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github | cvlab-epfl/TILDE-master | vl_test_imarray.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_homkermap.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_slic.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_slic.m | 200 | utf_8 | 12a6465e3ef5b4bcfd7303cd8a9229d4 | 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) ;
|
github | cvlab-epfl/TILDE-master | vl_test_ikmeans.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_mser.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_inthist.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_imdisttf.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_vlad.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_vlad.m | 1,977 | utf_8 | d3797288d6edb1d445b890db3780c8ce | function results = vl_test_vlad(varargin)
% VL_TEST_VLAD
vl_test_init ;
function s = setup()
randn('state',0) ;
s.x = randn(128,256) ;
s.mu = randn(128,16) ;
assignments = rand(16, 256) ;
s.assignments = bsxfun(@times, assignments, 1 ./ sum(assignments,1)) ;
function test_basic (s)
x = [1, 2, 3] ;
mu = [0, 0, 0] ;
a... |
github | cvlab-epfl/TILDE-master | vl_test_pr.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_pr.m | 3,763 | utf_8 | 4d1da5ccda1a7df2bec35b8f12fdd620 | 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 | cvlab-epfl/TILDE-master | vl_test_hog.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_argparse.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_liop.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_liop.m | 1,023 | utf_8 | a162be369073bed18e61210f44088cf3 | function results = vl_test_liop(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
randn('state',0) ;
s.patch = randn(65,'single') ;
xr = -32:32 ;
[x,y] = meshgrid(xr) ;
s.blob = - single(x.^2+y.^2) ;
function test_basic(s)
d = vl_liop(s.patch) ;
function test_blob(s)
% with a blob, all local intensity ord... |
github | cvlab-epfl/TILDE-master | vl_test_binsearch.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_roc.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/plotop/vl_roc.m | 8,747 | utf_8 | 6b8b4786c9242d5112ca90a616db507a | 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 | cvlab-epfl/TILDE-master | vl_click.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_pr.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/plotop/vl_pr.m | 9,135 | utf_8 | c5d1b9d67f843d10c0b2c6b48fab3c53 | 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 | cvlab-epfl/TILDE-master | vl_ubcread.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_frame2oell.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/sift/vl_frame2oell.m | 2,806 | utf_8 | c93792632f630743485fa4c2cf12d647 | function eframes = vl_frame2oell(frames)
% VL_FRAMES2OELL Convert a geometric frame to an oriented ellipse
% EFRAME = VL_FRAME2OELL(FRAME) converts the generic FRAME to an
% oriented ellipses EFRAME. FRAME and EFRAME can be matrices, with
% one frame per column.
%
% A frame is either a point, a disc, an orien... |
github | cvlab-epfl/TILDE-master | vl_plotsiftdescriptor.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/sift/vl_plotsiftdescriptor.m | 5,114 | utf_8 | a4e125a8916653f00143b61cceda2f23 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptor D. If D is a
% matrix, it plots one descriptor per column. D has the same format
% used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warpe... |
github | cvlab-epfl/TILDE-master | phow_caltech101.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/apps/phow_caltech101.m | 11,594 | utf_8 | 7f4890a2e6844ca56debbfe23cca64f3 | 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 speedu... |
github | cvlab-epfl/TILDE-master | sift_mosaic.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | encodeImage.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/apps/recognition/encodeImage.m | 5,278 | utf_8 | 5d9dc6161995b8e10366b5649bf4fda4 | function descrs = encodeImage(encoder, im, varargin)
% ENCODEIMAGE Apply an encoder to an image
% DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER
% to image IM, returning a corresponding code vector PSI.
%
% IM can be an image, the path to an image, or a cell array of
% the same, to operate on multiple ... |
github | cvlab-epfl/TILDE-master | experiments.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/apps/recognition/experiments.m | 6,905 | utf_8 | 1e4a4911eed4a451b9488b9e6cc9b39c | function experiments()
% EXPERIMENTS Run image classification experiments
% The experimens download a number of benchmark datasets in the
% 'data/' subfolder. Make sure that there are several GBs of
% space available.
%
% By default, experiments run with a lite option turned on. This
% quickly runs all... |
github | cvlab-epfl/TILDE-master | getDenseSIFT.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/apps/recognition/getDenseSIFT.m | 1,679 | utf_8 | 2059c0a2a4e762226d89121408c6e51c | function features = getDenseSIFT(im, varargin)
% GETDENSESIFT Extract dense SIFT features
% FEATURES = GETDENSESIFT(IM) extract dense SIFT features from
% image IM.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made availab... |
