl_test_init ;
function s = setup()
s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ;
function test_mser(s)
[regions,frames] = vl_mser(s.im) ;
mask = vl_erfill(s.im, regions(1)) ;
|
github | juliacamps/OR-master | vl_test_inthist.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_inthist.m | 811 | utf_8 | 459027d0c54d8f197563a02ab66ef45d | function results = vl_test_inthist(varargin)
% VL_TEST_INTHIST
vl_test_init ;
function s = setup()
rand('state',0) ;
s.labels = uint32(8*rand(123, 76, 3)) ;
function test_basic(s)
l = 10 ;
hist = vl_inthist(s.labels, 'numlabels', l) ;
hist_ = inthist_slow(s.labels, l) ;
vl_assert_equal(double(hist),hist_) ;
function... |
github | juliacamps/OR-master | vl_test_imdisttf.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_imdisttf.m | 1,885 | utf_8 | ae921197988abeb984cbcdf9eaf80e77 | function results = vl_test_imdisttf(varargin)
% VL_TEST_DISTTF
vl_test_init ;
function test_basic()
for conv = {@single, @double}
conv = conv{1} ;
I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ;
D = vl_imdisttf(I);
assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
I(2,2) = -3 ;
[D,map] = vl_imdisttf(I) ;
asse... |
github | juliacamps/OR-master | vl_test_pr.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_pr.m | 2,950 | utf_8 | fbe44689dacb16970984e4dbcede0430 | function results = vl_test_pr(varargin)
% VL_TEST_PR
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[rc,pr] = vl_pr(s.labels,s.scores0) ;
vl_assert_almost_equal(pr, [1 1/1 2/2 2/3 2/4 2/5]) ;
vl_assert_almost_equal(rc, ... |
github | juliacamps/OR-master | vl_test_hog.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_hog.m | 1,555 | utf_8 | eed7b2a116d142040587dc9c4eb7cd2e | function results = vl_test_hog(varargin)
% VL_TEST_HOG
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
[x,y]= meshgrid(linspace(-1,1,128)) ;
s.round = single(x.^2+y.^2);
s.imSmall = s.im(1:128,1:128,:) ;
s.imSmall = s.im ;
s.imSmallFlipped = s.imSmall(:,end:-1:1,:) ;
function test_basic... |
github | juliacamps/OR-master | vl_test_argparse.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_argparse.m | 795 | utf_8 | e72185b27206d0ee1dfdc19fe77a5be6 | function results = vl_test_argparse(varargin)
% VL_TEST_ARGPARSE
vl_test_init ;
function test_basic()
opts.field1 = 1 ;
opts.field2 = 2 ;
opts.field3 = 3 ;
opts_ = opts ;
opts_.field1 = 3 ;
opts_.field2 = 10 ;
opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ;
assert(isequal(opts, opts_)) ;
opts_.field1 = 9 ;
... |
github | juliacamps/OR-master | vl_test_binsearch.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_binsearch.m | 1,339 | utf_8 | 85dc020adce3f228fe7dfb24cf3acc63 | function results = vl_test_binsearch(varargin)
% VL_TEST_BINSEARCH
vl_test_init ;
function test_inf_bins()
x = [-inf -1 0 1 +inf] ;
vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ;
vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ;
vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ;
vl_a... |
github | juliacamps/OR-master | vl_test_maketrainingset.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/xtest/vl_test_maketrainingset.m | 1,014 | utf_8 | 147ca63d80a18ed3659dac4a3efcf84e | function results = vl_test_maketrainingset(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.biasMultiplier = 10 ;
s.lambda = 0.01 ;
Np = 10 ;
Nn = 10 ;
Xp = diag([1 3])*randn(2, Np) ;
Xn = diag([1 3])*randn(2, Nn) ;
Xp(1,:) = Xp(1,:) + 2 + 1 ;
Xn(1,:) = Xn(1,:) - 2 + 1 ;
s.X = [Xp... |
github | juliacamps/OR-master | vl_plotframe.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/plotop/vl_plotframe.m | 5,397 | utf_8 | eb21148a33aae6a835f47faa0db311d6 | function h = vl_plotframe(frames,varargin)
% VL_PLOTFRAME Plot feature frame
% VL_PLOTFRAME(FRAME) plots the frames FRAME. Frames are attributed
% image regions (as, for example, extracted by a feature detector). A
% frame is a vector of D=2,3,..,6 real numbers, depending on its
% class. VL_PLOTFRAME() supports t... |
github | juliacamps/OR-master | vl_roc.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/plotop/vl_roc.m | 8,743 | utf_8 | eb8acd02ccf91e98a933e49754da010a | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC ROC curve.
% [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
% Characteristic (ROC) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative one. SCORES are... |
github | juliacamps/OR-master | vl_click.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/plotop/vl_click.m | 2,661 | utf_8 | 6982e869cf80da57fdf68f5ebcd05a86 | function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any k... |
github | juliacamps/OR-master | vl_pr.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/plotop/vl_pr.m | 8,131 | utf_8 | 089b4b895dac21402ff0f7fba75fb823 | function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative on... |
github | juliacamps/OR-master | vl_ubcread.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/sift/vl_ubcread.m | 3,015 | utf_8 | e8ddd3ecd87e76b6c738ba153fef050f | function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', '... |
github | juliacamps/OR-master | vl_plotsiftdescriptor.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/toolbox/sift/vl_plotsiftdescriptor.m | 4,348 | utf_8 | b9a98b0c298fa249fb5fcd1314762b88 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptors D, stored as
% columns of the matrix D. D has the same format used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warped to
% the SIFT fram... |
github | juliacamps/OR-master | phow_caltech101.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/apps/phow_caltech101.m | 11,301 | utf_8 | 8316095b4842a2c43cf3dfc91e313aee | function phow_caltech101
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedup ... |
github | juliacamps/OR-master | sift_mosaic.m | .m | OR-master/Practical_Session_Multiview_Analysis/code/vlfeat-0.9.16/apps/sift_mosaic.m | 4,621 | utf_8 | 8fa3ad91b401b8f2400fb65944c79712 | function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAI... |
github | kutzer/URToolbox-master | installURToolbox.m | .m | URToolbox-master/installURToolbox.m | 14,742 | utf_8 | a56200d5a0eee793ee11302fb0a6142d | function installURToolbox(replaceExisting)
% INSTALLURTOOLBOX installs UR Toolbox for MATLAB.
