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github | jianxiongxiao/ProfXkit-master | vl_test_pr.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_hog.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_argparse.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_liop.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_binsearch.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_roc.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_click.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_pr.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_ubcread.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_frame2oell.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/toolbox/sift/vl_frame2oell.m | 2,160 | utf_8 | 457c5f2e8b637108c8c1b2256396de13 | function eframes = vl_frame2oell(frames)
% FRAMES2OELL Convert generic feature frames to oriented ellipses
% EFRAMES = VL_FRAME2OELL(FRAMES) converts the specified FRAMES to
% the oriented ellipses EFRAMES.
%
% A frame is either a point, disc, oriented disc, ellipse, or
% oriented ellipse. These are represene... |
github | jianxiongxiao/ProfXkit-master | vl_plotsiftdescriptor.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/toolbox/sift/vl_plotsiftdescriptor.m | 4,725 | utf_8 | 395bf4e0d7417674401ddf34cc8a70da | 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 | jianxiongxiao/ProfXkit-master | phow_caltech101.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/apps/phow_caltech101.m | 11,595 | utf_8 | cdd4c2add2b7bbfe66a43831513f99fc | 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 | jianxiongxiao/ProfXkit-master | sift_mosaic.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | encodeImage.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | experiments.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | getDenseSIFT.m | .m | ProfXkit-master/align2RGBD/align2RGBD/lib/vlfeat/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 | jianxiongxiao/ProfXkit-master | parseXMLstring.m | .m | ProfXkit-master/MatlabTurkTool/parseXMLstring.m | 7,033 | utf_8 | 7de86ff84d643a943e5ec4bc4a3cf2cb | function object = parseXMLstring(str)
fname = [tempname '.xml'];
fp = fopen(fname,'w');
fprintf(fp,'%s',str);
fclose(fp);
object = xml2struct(fname);
delete(fname);
end
% downloaded from http://www.mathworks.com/matlabcentral/fileexchange/28518-xml2struct
function [ s ] = xml2struct( file )
%Convert xml file into a... |
github | jianxiongxiao/ProfXkit-master | HMACencode.m | .m | ProfXkit-master/MatlabTurkTool/HMACencode.m | 879 | utf_8 | 208b549fd6a8159bbf99373ddf26346f | function encodedHMAC = HMACencode(str,key)
%encodedHMAC = urlEncode(doHMAC_SHA1(str, key));
encodedHMAC = doHMAC_SHA1(str, key);
end
function signStr = doHMAC_SHA1(str, key)
import java.net.*;
import javax.crypto.*;
import javax.crypto.spec.*;
import org.apache.commons.codec.binary.*
algorithm = 'HmacSHA1';
keyStr =... |
github | jianxiongxiao/ProfXkit-master | queryFlickrApi.m | .m | ProfXkit-master/querySearchEngine/queryFlickrApi.m | 5,548 | utf_8 | 0ca6a2d4afe92bd519260dfb228ddeba | function result = queryFlickrApi(original_keywords)
% example usage: result = queryFlickrApi();
% information: see flickr api page: https://www.flickr.com/services/api/
% https://www.flickr.com/services/api/explore/flickr.photos.search
if ~exist('original_keywords','var')
original_keywords = 'living room';
end
... |
github | jianxiongxiao/ProfXkit-master | readFlickrObj.m | .m | ProfXkit-master/querySearchEngine/queryFlickrOld/readFlickrObj.m | 1,754 | utf_8 | aa38ea7ca02068e6a36e93ef86852df9 | % this function will read a Bing download xml file and covert it
% into mysql batch command file
function outObj = readFlickrObj(inFname, outObj, topic_mid, keywords)
urlPhoto = 'http://farm%s.static.flickr.com/%s/%s_%s_b.jpg';
xDoc = xmlread(inFname);
allImages = xDoc.getElementsByTagName('photo');
for k = 0:all... |
github | jianxiongxiao/ProfXkit-master | readGoogleObj.m | .m | ProfXkit-master/querySearchEngine/queryGoogleCpp/readGoogleObj.m | 2,356 | utf_8 | d9abab20fdf480157d1218866b5fb270 | % this function will read a google download html file and covert it
% into mysql batch command file
%{
<a href="/imgres?imgurl=http://schools.archchicago.org/images/highschools/18.jpg&imgrefurl=http://schools.archchicago.org/schools/schoollistcity.aspx&h=266&w=324&sz=18&tbnid=o2GpciHX-BLS1M:&tb... |
github | jianxiongxiao/ProfXkit-master | drawCamera.m | .m | ProfXkit-master/drawCamera/drawCamera.m | 1,492 | utf_8 | 1fb3a23e9903c4d7fd994aa0863b8563 | function drawCamera()
close all
addpath ../icosahedron2sphere
points = icosahedron2sphere(1);
points = points(points(:,3)>=0,:);
%{
plot3(points(:,1),points(:,2),points(:,3),'.')