github | cvlab-epfl/TILDE-master | fast9.m | .m | TILDE-master/matlab/external/external_codes/methods/fast9.m | 230,457 | utf_8 | b2b8305ea2271460b1a53996daa3436d | %FAST9. perform an FAST corner detection from your FAST-ER generated tree
% [corners, scores] = FAST9.(image, threshold) performs the detection on the image
% and returns the X coordinates in corners(:,1), the Y coordinares in corners(:,2) and
% optionally, the scores in scores(:). The score is computed using... |
github | cvlab-epfl/TILDE-master | runKeypointsEdgeFoci.m | .m | TILDE-master/matlab/external/external_codes/methods/runKeypointsEdgeFoci.m | 1,605 | utf_8 | e15052b1a722a0117e23ad637f9064f6 | function [failed] = runKeypointsEdgeFoci(in_file_name, out_file_name)
failed = false;
global sRoot;
detector_path = [sRoot '/external/external_codes/methods/EdgeFociAndBiCE.exe'];
if (exist(detector_path) ~= 2)
failed = true;
return;
end
in_file_full_path = [in_file_name];... |
github | cvlab-epfl/TILDE-master | getKeypoints_TILDEP24.m | .m | TILDE-master/matlab/src/KeypointDetectors/TILDEP24/getKeypoints_TILDEP24.m | 3,754 | utf_8 | be4086a0db994daf2bd63bb3d314a405 | %% getKeypoints_TILDEP24.m ---
%
% Filename: getKeypoints_TILDEP24.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:21:28 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:21:34 2015 (+0200)
% By: Kwang
%... |
github | cvlab-epfl/TILDE-master | getKeypoints_MSER.m | .m | TILDE-master/matlab/src/KeypointDetectors/MSER/getKeypoints_MSER.m | 2,721 | utf_8 | 0f6dbd7b80025e0d5dc632cfdf0c3cac | %% getKeypoints_MSER.m ---
%
% Filename: getKeypoints_MSER.m
% Description: Wrapper function for MSER
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:18:09 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:19:52 2015 (+0200)
% ... |
github | cvlab-epfl/TILDE-master | getKeypoints_RANDOM.m | .m | TILDE-master/matlab/src/KeypointDetectors/RANDOM/getKeypoints_RANDOM.m | 1,491 | utf_8 | e3ebffb24c06b66d6ce05b5777d453b3 | %% getKeypoints_RANDOM.m ---
%
% Filename: getKeypoints_RANDOM.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:18:28 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:18:33 2015 (+0200)
% By: Kwang
% ... |
github | cvlab-epfl/TILDE-master | getKeypoints_WADE.m | .m | TILDE-master/matlab/src/KeypointDetectors/WADE/getKeypoints_WADE.m | 1,845 | utf_8 | 165f73e86af44f9461f4080511b790e0 | %% getKeypoints_WADE.m ---
%
% Filename: getKeypoints_WADE.m
% Description: Wrapper Function for WADE
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:21:38 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:21:50 2015 (+0200)
% ... |
github | cvlab-epfl/TILDE-master | getKeypoints_SIFER.m | .m | TILDE-master/matlab/src/KeypointDetectors/SIFER/getKeypoints_SIFER.m | 2,282 | utf_8 | 9fc60d011256da238417ba02b4ebd473 | %% getKeypoints_SIFER.m ---
%
% Filename: getKeypoints_SIFER.m
% Description: Wrapper Function for SIFER
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:20:14 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:20:29 2015 (+0200)
% ... |
github | cvlab-epfl/TILDE-master | getKeypoints_SIFT.m | .m | TILDE-master/matlab/src/KeypointDetectors/SIFT/getKeypoints_SIFT.m | 2,263 | utf_8 | f61ecf9d0b64f21cc90fcce27d345125 | %% getKeypoints_SIFT.m ---
%
% Filename: getKeypoints_SIFT.m
% Description: Wrapper Function for SIFT
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:20:35 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Fri Aug 28 15:03:43 2015 (+0200)
% ... |
github | cvlab-epfl/TILDE-master | getKeypoints_SFOP.m | .m | TILDE-master/matlab/src/KeypointDetectors/SFOP/getKeypoints_SFOP.m | 1,716 | utf_8 | d66360550570f9c5f66347afb42b369a | %% getKeypoints_SFOP.m ---
%
% Filename: getKeypoints_SFOP.m
% Description: Wrapper Function for SFOP
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:18:38 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:19:04 2015 (+0200)
% ... |
github | cvlab-epfl/TILDE-master | getKeypoints_SURF.m | .m | TILDE-master/matlab/src/KeypointDetectors/SURF/getKeypoints_SURF.m | 2,412 | utf_8 | dbd407572eb1a2a3abe0c3f18faa19a0 | %% getKeypoints_SURF.m ---
%
% Filename: getKeypoints_SURF.m
% Description: Wrapper Function for SURF
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:20:54 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Fri Aug 28 15:05:31 2015 (+0200)
% ... |
github | cvlab-epfl/TILDE-master | getKeypoints_EdgeFoci.m | .m | TILDE-master/matlab/src/KeypointDetectors/EdgeFoci/getKeypoints_EdgeFoci.m | 2,243 | utf_8 | 6e7eee687a35745ef9353f6f9eb029b2 | %% getKeypoints_EdgeFoci.m ---
%
% Filename: getKeypoints_EdgeFoci.m
% Description: Wrapper Function for EdgeFoci
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:16:41 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:19:23 2015 (+... |
github | cvlab-epfl/TILDE-master | getKeypoints_FAST.m | .m | TILDE-master/matlab/src/KeypointDetectors/FAST/getKeypoints_FAST.m | 2,425 | utf_8 | 6f542c8a816e111181034ead254c0b76 | %% getKeypoints_FAST.m ---
%
% Filename: getKeypoints_FAST.m
% Description: Wrapper Function for FAST
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:16:52 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:19:36 2015 (+0200)
% ... |
github | cvlab-epfl/TILDE-master | getKeypoints_TILDEP.m | .m | TILDE-master/matlab/src/KeypointDetectors/TILDEP/getKeypoints_TILDEP.m | 2,441 | utf_8 | 8155bd7809431b1ac6320eeed6c7ffe0 | %% getKeypoints_TILDEP.m ---