% INSTALLURTOOLBOX installs UR Toolbox into the following
% locations:
% Source: Destination
% URToolboxFunctions: matlabroot\toolbox\ur
% URToolboxSupport: matlabroot\toolbox\ur\OptiTrackT... |
github | kutzer/URToolbox-master | URToolboxUpdate.m | .m | URToolbox-master/URToolboxFunctions/URToolboxUpdate.m | 3,982 | utf_8 | cb3aad49073b43851e803bc1ea50f90f | function URToolboxUpdate
% URTOOLBOXUPDATE download and update the UR Toolbox.
%
% M. Kutzer 27Feb2016, USNA
% Updates
% 15Mar2018 - Updated to include try/catch for required toolbox
% installations and include msgbox warning when download
% fails.
% 08Jan2021 - Updated ToolboxUpdat... |
github | ananyo2012/qpso-gui-master | qpso.m | .m | qpso-gui-master/qpso.m | 10,647 | utf_8 | fd7f33c7d5b20b5355653e32129665b4 | function [data, fit_count, gbest_find, gbestval, worst, std_deviation, Mean, eltime, evalue] = qpso(rngseed, RUNNO,Max_Gen,Particle_Number,Dimension,VRmin,VRmax,levyflight,filename,handles)
%[gbest_find,gbestval_find,fitcount,std_deviation,Mean]= qpso('failure_mutual1',3,500,10000,40,30,0,1,varargin)
tic;
fhd=filename... |
github | ananyo2012/qpso-gui-master | gui.m | .m | qpso-gui-master/gui.m | 28,157 | utf_8 | 2c0b65a29b8017e031919124d1e8c48e | function varargout = gui(varargin)
% GUI MATLAB code for gui.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | jkrumbiegel/lmu_amd-master | PsychEyelinkDispatchCallback.m | .m | lmu_amd-master/EyelinkBasic/PsychEyelinkDispatchCallback.m | 18,310 | utf_8 | 2f67f6eecc89b4b57a3db89e957a4303 | function rc = PsychEyelinkDispatchCallback(callArgs, msg)
% Retrieve live eye-image from Eyelink, show it in onscreen window.
%
% This function is normally called from within the Eyelink() mex file.
% Normal user code only calls it once to supply the eyelink defaults struct.
% This is handled within the EyelinkInitDefa... |
github | jkrumbiegel/lmu_amd-master | edf2singlestruct.m | .m | lmu_amd-master/amdSearchExperiment/edf2singlestruct.m | 12,192 | utf_8 | 598ca531177fc6546ef3bc9529beb41f | function out = edf2singlestruct(filename)
% Converts .edf file to .mat files for each trial
%
% The function extracts data and events from a given .edf input file. It splits the blocked data
% into single-trial data and events and saves data and events for each trial into a matlab
% .mat file.
%
%______________... |
github | sinwel/hdr_matlab-master | Sony_HDR.m | .m | hdr_matlab-master/Sony_HDR.m | 29,306 | utf_8 | 7f4924a34ab68f89e5e882271fc91852 | function varargout = Sony_HDR(varargin)
% SONY_HDR MATLAB code for Sony_HDR.fig
% SONY_HDR, by itself, creates a new SONY_HDR or raises the existing
% singleton*.
%
% H = SONY_HDR returns the handle to a new SONY_HDR or the handle to
% the existing singleton*.
%
% SONY_HDR('CALLBACK',hObject,ev... |
github | sinwel/hdr_matlab-master | GenerateTabandCopyBanks.m | .m | hdr_matlab-master/GenerateTabandCopyBanks.m | 956 | utf_8 | 9d35abcb5e570c19c5c3dae1d371bd8a |
function GenerateTabandCopyBanks(param);
if nargin < 1
param.bits = 10;
param.blacklevel = 64;
end
d = 1;
b = 3+d;
c = -2-2*d;
% x = 0:0.01:1;
% figure;plot(b*x.^2+c*x.^3+d*x.^4);
for tidx = 1:961
D = tidx/961;
D = b*D.^2+c*D.^3+d*D.^4;
D_int(tidx) = uint16(D*255);
end
fid = fopen('... |
github | sinwel/hdr_matlab-master | bilateralFilter.m | .m | hdr_matlab-master/bilateralFilter.m | 6,799 | utf_8 | 7b4b53dbaaef938bf44ac877b016aac2 | %
% output = bilateralFilter( data, edge, ...
% edgeMin, edgeMax, ...
% sigmaSpatial, sigmaRange, ...
% samplingSpatial, samplingRange )
%
% Bilateral and Cross-Bilateral Filter using the Bilateral Grid.
%
% Bilaterally filters the image 'data' ... |
github | sinwel/hdr_matlab-master | defect_pixel_processhdr.m | .m | hdr_matlab-master/RK/defect_pixel_processhdr.m | 997 | utf_8 | 221a5c6ba73fdf92dc7bb761dcb6bf57 |
function out = defect_pixel_processhdr(I)
X = I;
%P = [0 0 1 0 0; 0 1 0 1 0; 1 0 1 0 1; 0 1 0 1 0; 0 0 1 0 0];
P = [
0 0 0 0 1 0 0 0 0;
0 0 0 0 0 0 0 0 0;
0 0 1 0 0 0 1 0 0;
0 0 0 0 0 0 0 0 0;
1 0 0 0 1 0 0 0 1;
0 0 0 0 0 0 0 0 0;
0 0 1 0 0 0 1 0 0;
0 0 0 0 0 0 0 0 0;
0 0 0 0 1 0 0 ... |
github | sinwel/hdr_matlab-master | Sony_HDR.m | .m | hdr_matlab-master/RK/Sony_HDR.m | 20,132 | utf_8 | 6e0f1b01c3678f0d2951e778a8da8db5 | function varargout = Sony_HDR(varargin)
% SONY_HDR MATLAB code for Sony_HDR.fig
% SONY_HDR, by itself, creates a new SONY_HDR or raises the existing
% singleton*.