%
% Filename: getKeypoints_TILDEP.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:21:16 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:21:22 2015 (+0200)
% By: Kwang
% ... |
github | cvlab-epfl/TILDE-master | getKeypoints_LearnedConvolutional.m | .m | TILDE-master/matlab/src/KeypointDetectors/LearnedConvolutional/getKeypoints_LearnedConvolutional.m | 2,683 | utf_8 | b803b21e75323289c01e7a4d9d228189 | %% getKeypoints_LearnedConvolutional.m ---
%
% Filename: getKeypoints_LearnedConvolutional.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:17:08 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:18:04 2015 (+0200)... |
github | cvlab-epfl/TILDE-master | computeKP.m | .m | TILDE-master/matlab/src/Utils/computeKP.m | 4,098 | utf_8 | 728a9ae48eeba90298ef21e871b87d49 | %% computeKP.m ---
%
% Filename: computeKP.m
% Description:
% Author: Yannick Verdie, Kwang Moo Yi
% Maintainer: Yannick Verdie, Kwang Moo Yi
% Created: Tue Jun 16 17:13:09 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Fri Aug 28 14:32:15 2015 (+0200)
% By: Kwang
% Update #: 2
% URL:... |
github | cvlab-epfl/TILDE-master | PreProcessTrainImage.m | .m | TILDE-master/matlab/src/Utils/PreProcessTrainImage.m | 2,341 | utf_8 | 4001f19220904998c9a592544e196d94 | %% PreProcessTrainImage.m ---
%
% Filename: PreProcessTrainImage.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:12:53 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:12:58 2015 (+0200)
% By:... |
github | cvlab-epfl/TILDE-master | ApplyLearnedFilterWithSVM_NoLoadFile.m | .m | TILDE-master/matlab/src/Utils/ApplyLearnedFilterWithSVM_NoLoadFile.m | 4,883 | utf_8 | 04e2e120d8d7d460ed62631ac7a0dd92 | %% ApplyLearnedFilterWithSVM_NoLoadFile.m ---
%
% Filename: ApplyLearnedFilterWithSVM_NoLoadFile.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:12:41 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:12:... |
github | cvlab-epfl/TILDE-master | evaluateKeyPointsCompWithDir_ratio.m | .m | TILDE-master/matlab/src/Utils/evaluateKeyPointsCompWithDir_ratio.m | 8,147 | utf_8 | ab8e61b3bd6adeaea97e70dab58ef5ec | %% evaluateKeyPointsCompWithDir_ratio.m ---
%
% Filename: evaluateKeyPointsCompWithDir_ratio.m
% Description:
% Author: Yannick Verdie, Kwang Moo Yi
% Maintainer: Yannick Verdie, Kwang Moo Yi
% Created: Tue Jun 16 17:13:51 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Fri Aug 28 14:33:33 2015 (+020... |
github | cvlab-epfl/TILDE-master | ApplyLearnedELLFilter.m | .m | TILDE-master/matlab/src/Utils/ApplyLearnedELLFilter.m | 1,516 | utf_8 | c5f1a3106901939bee40925a4e8c0ed1 | %% ApplyLearnedELLFilter.m ---
%
% Filename: ApplyLearnedELLFilter.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Author: Kwang
% Maintainer:
% Created: Tue Jun 16 17:12:09 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:... |
github | cvlab-epfl/TILDE-master | ApplyLearnedELLFilterWithPreProcImage_NoLoadFile.m | .m | TILDE-master/matlab/src/Utils/ApplyLearnedELLFilterWithPreProcImage_NoLoadFile.m | 3,460 | utf_8 | 291be0d0684e955aba712e6564769d21 | %% ApplyLearnedELLFilterWithPreProcImage_NoLoadFile.m ---
%
% Filename: ApplyLearnedELLFilterWithPreProcImage_NoLoadFile.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:12:31 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Up... |
github | cvlab-epfl/TILDE-master | derivative5.m | .m | TILDE-master/matlab/src/Utils/derivative5.m | 4,860 | utf_8 | e895bdf27bfc3b70b93d3c5968c41bba | % DERIVATIVE5 - 5-Tap 1st and 2nd discrete derivatives
%
% This function computes 1st and 2nd derivatives of an image using the 5-tap
% coefficients given by Farid and Simoncelli. The results are significantly
% more accurate than MATLAB's GRADIENT function on edges that are at angles
% other than vertical or horizont... |
github | cvlab-epfl/TILDE-master | PreProcessTrainImageSingleScale.m | .m | TILDE-master/matlab/src/Utils/PreProcessTrainImageSingleScale.m | 8,778 | utf_8 | 873adead1e2796dbb4175624ff2e008c | %% PreProcessTrainImageSingleScale.m ---
%
% Filename: PreProcessTrainImageSingleScale.m
% Description: Function which takes care of multiple channel composition
% Author: Kwang
% Maintainer:
% Created: Thu Jan 15 10:33:30 2015 (+0100)
% Version:
% Package-Requires: Separable filters, Dolar Toolbox, mexUtil... |
github | cvlab-epfl/TILDE-master | fastELLFiltering.m | .m | TILDE-master/matlab/src/Utils/tools_filtering/fastELLFiltering.m | 2,619 | utf_8 | c06f37de41faa7e26c51ec89cd3c4229 | %% fastELLFiltering.m ---
%
% Filename: fastELLFiltering.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:15:18 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:15:22 2015 (+0200)
% By: Kwang
% Updat... |
github | cvlab-epfl/TILDE-master | fastELLFiltering_approx.m | .m | TILDE-master/matlab/src/Utils/tools_filtering/fastELLFiltering_approx.m | 5,737 | utf_8 | 6ede14936d9e4cc48a5a8668abd78cee | %% fastELLFiltering_approx.m ---
%
% Filename: fastELLFiltering_approx.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:15:26 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:15:30 2015 (+0200)
% By: Kwa... |
github | cvlab-epfl/TILDE-master | binary2pts.m | .m | TILDE-master/matlab/src/Utils/tools_nonmax/binary2pts.m | 1,043 | utf_8 | 7085ad81cb552e31d7a33027c173596f | %% binary2pts.m ---
%
% Filename: binary2pts.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:16:18 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:16:23 2015 (+0200)
% By: Kwang
% Update #: 1
% URL... |
github | cvlab-epfl/TILDE-master | ApplyAdaptiveNonMax.m | .m | TILDE-master/matlab/src/Utils/tools_nonmax/ApplyAdaptiveNonMax.m | 1,293 | utf_8 | 21a91966661c1f7d60c5100c470e98ca | %% ApplyAdaptiveNonMax.m ---
%
% Filename: ApplyAdaptiveNonMax.m
% Description:
% Author: Yannick Verdie, Kwang Moo Yi
% Maintainer: Yannick Verdie, Kwang Moo Yi
% Created: Tue Jun 16 17:15:42 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:15:46 2015 (+0200)
% By: Kwang
% ... |
github | cvlab-epfl/TILDE-master | adaptiveNMSWithPoints.m | .m | TILDE-master/matlab/src/Utils/tools_nonmax/adaptiveNMSWithPoints.m | 2,966 | utf_8 | 9f4f2d5231b829132c3a67accc9e8dde | %% adaptiveNMSWithPoints.m ---
%
% Filename: adaptiveNMSWithPoints.m
% Description:
% Author: Yannick Verdie, Kwang Moo Yi
% Maintainer: Yannick Verdie, Kwang Moo Yi
% Created: Tue Jun 16 17:16:07 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:16:11 2015 (+0200)
% By: Kwang
%... |
github | cvlab-epfl/TILDE-master | pts2binary.m | .m | TILDE-master/matlab/src/Utils/tools_nonmax/pts2binary.m | 1,069 | utf_8 | a5d0b7d48d79dac980f82a6d69279bbc | %% pts2binary.m ---
%
% Filename: pts2binary.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:16:27 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:16:33 2015 (+0200)
% By: Kwang
% Update #: 1
% URL... |
github | cvlab-epfl/TILDE-master | ApplyNonMax2Score.m | .m | TILDE-master/matlab/src/Utils/tools_nonmax/ApplyNonMax2Score.m | 4,684 | utf_8 | 924ffce59cd896ebc1bb0fa9826d991c | %% ApplyNonMax2Score.m ---
%
% Filename: ApplyNonMax2Score.m
% Description:
% Author: Kwang Moo Yi, Yannick Verdie
% Maintainer: Kwang Moo Yi, Yannick Verdie
% Created: Tue Jun 16 17:15:56 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:16:02 2015 (+0200)
% By: Kwang... |
github | cvlab-epfl/TILDE-master | sortFeaturesSS_noAbs.m | .m | TILDE-master/matlab/src/Utils/tools_evaluate/sortFeaturesSS_noAbs.m | 2,080 | utf_8 | 781bc1f5332fcd01bb97035d2cd44535 | %% sortFeaturesSS_noAbs.m ---
%
% Filename: sortFeaturesSS_noAbs.m
% Description:
% Author: Yannick Verdie, Kwang Moo Yi
% Maintainer: Yannick Verdie, Kwang Moo Yi
% Created: Tue Jun 16 17:14:58 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:15:02 2015 (+0200)
% By: Kwang
% ... |
github | cvlab-epfl/TILDE-master | growAndScore.m | .m | TILDE-master/matlab/src/Utils/tools_evaluate/growAndScore.m | 1,935 | utf_8 | 338400bf641f0fc6ae6634bafdabc248 | %% growAndScore.m ---
%
% Filename: growAndScore.m
% Description:
% Author: Yannick Verdie, Kwang Moo Yi
% Maintainer: Yannick Verdie, Kwang Moo Yi
% Created: Tue Jun 16 17:14:27 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:14:32 2015 (+0200)
% By: Kwang
% Update #: 1
%... |
github | cvlab-epfl/TILDE-master | evaluate.m | .m | TILDE-master/matlab/src/Utils/tools_evaluate/evaluate.m | 2,126 | utf_8 | c1ca368d10a0a85bf931069157a74468 | %% evaluate.m ---
%
% Filename: evaluate.m
% Description:
% Author: Yannick Verdie, Kwang Moo Yi
% Maintainer: Yannick Verdie, Kwang Moo Yi
% Created: Tue Jun 16 17:14:18 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Fri Aug 28 14:42:28 2015 (+0200)
% By: Kwang
% Update #: 3
% URL:
%... |
github | cvlab-epfl/TILDE-master | sortFeaturesSS.m | .m | TILDE-master/matlab/src/Utils/tools_evaluate/sortFeaturesSS.m | 2,060 | utf_8 | 5e19d56b8c00f2260c348ec1e1feeaa1 | %% sortFeaturesSS.m ---
%
% Filename: sortFeaturesSS.m
% Description:
% Author: Yannick Verdie, Kwang Moo Yi
% Maintainer: Yannick Verdie, Kwang Moo Yi
% Created: Tue Jun 16 17:14:46 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:14:50 2015 (+0200)
% By: Kwang
% Update #:... |
github | cvlab-epfl/TILDE-master | mergeScoreImg2Keypoints.m | .m | TILDE-master/matlab/src/Utils/tools_evaluate/mergeScoreImg2Keypoints.m | 1,099 | utf_8 | 846cf1275c387006d39167b8de18f62e | %% mergeScoreImg2Keypoints.m ---
%
% Filename: mergeScoreImg2Keypoints.m
% Description:
% Author: Yannick Verdie, Kwang Moo Yi
% Maintainer: Yannick Verdie, Kwang Moo Yi
% Created: Tue Jun 16 17:14:35 2015 (+0200)
% Version:
% Package-Requires: ()
% Last-Updated: Tue Jun 16 17:14:42 2015 (+0200)
% By: Kwa... |
github | cvlab-epfl/TILDE-master | repeatability_noLoadFile.m | .m | TILDE-master/matlab/src/Utils/tools_evaluate/repeatability/repeatability_noLoadFile.m | 9,160 | utf_8 | 47f9981c00a15981074197f78366fed0 | function [erro,repeat,corresp, match_score,matches, twi]= ...
repeatability_noLoadFile(f1,f2,H,im1,im2, range, repeatabilityType)
if ~exist('range', 'var')
range = 5;
end
if ~exist('repeatabilityType','var')
repeatabilityType = 'RADIUS';
end
%Modified from the original version to not load files
% remember th... |
github | bock42/matMRI-master | GetGITInfo.m | .m | matMRI-master/GetGITInfo.m | 2,739 | utf_8 | b9607d082028b5dd8eb2248873166839 | function gitInfo = GetGITInfo(directory)
% gitInfo = GetGITInfo(directory)
%
% Description:
% Retrieves the git information on a specified directory or file. This is
% essentially a wrapper around the shell command "git".