%
% H = SONY_HDR returns the handle to a new SONY_HDR or the handle to
% the existing singleton*.
%
% SONY_HDR('CALLBACK',hObject,ev... |
github | sinwel/hdr_matlab-master | bilateralFilter.m | .m | hdr_matlab-master/RK/bilateralFilter.m | 6,799 | utf_8 | 7b4b53dbaaef938bf44ac877b016aac2 | %
% output = bilateralFilter( data, edge, ...
% edgeMin, edgeMax, ...
% sigmaSpatial, sigmaRange, ...
% samplingSpatial, samplingRange )
%
% Bilateral and Cross-Bilateral Filter using the Bilateral Grid.
%
% Bilaterally filters the image 'data' ... |
github | sinwel/hdr_matlab-master | Sony_HDR_fixed.m | .m | hdr_matlab-master/RK/Sony_HDR_fixed.m | 19,811 | utf_8 | 923da31499f499fc61d7cac2c4986507 | function varargout = Sony_HDR(varargin)
% SONY_HDR MATLAB code for Sony_HDR.fig
% SONY_HDR, by itself, creates a new SONY_HDR or raises the existing
% singleton*.
%
% H = SONY_HDR returns the handle to a new SONY_HDR or the handle to
% the existing singleton*.
%
% SONY_HDR('CALLBACK',hObject,ev... |
github | ZZJohn/Face_Hallucination_CNN-master | demo_SR.m | .m | Face_Hallucination_CNN-master/face_hallucination/srcnn/SRCNN_v1/SRCNN/demo_SR.m | 2,185 | utf_8 | e9d372ca2910d1d242da512268599863 | % =========================================================================
% Test code for Super-Resolution Convolutional Neural Networks (SRCNN)
%
% Reference
% Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Learning a Deep Convolutional Network for Image Super-Resolution,
% in Proceedings of European Conf... |
github | SRC877/Comparing-and-combining-Kalman-and-Wiener-Filter-for-video-denoising-master | gaussiannoise.m | .m | Comparing-and-combining-Kalman-and-Wiener-Filter-for-video-denoising-master/gaussiannoise.m | 4,035 | utf_8 | 8b79a55c6028abc128f46de83484099f |
% Example of how the Kalman filter performs for real data
%{
obj=VideoReader('pod.avi');
aviobj=VideoWriter('pocha.avi')
open(aviobj)
nFrames=obj.NumberOfFrames;
for k=1:nFrames
img=read(obj,k);
im2 = imnoise(img,'gaussian',0.1,0.2)
f=im2frame(im2)
writeVideo(aviobj,f)
figure(1),imshow(im2,[]);
en... |
github | sagihaider/Single-Trial-EEG-Classification-master | f_Adaptive_Learning_A.m | .m | Single-Trial-EEG-Classification-master/single-trial EEG classification/functions/f_Adaptive_Learning_A.m | 607 | utf_8 | 4d1126a24589b943eb621ea4d52f2ead |
% Function Name:f_Adaptive_Learning_A
% (c) Haider Raza, Intelligent System Research Center, University of Ulster, Northern Ireland, UK.
% Raza-H@email.ulster.ac.uk
% Date: 18-11-2014.
function [LABEL]=f_Adaptive_Learning_A(TEST_X,model)
[No_of_Trails, Dim]=size(TEST_X);
%... |
github | SainsburyWellcomeCentre/AllenBrainAPI-master | getAllenStructureList.m | .m | AllenBrainAPI-master/getAllenStructureList.m | 10,449 | utf_8 | f29169e397532e8f7f86891385774ed4 | function [ARA_table,tableRowInds] = getAllenStructureList(varargin)
% Download the list of adult mouse structures from the Allen API.
%
% function ARA_table = getAllenStructureList('param1',val1,...)
%
%
% Purpose
% Make an API query to read in the Allen Reference Atlas (ARA) brain area
% list. All areas and data are ... |
github | SainsburyWellcomeCentre/AllenBrainAPI-master | thalamus.m | .m | AllenBrainAPI-master/examples/thalamus.m | 3,125 | utf_8 | fba8cdfc984fa67f9305a3dac6f5c7dc | function thalamus
% make a projection density plot
%
% This is the MATLAB version of the R example found at
% http://api.brain-map.org/examples/doc/thalamus/thalamus.R.html
%
% We have some more robust MATLAB functions that encapsulate
% some operations that were performed in-line in the R example.
% These standalon... |
github | taznux/radiomics-tools-master | nrrdread.m | .m | radiomics-tools-master/Tools/MatlabTools/NRRD4Matlab/nrrdread.m | 5,153 | utf_8 | bf93c73926d3816da69f2b966a2d21e2 | function [X, meta] = nrrdread(filename)
%NRRDREAD Import NRRD imagery and metadata.
% [X, META] = NRRDREAD(FILENAME) reads the image volume and associated
% metadata from the NRRD-format file specified by FILENAME.
%
% Current limitations/caveats:
% * "Block" datatype is not supported.