%
% Input:
% directory (string) - Directory name of interest.
%
% Output:
% gitInfo (struct) - St... |
github | vllab/discovery-master | learning_UFL_model.m | .m | discovery-master/learning_UFL_model.m | 12,243 | utf_8 | 70da09ee8fccf7e19f91f0b39ca1e2e2 | function learning_UFL_model()
% script_siamese_KITTI_ZF()
% Siamese training and testing with part of Zeiler & Fergus model
% --------------------------------------------------------
clc;
clear mex;
clear is_valid_handle; % to clear init_key
%%addpath matlab library
addpath(addpath(genpath('../matlablib/edges-master')... |
github | vllab/discovery-master | showboxes.m | .m | discovery-master/utils/showboxes.m | 2,624 | utf_8 | be6b3bca7e6364f27e7ac8d3f76a3628 | function showboxes(im, boxes, legends, color_conf)
% Draw bounding boxes on top of an image.
% showboxes(im, boxes)
%
% -------------------------------------------------------
fix_width = 800;
if isa(im, 'gpuArray')
im = gather(im);
end
imsz = size(im);
scale = fix_width / imsz(2);
im = imresize(im, scale);
if ... |
github | vllab/discovery-master | ManifoldRanking.m | .m | discovery-master/code/Saliency/ManifoldRanking.m | 2,325 | utf_8 | 7253c0743f65e50919f417a17237d033 | function [stage2, stage1, bsalt, bsalb, bsall, bsalr] = ManifoldRanking(adjcMatrix, idxImg, bdIds, colDistM)
% The core function for Manifold Ranking Saliency:
% C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang. Saliency
% detection via graph-based manifold ranking. In CVPR, 2013.
% Code Author: Wangjiang Zhu
% Emai... |
github | vllab/discovery-master | GeodesicSaliency.m | .m | discovery-master/code/Saliency/GeodesicSaliency.m | 2,569 | utf_8 | 3b70356a49bc4ae0f632b64b35c90d40 | function geoDist = GeodesicSaliency(adjcMatrix, bdIds, colDistM, posDistM, clip_value)
% The core function for Geodesic Saliency Algorithm:
% Y.Wei, F.Wen,W. Zhu, and J. Sun. Geodesic saliency using background
% priors. In ECCV, 2012.
% Code Author: Wangjiang Zhu
% Email: wangjiang88119@gmail.com
% Date: 3/24/2014
sp... |
github | vllab/discovery-master | colorspace.m | .m | discovery-master/code/Saliency/Funcs/colorspace.m | 13,590 | utf_8 | b1a9eb973fa39950345a1df707b5d2c8 | function varargout = colorspace(Conversion,varargin)
%COLORSPACE Convert a color image between color representations.
% B = COLORSPACE(S,A) converts the color representation of image A
% where S is a string specifying the conversion. S tells the
% source and destination color spaces, S = 'dest<-src', or
% alt... |
github | vllab/discovery-master | extract_feature.m | .m | discovery-master/code/util/extract_feature.m | 2,395 | utf_8 | 5c7158bcd834eed238f9ab38c1956169 | function [feat,data]= extract_feature(conf, caffe_net, im, boxes,varargin)
%varargin : other layer data output
% --------------------------------------------------------
% Faster R-CNN
% Copyright (c) 2015, Shaoqing Ren
% Licensed under The MIT License [see LICENSE for details]
% ---------------------------------------... |
github | vllab/discovery-master | tool_dist.m | .m | discovery-master/code/util/tool_dist.m | 5,164 | utf_8 | 313d9e0e04b6556fe7ae20d4f76a1141 | function D = tool_dist( X, Y, metric )
% Calculates the distance between sets of vectors.
%
% Let X be an m-by-p matrix representing m points in p-dimensional space
% and Y be an n-by-p matrix representing another set of points in the same
% space. This function computes the m-by-n distance matrix D where D(i,j)
% is t... |
github | vllab/discovery-master | featpyramid.m | .m | discovery-master/code/features/featpyramid.m | 2,229 | utf_8 | 3c343c5e8029b129e5ac031c1f53fdab | function pyra = featpyramid(im, model)
% pyra = featpyramid(im, model, padx, pady);
% Compute feature pyramid.
%
% pyra.feat{i} is the i-th level of the feature pyramid.
% pyra.scales{i} is the scaling factor used for the i-th level.
% pyra.feat{i+interval} is computed at exactly half the resolution of feat{i}.