% * Only tested with "... |
github | taznux/radiomics-tools-master | nrrdwrite.m | .m | radiomics-tools-master/Tools/MatlabTools/NRRD4Matlab/nrrdwrite.m | 7,603 | utf_8 | 44da78086fb5934153b613714420199c | % ========================================================================
%
% nrrdwriter_dan
%
% filename - 'myimage.ext' - 'veins.nrrd'
% matrix - data - Matlab matrix
% meta - meta data of image
% ex) meta =
%
% type: 'float'
% dimension: '4'
% s... |
github | DSutyak/CUSail-master | odeEuler.m | .m | CUSail-master/Simulators/3DsailSim/odeEuler.m | 9,464 | utf_8 | 702e5198e24be5f0e06ad28b191a2590 | function [t,stateArray0]=odeEuler(fhandle,stateArray0,ptemp)
%%% allows for user to have active control of sail and rudder
%creates global parameters to be used in subfunctions
p=ptemp;
t=0;
stateArray0=stateArray0';
stateArray=stateArray0;
keyspeed = 1;
%Initialize figure object
f=figure(1);
cla
set(f,'units','norm... |
github | DSutyak/CUSail-master | odeEuler2.m | .m | CUSail-master/Simulators/3DsailSim/odeEuler2.m | 10,927 | utf_8 | cb4ce4dd71e3e70b33a18384ac694f89 | function [t,stateArray0]=odeEuler2(fhandle,stateArray0,ptemp)
%%% allows for user to have active control of sail and rudder
%creates global parameters to be used in subfunctions
p=ptemp;
t=0;
stateArray0=stateArray0';
stateArray=stateArray0;
keyspeed = 1;
%Initialize figure object
f=figure(1);
cla
set(f,'units','nor... |
github | DSutyak/CUSail-master | arrow.m | .m | CUSail-master/Simulators/2DSailSim/arrow.m | 55,175 | utf_8 | 219009b4d9e5166d5c1c2c4840748689 | function [h,yy,zz] = arrow(varargin)
% ARROW Draw a line with an arrowhead.
%
% ARROW(Start,Stop) draws a line with an arrow from Start to Stop (points
% should be vectors of length 2 or 3, or matrices with 2 or 3
% columns), and returns the graphics handle of the arrow(s).
%
% ARROW uses the mouse (cl... |
github | DSutyak/CUSail-master | sail3.m | .m | CUSail-master/Simulators/2DSailSim/sail3.m | 5,825 | utf_8 | 9481d2e498eb1be2becdd11233a1f96f | function sail3(testnum1, testnum2, testnum3)
%This function calls the function 'input.m' and uses an euler integration
%that makes use of the 'rhs.m' function, where the dynamics of the simulation takes place.
%Outputs:
% -Matrix with time points determining row,
% and columns=[[x,y]position , [x,y]velocity, boat ... |
github | DSutyak/CUSail-master | sailn5.m | .m | CUSail-master/Simulators/2DSailSim/sailn5.m | 6,084 | utf_8 | 1dbd92db54ebf2a42f3b63fb888a29f9 | function sail3
%This function calls the function 'input.m' and uses an euler integration
%that makes use of the 'rhs.m' function, where the dynamics of the simulation takes place.
%Outputs:
% -Matrix with time points determining row,
% and columns=[[x,y]position , [x,y]velocity, boat angle, boat angular velocity,... |
github | DSutyak/CUSail-master | comm_test.m | .m | CUSail-master/new_basestation/comm_test.m | 576 | utf_8 | 455aca311c606fcc79819020beb376c8 | s = serial('COM6', 'BaudRate', 9600);
%x = serial('COM5', 'BaudRate', 9600, 'Terminator', 'CR', 'StopBit', 1, 'Parity', 'None');
fopen(s);
%fopen(x);
%fprintf(s,'testn');
%while (1)
disp(s.BytesAvailable);
s.BytesAvailableFcnMode = 'terminator';
s.Terminator = '}';
%set(s, 'BytesAvailableFcn', @onT... |
github | DSutyak/CUSail-master | base_station.m | .m | CUSail-master/new_basestation/base_station.m | 18,467 | utf_8 | f85e2c949bf148602b77825e345906b9 | %==========================================================================
% INITIALIZATION CODE
%==========================================================================
function varargout = base_station(varargin)
% BASE_STATION MATLAB code for base_station.fig
% BASE_STATION, by itself, creates a new BASE_S... |
github | sg-s/alicat-MFC-master | reg2num.m | .m | alicat-MFC-master/reg2num.m | 303 | utf_8 | 733743c7b0732937a4b069d638993855 | % reg2num.m
% simple function to convert the register readout of Alicat MFCs to a number
% Rob Campbell had this function at some point, but is no longer on his repo
% this is my version to mimic what I think his function does
%
function [n] = reg2num(r)
a = strfind(r,'=');
n = str2double(r(a+1:end)); |
github | sg-s/alicat-MFC-master | setMFCParameters.m | .m | alicat-MFC-master/@MFC/setMFCParameters.m | 801 | utf_8 | a7a1eebe0fb519e76547956617921978 | % set MFC parameters from MFC connected to the serial port
function [m] = setMFCParameters(m,param,value)
s = dbstack;
% prevent crazy loops -- only execute when not called by getMFCParameters
if any(strcmp({s.name},'getMFCParameters'))
return
end
if isnan(value)
return
end
assert(value<=20000,'Value too high: f... |
github | sg-s/alicat-MFC-master | getMFCParameters.m | .m | alicat-MFC-master/@MFC/getMFCParameters.m | 339 | utf_8 | bfc95535b8ce43c37a332e68851b1456 | % get MFC parameters from MFC connected to the serial port
function [m] = getMFCParameters(m)
% P-gain register 21
registers = [21 22 23];
parameters = {'P','D','I'};
for i = length(registers):-1:1
probe = [m.name, '$$R' mat2str(registers(i))];
fprintf(m.fid,probe);
raw_m = fscanf(m.fid);
m.(parameters{i}) = r... |
github | chenmaoshan/Madagascar-master | test_dgt.m | .m | Madagascar-master/user/pyang/test_dgt.m | 3,445 | UNKNOWN | 521effe52c3a9af0d2f39c421cf8edb2 | function test_dgt
%dgt.m: This programme is used to compute dual frame and reconstruct
% the original signal.