% first... |
github | vllab/discovery-master | extract_segfeat_hog.m | .m | discovery-master/code/features/extract_segfeat_hog.m | 2,092 | utf_8 | 96170fc97c4a0ad494927a8e4b4caf06 | function [ feat ] = extract_segfeat_hog(img, seg)
%% extract hog features from segments
% initialize structs
feat = struct;
% compute HOG features
szCell = 8;
nX=8; nY=8;
nDim = nX*nY*31;
hist_temp = zeros(size(seg.coords,1), nDim);
%im_patch_pad = ones(szCell*(nY+2),szCell*(nX+2),3);
%load('./who2/bg11.mat');
pixel... |
github | vllab/discovery-master | whiten.m | .m | discovery-master/code/features/whiten.m | 1,693 | utf_8 | f8e3dcd66d8a39f584494ed1e977350f | % function [R,neg] = whiten(bg,nx,ny)
% Obtain whitenixng matrix and mean from a general HOG model
% by a cholesky decompoition on a stationairy covariance matrix
% feat' = R\(feat - neg) has zero mean and unit covariance
%
% bg.neg: negative mean (nf by 1)
% bg.cov: covariance for k spatial offsets (nf by nf by k)... |
github | vllab/discovery-master | roidb_from_voc.m | .m | discovery-master/imdb/roidb_from_voc.m | 7,325 | utf_8 | ef87ae9f2d80c96ec3b8e885bff99d7c | function roidb = roidb_from_voc(imdb, varargin)
% roidb = roidb_from_voc(imdb, rootDir)
% Builds an regions of interest database from imdb image
% database. Uses precomputed selective search boxes available
% in the R-CNN data package.
%
% Inspired by Andrea Vedaldi's MKL imdb and roidb code.
% AUTORIGHTS
% --... |
github | vllab/discovery-master | classification_demo.m | .m | discovery-master/external/caffe/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | tommysprague/iEye_ts-master | i2d.m | .m | iEye_ts-master/i2d.m | 2,691 | utf_8 | b255ebc4471270b7bcd6a317c77a8607 | function varargout = i2d(varargin)
% I2D MATLAB code for i2d.fig
% I2D, by itself, creates a new I2D or raises the existing
% singleton*.
%
% H = I2D returns the handle to a new I2D or the handle to
% the existing singleton*.
%
% I2D('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | tommysprague/iEye_ts-master | iEye.m | .m | iEye_ts-master/iEye.m | 26,781 | utf_8 | d968661d6380c6927f214f6647eb0d1d | function varargout = iEye(varargin)
% IEYE_DISPLAY M-file for iEye_Display.fig
% IEYE_DISPLAY, by itself, creates a new IEYE_DISPLAY or raises the existing
% singleton*.
%
% H = IEYE_DISPLAY returns the handle to a new IEYE_DISPLAY or the handle to
% the existing singleton*.
%
% IEYE_DISPLAY('C... |
github | tommysprague/iEye_ts-master | SplitVec.m | .m | iEye_ts-master/select/SplitVec.m | 9,026 | utf_8 | 823a7fd79ec2827e688056b90b5921bf | function varargout = SplitVec(v, fun, varargin)
% [out1, out2, ...] = SplitVec(V, FUN, type1, type2, ...)
% [out1, out2, ...] = SplitVec(V, COL, type1, type2, ...)
%
% Purpose: Partition an input vector V into smaller series of subvectors
% of consecutive elements based on split points
%
% EXAMPLE:
% ... |
github | endsley/ml_examples-master | spectral_clustering.m | .m | ml_examples-master/spectral_clustering/spectral_clustering/spectral_clustering.m | 609 | utf_8 | e50835bc71e2017e9ed6ddff256b91dd |
% Data is assume to have each row as a single sample
function allocation = spectral_clustering(data, num_clusters, sigma)
N = size(data,1);
K = zeros(N,N);
for a = 1:N
for b= 1:N
K(a,b) = exp(-((data(a,:) - data(b,:))*(data(a,:) - data(b,:))')/(2*sigma));
end
end
D = diag(1./sqrt(sum(K)));
L = D*K*D;
... |
github | endsley/ml_examples-master | segment_image.m | .m | ml_examples-master/spectral_clustering/find_k/segment_image.m | 3,428 | utf_8 | 192bc3b9f823142a0e0123f220fa604e |
function [mask,clusts,Quality,D,W] = segment_image(IM,R,nGroups,method1,method2,varargin);
%% intensity based image segmentation
%% [mask,clusts,Quality,D,W] = seg_image(IM,R,nGroups,method1,method2,varargin)
%%
%% Input:
%% IM: image to segment
%% R: neighborhood of connectivity
%% nGroups... |
github | endsley/ml_examples-master | addborder.m | .m | ml_examples-master/spectral_clustering/find_k/addborder.m | 3,044 | utf_8 | 3f46875ac5de8e37786a675dfd98662a | %%%%%
function imbig = addborder(im,xbdr,ybdr,arg);
% imnew = addborder(im,xborder,yborder,arg) Make image w/added border.
% imnew = addborder(im,5,5,128) Add 5 wide border of val 128.
% imnew = addborder (im,5,5,'even') Even reflection.
% imnew = addborder (im,5,5,'odd') Odd reflection.