%
% Reference: Qian, Shie, and Dapang Chen. "Discrete gabor transform."
% Signal Processing, IEEE Transactions on 41.7 (1993): 2429-2438.
%
% Copyright (C) 2010 Xi'an Jiaotong University (P... |
github | chenmaoshan/Madagascar-master | demo_tau_method.m | .m | Madagascar-master/user/pyang/demo_tau_method.m | 2,201 | utf_8 | cdfc7076867b1278b1949a7f635456f6 | function demo_tau_method
%% Copyright (c) Xi'an Jiaotong University 2014
% % Originally written by Guowei Zhang, adapted by Pengliang Yang
% %
% % Reference: Joakim O. Blanch, Johan O.A. Robertsson, and William W. Symmes,
% % Modeling of a constant Q: methodology and algorithm for an efficient and
% % optimall... |
github | chenmaoshan/Madagascar-master | test_dlct2.m | .m | Madagascar-master/user/pyang/test_dlct2.m | 6,027 | utf_8 | 90744ad4292b8cf0f4dd5d14404f72f6 | function test_dlct2
% Description: matlab exmaple of DLCT
% Reference:
% Alkishriwo, Osama A., and Luis F. Chaparro. "A discrete
% linear chirp transform (DLCT) for data compression."
% 11th International Conference on Information Science, Signal
% Processing and their Applications (ISSPA) IEEE, 2012.
%
% Cop... |
github | chenmaoshan/Madagascar-master | test_dlct1.m | .m | Madagascar-master/user/pyang/test_dlct1.m | 2,325 | utf_8 | 8123c2761ea3397f10bcdb24172557a7 | function test_dlct1
% Description: matlab exmaple of DLCT
% Reference:
% Alkishriwo, Osama A., and Luis F. Chaparro. "A discrete
% linear chirp transform (DLCT) for data compression."
% 11th International Conference on Information Science, Signal
% Processing and their Applications (ISSPA) IEEE, 2012.
%
% Cop... |
github | chenmaoshan/Madagascar-master | cg_avo.m | .m | Madagascar-master/user/mehdi/cg_avo.m | 618 | utf_8 | 62588e64baf7e41bef6d2dcba02a0419 | % by Mehdi Eftekhari Far
%last updated: 12/3/2010
function [weight]=cg_avo(n_unknown,cons,bkbm,tolerancer,itmax,I)
M=n_unknown;
w=zeros(1,M);
B=cons';
g=-B;
p=g;
A=bkbm;
for i=1:I
alpha=sum(g.*g)/sum(p'.*(A*p'));
z=g.*g;
w=w-alpha.*p;
g=g-alpha.*(A*p')';
d=g.*g;
beta=d/z;
... |
github | chenmaoshan/Madagascar-master | replace.m | .m | Madagascar-master/book/rsf/manual/replace.m | 4,178 | utf_8 | a5e18aec198aaf97df77efbaec9d5e12 | function replace(oldtxt,newtxt,file)
% REPLACE will lookinto a file and change all occurrences of a string with another string.
% This function is extremely fragile and untested (and dangerous). Use with extreme caution.
%
% Use \\ to produce a backslash character and %% to produce the percent
% character.
%
% SYNTAX
... |
github | chenmaoshan/Madagascar-master | thresh.m | .m | Madagascar-master/book/xjtu/mcaseislet/Fig/thresh.m | 947 | utf_8 | 7a86b574598899db644a9fbdcf739553 | function thresholding_op
close,clc,clear all
x=[-5:0.02:5];
thr=1.5;
normp=0.5;
y1=HardThresh(x,thr);
y2=SoftThresh(x,thr);
y3=pThresh(x,thr,normp);
y4=SteinThresh(x,thr);
y5=pexpThresh(x,thr,normp);
plot(x,y1,'k','linewidth',1)
hold on
plot(x,y2,'g','linewidth',1)
hold on
plot(x,y3,'b','linewidth',1)
... |
github | chenmaoshan/Madagascar-master | SolveISTc.m | .m | Madagascar-master/book/slim/geo2008NewInsightsPareto/Matfcts/SolveISTc.m | 4,763 | utf_8 | ef1677ada13e87bf0a03523cceadefa4 | function [x, xnorms, rnorms, lambdas] = SolveISTc(A, y, Iters, InnerIters, x, fullPath)
% SolveISTc: Iterative Soft Thresholding with cooling
%
%---------------------------------------------------------------
% Solve the basis pursuit (BP) problem
%
% min ||x||_1 s.t. Ax=y
%
%------------------------------------... |
github | chenmaoshan/Madagascar-master | SolveIRLS.m | .m | Madagascar-master/book/slim/geo2008NewInsightsPareto/Matfcts/SolveIRLS.m | 5,437 | utf_8 | f8ff121a615a72c075d727c3325c949f | function [x, xnorms, rnorms] = SolveIRLS(A, y, Iters, InnerIters, a, sigma, damp, x, fullPath)
% SolveIRLS: Iterative Reweighted least-squares
%
%---------------------------------------------------------------
% Solve
%
% min 1/2||y-Ax||_2^2 + damp*||Wx||_2^2
%
%----------------------------------------------------... |
github | chenmaoshan/Madagascar-master | SolveIST.m | .m | Madagascar-master/book/slim/geo2008NewInsightsPareto/Matfcts/SolveIST.m | 4,357 | utf_8 | 6bd35ee25f68d9e56cf45e22eb0146dd | function [x, xnorms, rnorms] = SolveIST(A, y, Iters, lambda, x, fullPath)
% SolveIST: Iterative Soft Thresholding
%
%---------------------------------------------------------------
% Solve
%
% min 1/2||y-Ax|| + lambda*||x||_1
%
%---------------------------------------------------------------
%
% INPUTS
% ======
% ... |
github | chenmaoshan/Madagascar-master | lsqr.m | .m | Madagascar-master/book/slim/geo2008NewInsightsPareto/Matfcts/private/lsqr.m | 11,849 | utf_8 | b60925c5944249161e00049c67d30868 | function [ x, istop, itn, r1norm, r2norm, anorm, acond, arnorm, xnorm, var ]...
= lsqr( m, n, A, b, damp, atol, btol, conlim, itnlim, show )
%
% [ x, istop, itn, r1norm, r2norm, anorm, acond, arnorm, xnorm, var ]...
% = lsqr( m, n, A, b, damp, atol, btol, conlim, itnlim, show );
%
% LSQR solves Ax = b or mi... |
github | chenmaoshan/Madagascar-master | initialFilter.m | .m | Madagascar-master/book/tccs/dsd/Matfun/initialFilter.m | 2,919 | utf_8 | 7663b728bce699823160342b5643d106 | function [H, hsize] = initialFilter(htype, level)
% filters are of size $hsize x hsize$, the # of filters is hsize^2
switch htype
case 'spline' % Piece-wise Linear Spline
[h, hsize] = TFDict(level);
case 'haar' % Haar transform matrix
hsize = 2^level;
h = haarmtx... |
github | chenmaoshan/Madagascar-master | fx_emdpf.m | .m | Madagascar-master/book/tccs/emdpf/Matfun/fx_emdpf.m | 4,054 | utf_8 | 199aa0468a64f2e00f637f04d30cac1b | function [ D1 ] = fx_emdpf(D,flow,fhigh,dt,N, lf, mu)
%FXEMDPF: F-X domain empirical mode decomposition predictive filtering
% IN D: intput data
% flow: processing frequency range (lower)
% fhigh: processing frequency range (higher)
% dt: temporal sampling interval
% N: number of IMF... |
github | chenmaoshan/Madagascar-master | fx_decon.m | .m | Madagascar-master/book/tccs/emdpf/Matfun/fx_decon.m | 3,932 | utf_8 | 6b47709ea0f32265d79d98ea49f49c46 | function [DATA_f] = fx_decon(DATA,dt,lf,mu,flow,fhigh);
%FX_DECON: SNR enhancement using fx-deconvolution.
%
% [DATA_f] = fx_decon(DATA,dt,lf,mu,flow,fhigh);
%
% IN DATA: the data matrix, columns are traces
% dt: sampling interval in sec
% lf: lenght of operator (lenght of the filter)
% ... |
github | chenmaoshan/Madagascar-master | rsf_create.m | .m | Madagascar-master/api/octave/rsf_create.m | 3,107 | utf_8 | dab629fb15d420286fe7a45c6b504af0 | ## Copyright (C) 2007 Ioan Vlad
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
##
## This program is distributed... |
github | chenmaoshan/Madagascar-master | rsf_dim.m | .m | Madagascar-master/api/octave/rsf_dim.m | 2,394 | utf_8 | 008750836bdbae9c693cb9c7d0890a84 | ## Copyright (C) 2007 Ioan Vlad
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
##
## This program is distributed... |
github | chenmaoshan/Madagascar-master | rsf_par.m | .m | Madagascar-master/api/octave/rsf_par.m | 2,369 | utf_8 | 86fa70b256264c49b98a52f8205c92c6 | ## Copyright (C) 2007 Ioan Vlad
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
##
## This program is distributed... |
github | mboulet/flight-master | ModelVisualizer.m | .m | flight-master/estimators/matlab_plane/ModelVisualizer.m | 6,109 | utf_8 | 785a27da005b4e1632a11eb0548739a4 | classdef ModelVisualizer < Visualizer
% Implements the draw function for the MURI Plane
properties
angleRep = 'euler';
end
methods
function obj = ModelVisualizer(angleRep)
obj = obj@Visualizer(1);
obj.playback_speed = 1;
obj.display_dt = 0;
if (nargin > 0)
obj.angleR... |
github | ntienvu/ICDM2016_OLR-master | OnepassLogisticRegression.m | .m | ICDM2016_OLR-master/standard_classification/OnepassLogisticRegression.m | 3,258 | utf_8 | 76cca0d94ff4c86485e18f27b69a61db | function Output = OnepassLogisticRegression(yyTrain,xxTrain,yyTest,xxTest)
% input ===================================================================
% yyTrain: label of trainning data [NTrain x 1]
% xxTrain: feature of training data [NTrain x dd]
% yyTest: label of testing data [NTest x 1]
% xxTest: feature ... |
github | ntienvu/ICDM2016_OLR-master | LogisticRegression_SGD.m | .m | ICDM2016_OLR-master/baselines/LogiticRegression_SGD/LogisticRegression_SGD.m | 3,890 | utf_8 | a147833858e8cd9c6e7b64ab98d6cdf1 | function Output = LogisticRegression_SGD(yyTrain,xxTrain,yyTest,xxTest)
% Algorithm 2 in the paper
% input ===================================================================
% yyTrain: label of trainning data [NTrain x 1]
% xxTrain: feature of training data [NTrain x dd]
% yyTest: label of testing data [NTest x... |
github | ntienvu/ICDM2016_OLR-master | LogisticRegression_SGD_Block.m | .m | ICDM2016_OLR-master/baselines/LogiticRegression_SGD/LogisticRegression_SGD_Block.m | 3,968 | utf_8 | af815b5d4114efb54e2b9e05f65adac8 | function Output = LogisticRegression_SGD_Block(model,yyTrain,xxTrain,yyTest,xxTest)
% input ===================================================================
% yyTrain: label of trainning data [NTrain x 1]
% xxTrain: feature of training data [NTrain x dd]
% yyTest: label of testing data [NTest x 1]
% xxTest:... |
github | ntienvu/ICDM2016_OLR-master | LogisticRegrLaplacianApproxBinary.m | .m | ICDM2016_OLR-master/baselines/LogisticRegression_Laplacian/LogisticRegrLaplacianApproxBinary.m | 1,022 | utf_8 | 1b370d48e91613421738f338ed2a56e7 | function model = LogisticRegrLaplacianApproxBinary(yyTrain,xxTrain)
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
fun=@Laplacian_mean_function;
XX=xxTrain'*xxTrain;
dd=size(xxTrain,2);
mu_0=zeros(dd,1);
Sig_0=1*eye(dd);
Sig_1_Sig=1;
options = optimset('Display', 'off','MaxIter',10);... |
github | ntienvu/ICDM2016_OLR-master | run_experiment_bc.m | .m | ICDM2016_OLR-master/baselines/libol_v0.3.0/run_experiment_bc.m | 17,142 | utf_8 | 8766f48c1dc7170bb9b08ac6272620af | function run_experiment_bc(varargin)
%RUN_EXPERIMENT_BC(dataset_name, file_format, impl_lang):
%--------------------------------------------------------------------------
%This example demos how to run online learning experiments automatically.