... |
github | endsley/ml_examples-master | evecs.m | .m | ml_examples-master/spectral_clustering/find_k/evecs.m | 1,034 | utf_8 | 692ea109205329dff86c7bb4523d5494 |
function [V,ss,L] = evecs(A,nEvecs)
%% calculate eigenvectors, eigenvalues of the laplaican of A
%%
%% [V,ss,L] = evecs(A,nEvecs)
%%
%% Input:
%% A = Affinity matrix
%% nEvecs = number of eigenvectors to compute
%%
%% Output:
%% V = eigenvectors
%% ss = eigenvalues
%% ... |
github | endsley/ml_examples-master | cluster_rotate.m | .m | ml_examples-master/spectral_clustering/find_k/cluster_rotate.m | 1,802 | utf_8 | 6503848de67e091d1fee109e4a32dbd8 |
function [clusts,best_group_index,Quality,Vr] = cluster_rotate(A,group_num,fig,method)
%% cluster by rotating eigenvectors to align with the canonical coordinate
%% system
%%
%% [clusts,best_group_index,Quality,Vr] = cluster_rotate(A,group_num,method,fig)
%%
%% Input:
%% A = Affinity matrix
%% grou... |
github | endsley/ml_examples-master | display_clust_image_2.m | .m | ml_examples-master/spectral_clustering/find_k/display_clust_image_2.m | 1,594 | utf_8 | 44594ac61783bf6cf7ec46c836b978bb |
function [IM_result]=display_clust_image(IM,input_mask,fig)
% [IMseg]=display_clust_image(IM,input_mask,fig)
% display image segmentation results:
% input :
% IM = input image
% input_mask = segmentation mask
% fig = matlab figure number for display
%
% output:
% IMseg = ... |
github | endsley/ml_examples-master | imdist.m | .m | ml_examples-master/spectral_clustering/find_k/imdist.m | 1,756 | utf_8 | e5f5bb565d9850f0db83e43b38e1791d |
function [D,ind_non_zero,rows_nonz,cols_nonz] = imdist(IM,R)
%
% [D,ind_non_zero,rows_nonz,cols_nonz] = imdist(IM,R)
% build a sparse distance matrix for image IM connecting pixels within
% radius R
%
% Lihi Zelnik-Manor, March 2005, Caltech
%
[rows,cols,colors] = size(IM);
[x,y] = meshgrid(1:rows,1:cols);
ndata = ro... |
github | endsley/ml_examples-master | plot_2d_data.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/plot_2d_data.m | 1,322 | utf_8 | 84bebda45e695483849bdb1acb9d1cea |
function plot_2d_data(assignment, data, N, figure_id)
dot_type = 'o';
cmap = colormap('default');
tmp_fig = figure(figure_id);
%set(tmp_fig, 'visible','Off')
hold on;
for m = 1:N
if(assignment(m) == 1)
%printf('plot 1\n')
plot(data(1,m), data(2, m),['r' dot_type]);
elseif(assignment(m) == 2)
%print... |
github | endsley/ml_examples-master | generate_distribution.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/generate_distribution.m | 903 | utf_8 | e26f8302bdbd1554db22be1edbeddd7b |
function [y_total, y_normalized] = generate_distribution(N, sigma, sigma_2, x1,y1)
epsilon = 0.000001;
% Create original y
A = [ones(length(x1),1) x1 x1.^2 x1.^3 x1.^4 x1.^5 x1.^6];
[q r] = qr(A);
coef = r\(q'*y1);
x_lower = min(x1);
x_upper = max(x1);
x = [0:99]';
A = [ones(length(x),1) x x.^2 x.^3 x.^4 x... |
github | endsley/ml_examples-master | plot_cluster_results.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/plot_cluster_results.m | 827 | utf_8 | 0ffbb32c00232467ec6f41002527ab79 |
function plot_cluster_results(x, assignment, data, N, figure_id)
dot_type = '';
figure(figure_id);
hold on;
for m = 1:N
if(assignment(m) == 1)
%printf('plot 1\n')
plot(x, data(:, m),['r' dot_type]);
elseif(assignment(m) == 2)
%printf('plot 2\n')
plot(x, data(:, m),['g' dot_type]);
elseif(assignm... |
github | endsley/ml_examples-master | l1_ls.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/l1_ls.m | 8,414 | utf_8 | 592cd5d633c7f3e474bcad9309e4ea07 | function [x,status,history] = l1_ls(A,varargin)
%
% l1-Regularized Least Squares Problem Solver
%
% l1_ls solves problems of the following form:
%
% minimize ||A*x-y||^2 + lambda*sum|x_i|,
%
% where A and y are problem data and x is variable (described below).
%
% CALLING SEQUENCES
% [x,status,history] = l1... |
github | endsley/ml_examples-master | get_menger_curvature.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/get_menger_curvature.m | 1,494 | utf_8 | 4063c24f083b32860e13f33ca080aedb |
function curvature_function = get_menger_curvature(data)
inc = 5;
curvature_function = [];
filter_len = 10;
for m = 1:length(data)
if (m-inc) < 1 % avoid the first points due to edge conditions
curvature_function = [curvature_function, 0];
elseif (m+inc) > length(data)
curvature_function = [curvature_fu... |
github | endsley/ml_examples-master | spectral_fit.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/spectral_fit.m | 856 | utf_8 | 83ad83323a1b5ee80f1fc43b77c8a507 |
function [centroid, pointsInCluster, assignment] = spectral_fit(Adjacency_matrix, cluster_N)
%Adjacency_matrix = [1 1 0 0 0;1 1 0 0 0;0 0 1 1 0; 0 0 1 1 1;0 0 0 1 1]
%------------------------------
Degree_matrix = diag(sum(Adjacency_matrix));
% Shi and Malik Method
% Find max inv(D)*W
Laplacian = Degree_mat... |
github | endsley/ml_examples-master | sample_data_generation.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/sample_data_generation.m | 10,905 | utf_8 | e9cbe78cf84bcb7b8503f7142be081b5 |
function [y_normalized, y_total, N, labels, time_series_data] = sample_data_generation(data_set_id, plot_data)
original_view = 1;
number_of_data_per_type = 20;
time_series_data = 1;
if(plot_data == 1)
%figure(1, "position", get(0,"screensize")([3,4,3,4]).*[0 0 0.4 0.4]);
end
if(data_set_id == 1)
sigma1 =... |