%Examples:
% run_experiment_bc('svmguide3','mat','m')
% run_experiment... |
github | ntienvu/ICDM2016_OLR-master | CV_algorithm.m | .m | ICDM2016_OLR-master/baselines/libol_v0.3.0/CV_algorithm.m | 4,790 | utf_8 | b43fd88d83b8669afa305c75e45b39d3 | function [ options ] = CV_algorithm(Y,X,options)
%CV_algorithm: This aims to choose best paramters via validation automatically.
%--------------------------------------------------------------------------
% INPUT:
% Y: the label vector, e.g., Y(t) is the label of t-th instance;
% X: training data, e... |
github | ntienvu/ICDM2016_OLR-master | demo.m | .m | ICDM2016_OLR-master/baselines/libol_v0.3.0/demo.m | 4,276 | utf_8 | edd23adb3a7ab8426171f2dcdf31c5de | function demo(varargin)
%DEMO: demo the usage of LIBOL
% demo(task_type, algorithm_name, dataset_name, file_format, impl_lang);
%--------------------------------------------------------------------------
% Examples:
% demo
% demo('bc')
% demo('mc')
% demo('bc','PA')
% demo('bc','SCW','svmguide3')
% demo('bc... |
github | ntienvu/ICDM2016_OLR-master | run_experiment.m | .m | ICDM2016_OLR-master/baselines/libol_v0.3.0/run_experiment.m | 2,762 | utf_8 | eb590541b8636a9fee4d9d3a234eef5b | function run_experiment(varargin)
%RUN_EXPERIMENT Run online learning expriments automatically
%RUN_EXPERIMENT(task_type, dataset_name, file_format, impl_lang):
%--------------------------------------------------------------------------
% 'task_type' - define the types of tasks, which include two options:
% ... |
github | ntienvu/ICDM2016_OLR-master | run_experiment_mc.m | .m | ICDM2016_OLR-master/baselines/libol_v0.3.0/run_experiment_mc.m | 14,028 | utf_8 | 8fc4db20e6e1bb665557088cab1868e2 | function run_experiment_mc(varargin)
% run_experiment (dataset_name, file_format, impl_lang):
%--------------------------------------------------------------------------
% This example demos how to run online learning experiments automatically.
% Examples:
% run_experiment_mc('glass','mat','m')
% run_experiment_mc... |
github | ntienvu/ICDM2016_OLR-master | LRSGD_labeldrift.m | .m | ICDM2016_OLR-master/label_drift/LRSGD_labeldrift.m | 3,950 | utf_8 | d8728b5c3cdc6988b367af26f84e765a | function model = LRSGD_labeldrift(model,yyTrain,xxTrain,yyTest,xxTest)
% input ===================================================================
% yyTrain: label of trainning data [NTrain x 1]
% xxTrain: feature of training data [NTrain x dd]
% yyTest: label of testing data [NTest x 1]
% xxTest: feature of t... |
github | ntienvu/ICDM2016_OLR-master | OnepassLogisticRegression_labeldrift.m | .m | ICDM2016_OLR-master/label_drift/OnepassLogisticRegression_labeldrift.m | 3,025 | utf_8 | 3d46d8b18334526c7e42f34f41f66e5f | function model = OnepassLogisticRegression_labeldrift(model,yyTrain,xxTrain,yyTest,xxTest)
% input ===================================================================
% modelSuffStats: contain model sufficient statistic of P, Q
% yyTrain: label of trainning data in block b[NbTrain x 1]
% xxTrain: feature of trai... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | contour.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/contour.m | 2,140 | utf_8 | a7585eef1ce4f767416a8ab408f0d8a1 | % extract contours and neighboring regions given non-max suppressed edge map
function contours = contour(nmax)
% work of Pablo Arbelaez <arbelaez@eecs.berkeley.edu>
% extract contours
tic;
[skel, labels, is_v, is_e, assign, vertices, edges, ...
v_left, v_right, e_left, e_right, c_left, c_right, ...
edge_equiv_ids,... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | disp_contours.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/disp_contours.m | 3,323 | utf_8 | b784d85ef4730c0d5f9b04c2df8a348a | % interactively display contours and neighboring regions
function disp_contours(contours, im)
% get image size
im_size = size(contours.skel);
% get vertex and edge indices
v_inds = find(contours.is_v);
e_inds = find(contours.is_e);
% initialize region indices
r_inds_left = [];
r_inds_right = [];
r_inds_left_ext = ... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | Segment_image_color_Al.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/Segment_image_color_Al.m | 17,962 | utf_8 | 142000a20caefcb5b59e22e1fc25b3c6 | function [seeds, obj_names, seg, cmap, seeds_ucm_img, bndry_img, data] = ...