github | endsley/ml_examples-master | get_curvature_angle.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/get_curvature_angle.m | 835 | utf_8 | 2f28b183e84221ad17a2ec250e0fc533 |
function angle = get_curvature_angle(point, curv)
left_p = point - 5;
right_p = point - 4;
y = [curv(left_p); curv(right_p)];
A = [[left_p;right_p], [1;1]];
[q r] = qr(A);
coef = r\(q'*y);
direction_1 = [1, coef(1)];
left_p = point - 10;
right_p = point + 10;
pp = [[left_p:right_p]', ones(length([left_p:r... |
github | endsley/ml_examples-master | get_chieh_curvature.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/get_chieh_curvature.m | 4,779 | utf_8 | 78b9b73b781b8823ef794529623d2848 |
function [curvature_function, cutoff_point] = get_chieh_curvature(data)
% inc = ceil(length(data)*0.02);
% curvature_function = [];
% cutoff_point = 10;
%
% for m = 1:length(data)
% %for m = 1:100
% m
% if (m-inc) < 1
% curvature_function = [curvature_function, 0];
% elseif ((m+inc) > length(data))
% curvatur... |
github | endsley/ml_examples-master | calc_Eucli_Distance_matrix.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/calc_Eucli_Distance_matrix.m | 366 | utf_8 | ce576470d1e46c3539c65daee7c3f799 |
% Each column in A is a single data point
function Euclid_matrix = calc_Eucli_Distance_matrix(A, use_L1)
N = size(A,2);
Euclid_matrix = [];
for m = 1:N
if(use_L1 == 1)
single_row = sum(abs(A - repmat(A(:,m), 1, N)));
else
D = abs(A - repmat(A(:,m), 1, N));
single_row = sqrt(sum(D.^2));
end
Euclid_m... |
github | endsley/ml_examples-master | evecs.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/evecs.m | 1,056 | utf_8 | 56eae66ead9c6febdf8b7aeb1f8388fb |
function [V,ss,L] = evecs(A,nEvecs)
%% calculate eigenvectors, eigenvalues of the laplaican of A
%%
%% [V,ss,L] = evecs(A,nEvecs)
%%
%% Input:
%% A = Affinity matrix
%% nEvecs = number of eigenvectors to compute
%%
%% Output:
%% V = eigenvectors
%% ss = eigenvalues
%% ... |
github | endsley/ml_examples-master | get_peaks.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/get_peaks.m | 2,235 | utf_8 | 2632f57af482335b685635f341e7fb34 |
function [cutoff_point, filtered_curv] = get_peaks(x, curv)
gap = ceil(length(x)*0.03);
%gap = 7;
filtered_curv = x;
filtered_curv(filtered_curv < 1.5*std(filtered_curv)) = 0;
cutoff_point = gap;
if(sum(filtered_curv) == 0)
filtered_curv = x;
filtered_curv(filtered_curv < std(filtered_curv)) = 0;
end
if... |
github | endsley/ml_examples-master | fft_filter.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/fft_filter.m | 383 | utf_8 | 5150bd5585cdaac27b5e16428f3b023c |
function out_matrix = fft_filter(A, reduction_percentage)
if reduction_percentage == 0
out_matrix = A;
return;
end
dat_size = size(A,1);
increments = floor(reduction_percentage*( dat_size - 1 )/2);
first = ceil((dat_size - 1)/2) + 1 - increments
second = ceil((dat_size - 1)/2 + 0.5) + 1 + increments
f = f... |
github | endsley/ml_examples-master | spectral_path_clustering.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/spectral_path_clustering.m | 1,969 | utf_8 | 8f86fb00f4f083cf7ac7a424d1e06bca | % Input argument
% A : is the data, where each sample is a single column
% EV_percentage : this controls what percentage of emphasis 1.00 is completely time domain and 0 is completely Freq domain
% remove_percentage : percentage of data we remove for variance map, 1 is 100%
% plot_it : 1 to display plot and 0, not to
%... |
github | endsley/ml_examples-master | cluster_rotate.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/cluster_rotate.m | 1,802 | utf_8 | 6503848de67e091d1fee109e4a32dbd8 |
function [clusts,best_group_index,Quality,Vr] = cluster_rotate(A,group_num,fig,method)
%% cluster by rotating eigenvectors to align with the canonical coordinate
%% system
%%
%% [clusts,best_group_index,Quality,Vr] = cluster_rotate(A,group_num,method,fig)
%%
%% Input:
%% A = Affinity matrix
%% grou... |
github | endsley/ml_examples-master | form_cluster_matrix.m | .m | ml_examples-master/spectral_clustering/Affinity_matrix_maker/path_cluster_lib/form_cluster_matrix.m | 209 | utf_8 | a047e7ea4a03cd89cb0f97db827bdc5d |
function cluster_matrix = form_cluster_matrix(labels)
labels = labels(:);
cluster_matrix = repmat(labels, 1, length(labels)) - repmat(labels',length(labels), 1);
cluster_matrix = cluster_matrix == 0;
end
|
github | endsley/ml_examples-master | get_Distance_in_Freq.m | .m | ml_examples-master/spectral_clustering/spectral_trajectory/path_cluster_lib/get_Distance_in_Freq.m | 821 | utf_8 | 2a96f3757a87c49f7e6ded40a326ce44 |
% Each column in A is a single data point
function out_matrix = get_Distance_in_Freq(A, weight, remove_percentage, tight_bound )
if weight == 0
out_matrix = [1];
return
end
A_out{1} = 0;
for m = 1:length(A)
len = floor(size(A{m},1)/2);
p = abs(fft(A{m}));
p = p(1:len, :);
p = variance_map_filter(p, re... |
github | endsley/ml_examples-master | plot_2d_data.m | .m | ml_examples-master/spectral_clustering/spectral_trajectory/path_cluster_lib/plot_2d_data.m | 1,203 | utf_8 | 4df794bb55da389b1caf830592af7d5a |
function plot_2d_data(assignment, data, N, figure_id)
dot_type = 'o';
cmap = colormap('default');
figure(figure_id);
hold on;
for m = 1:N
if(assignment(m) == 1)
%printf('plot 1\n')
plot(data(1,m), data(2, m),['r' dot_type]);
elseif(assignment(m) == 2)
%printf('plot 2\n')
plot(data(1,m), data(2, m... |
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