Segment_image_color_Al(im, ucm, seeds, obj_names, f_var,filename, fh )
% Segmentation process based on Constrained segmentation
set(0, 'DefaultFigureVisible', 'off')
set(0, 'DefaultAxesVisible', 'off')
% get image size
[sx sy sz] = size(... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | nonmax_channels.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/nonmax_channels.m | 314 | utf_8 | 49a5e48384add2644eb4df8eea927460 | % given NxMxnum_ori oriented channels, compute oriented nonmax suppression
function nmax = nonmax_channels(pb, nonmax_ori_tol)
if (nargin < 2), nonmax_ori_tol = pi/8; end
n_ori = size(pb,3);
oris = (0:(n_ori-1))/n_ori * pi;
[y,i] = max(pb,[],3);
i = oris(i);
y(y<0)=0;
nmax = nonmax_oriented(y,i, nonmax_ori_tol);
|
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | gPb_from_cues.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/gPb_from_cues.m | 1,446 | utf_8 | 1f6871c3ff35f275a7915b710b83c359 | % compute globalPb from mPb, sPb, and cues
function [gPb_orient gPb_thin] = gPb_from_cues(bg1, bg2, bg3, cga1, cga2, cga3, cgb1, cgb2, cgb3, tg1, tg2, tg3, mPb, sPb)
weights = [0 0 0.0039 0.0050 0.0058 0.0069 0.0040 0.0044 0.0049 0.0024 0.0027 0.0170 0.0074];
% get size of finest scale
[sx sy sz] = size(b... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | globalPb_pieces_lum.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/globalPb_pieces_lum.m | 2,987 | utf_8 | 2e49e4f3da4c2a58f3b35c5dd50cbfab | function [gPb_orient] = globalPb_pieces_lum(imgFile, outFile, overlap, piece_size)
% by Nico Valentini, includingf work of Pablo Arbelaez
% arbelaez@ees.berkeley.edu
%
% DESCRY=IPTION: version of globalPb for large images. Proceeds by chpping
% the image into pieces, processing each piece independently, and then
% mer... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | Miji_exe.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/Miji_exe.m | 2,961 | utf_8 | 8f660101a46b7355d67e5d028a862677 | function Miji_exe(open_imagej,fiji_dir)
%% This script sets up the classpath to Fiji and optionally starts MIJ
% Author: Nico Valentini, Jacques Pecreaux, Johannes Schindelin, Jean-Yves Tinevez
if nargin < 1
open_imagej = true;
end
%% Get the Fiji directory
if nargin > 1
fiji_dir... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | globalPb_pieces.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/globalPb_pieces.m | 2,965 | utf_8 | f485b7d0e80c6e729c8533c191f41e63 | function [gPb_orient] = globalPb_pieces(imgFile, outFile, overlap, piece_size)
% by Pablo Arbelaez (semi modified by Nico Valentini)
% arbelaez@ees.berkeley.edu
%
% DESCRY=IPTION: version of globalPb for large images. Proceeds by chpping
% the image into pieces, processing each piece independently, and then
% merging ... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | fit_contour.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/fit_contour.m | 2,079 | utf_8 | 4bfdde57e42bc906f763181bfeffecad | % extract contours and neighboring regions given non-max suppressed edge map
function contours = fit_contour(nmax)
% extract contours
[skel, labels, is_v, is_e, assign, vertices, edges, ...
v_left, v_right, e_left, e_right, c_left, c_right, ...
edge_equiv_ids, is_compl, e_x_coords, e_y_coords] = ...
mex_contour_s... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | nonmax_oriented.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/nonmax_oriented.m | 1,172 | utf_8 | a4e08e58a40018a0a8fbbe52c89d89cf | % Oriented non-max suppression (2D).
%
% Perform non-max suppression orthogonal to the specified orientation on
% the given 2D matrix using linear interpolation in a 3x3 neighborhood.
%
% A local maximum must be greater than the interpolated values of its
% adjacent elements along the direction orthogonal to this orie... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | multiscalePb.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/multiscalePb.m | 5,367 | utf_8 | 408d093af275cac245a7748624f46b21 | function [mPb_nmax, mPb_nmax_rsz, bg1, bg2, bg3, cga1, cga2, cga3, cgb1, cgb2, cgb3, tg1, tg2, tg3, textons] = multiscalePb(im, rsz)
%function [mPb_nmax, mPb_nmax_rsz, bg1, bg2, bg3, cga1, cga2, cga3, cgb1, cgb2, cgb3, tg1, tg2, tg3, textons] = multiscalePb(im, rsz)
%
% description:
% compute local contour cues of an i... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | mPb_from_cues.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/mPb_from_cues.m | 2,595 | utf_8 | 2251f35fe181a45235f5eeeb417bff50 | % compute multiscale pb from cues
function [mPb_nmax, mPb_nmax_rsz] = mPb_from_cues(bg1, bg2, bg3, cga1, cga2, cga3, cgb1, cgb2, cgb3, tg1, tg2, tg3, rsz);
weights = [0.0146 0.0145 0.0163 0.0210 0.0243 0.0287 0.0166 0.0185 0.0204 0.0101 0.0111 0.0141];
% get size of finest scale
[sx s... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | Shoreline_extraction_color_Al.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/Shoreline_extraction_color_Al.m | 5,026 | utf_8 | 3fb609240616f132a49b8e06f248ed3e | %% Shoreline extraction using GlobalProbability of Boundary, and UCM
% (Ultrametric COntour Map) based on the max oriened gPb and Constrained
% Segmentation (see Arbelaez et al., 2011) modified.
function [seeds, obj_names, seg, cmap, seeds_ucm_img, bndry_img, data] = ...
Shoreline_extraction_color_Al(imag, seeds,... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | contours2ucm.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/contours2ucm.m | 5,756 | utf_8 | e72e0f924d2155a367125e0e8309f647 | function [ucm] = contours2ucm(pb_oriented, fmt)
% Creates Ultrametric Contour Map from oriented contours
%
% syntax:
% [ucm] = contours2ucm(pb_oriented, fmt)
%
% description:
% Computes UCM by considering
% the mean pb value on the boundary between regions as dissimilarity.
%
% arguments:
% pb_oriented: Orient... |
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