title(sprintf('Level %d with %d points',1,size(points,1)))
axis equal
axis tight
xlabel('x');
ylabel('y');
zlabel('z');
%}
aspect_ratio... |
github | jianxiongxiao/ProfXkit-master | transformPointCloud.m | .m | ProfXkit-master/RenderPCcamera/transformPointCloud.m | 130 | utf_8 | ebf18e96a2a3d9ca20da267e9d345dcd |
function XYZtransform = transformPointCloud(XYZ,Rt)
XYZtransform = Rt(1:3,1:3) * XYZ + repmat(Rt(1:3,4),1,size(XYZ,2));
end
|
github | jianxiongxiao/ProfXkit-master | transformPointCloud.m | .m | ProfXkit-master/WarpDepthMesh/transformPointCloud.m | 130 | utf_8 | ebf18e96a2a3d9ca20da267e9d345dcd |
function XYZtransform = transformPointCloud(XYZ,Rt)
XYZtransform = Rt(1:3,1:3) * XYZ + repmat(Rt(1:3,4),1,size(XYZ,2));
end
|
github | jianxiongxiao/ProfXkit-master | bilateralFilter.m | .m | ProfXkit-master/SiftFu/SiftFu/bilateralFilter.m | 7,891 | utf_8 | 456ce9778b227be5bc1eb6ed3b34ed36 | %
% 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 | jianxiongxiao/ProfXkit-master | projICP.m | .m | ProfXkit-master/SiftFu/SiftFu/projICP.m | 4,587 | utf_8 | 197ed7d4d038ab9947bf507d22a40379 | function camAAr2c = projICP(camAAr2c)
global VMap;
global NMap;
global XYZcamBilateral;
global NcamBilateral;
global K;
global NMapCam;
global VMapCam;
global Vku;
for iterID = 1:10
%% data association
% transform the point cloud
VMapCam = AngleAxisRota... |
github | jianxiongxiao/ProfXkit-master | SiftFu.m | .m | ProfXkit-master/SiftFu/SiftFu/SiftFu.m | 12,329 | utf_8 | 42b1ec7d5842dcbcf7a94ab1deddc050 | function newDepth = SiftFu(sequenceName, frameIDs)
%{
Please cite the following paper if you use this code
Citation:
J. Xiao, A. Owens and A. Torralba
SUN3D Database: Semantic RGB-D Bundle Adjustment with Human in the Loop
Proceedings of 14th IEEE International Conference on Computer Vision (ICCV2013)
%}
addpath(ge... |
github | jianxiongxiao/ProfXkit-master | estimateRt.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/estimateRt.m | 501 | utf_8 | d7ad6f4ea024b18ceb9915fec69b9a71 | % Usage: Rt = estimateRt(x1, x2)
% Rt = estimateRt(x)
%
% Arguments:
% x1, x2 - Two sets of corresponding 3xN set of homogeneous
% points.
%
% x - If a single argument is supplied it is assumed that it
% is in the form x = [x1; x2]
% Returns:
% ... |
github | jianxiongxiao/ProfXkit-master | ransacfitRt.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/ransacfitRt.m | 2,778 | utf_8 | 07e66db36ff0d62d460c90f54e34bc7b | % Usage: [Rt, inliers] = ransacfitRt(x1, x2, t)
%
% Arguments:
% x1 - 3xN set of 3D points.
% x2 - 3xN set of 3D points such that x1<->x2.
% t - The distance threshold between data point and the model
% used to decide whether a point is an inlier or not.
%
% Note that it ... |
github | jianxiongxiao/ProfXkit-master | show.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/show.m | 4,944 | utf_8 | e2225be3d05b416c72fc6f1acbde02d0 | % SHOW - Displays an image with the right size and colors and with a title.
%
% Usage:
% h = show(im)
% h = show(im, figNo)
% h = show(im, title)
% h = show(im, figNo, title)
%
% Arguments: im - Either a 2 or 3D array of pixel values or the name
% of an image f... |
github | jianxiongxiao/ProfXkit-master | nonmaxsuppts.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/nonmaxsuppts.m | 5,086 | utf_8 | 6d711b2f28fd3ea2543d59f3e89139b7 | % NONMAXSUPPTS - Non-maximal suppression for features/corners
%
% Non maxima suppression and thresholding for points generated by a feature
% or corner detector.
%
% Usage: [r,c] = nonmaxsuppts(cim, radius, thresh, im)
% /
% ... |
github | jianxiongxiao/ProfXkit-master | ransacfitfundmatrix.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/ransacfitfundmatrix.m | 5,544 | utf_8 | b87d72c56902f27c573b6c545f6754ab | % RANSACFITFUNDMATRIX - fits fundamental matrix using RANSAC
%
% Usage: [F, inliers] = ransacfitfundmatrix(x1, x2, t)
%
% Arguments:
% x1 - 2xN or 3xN set of homogeneous points. If the data is
% 2xN it is assumed the homogeneous scale factor is 1.
% x2 - 2xN or 3xN set of homogeneo... |
github | jianxiongxiao/ProfXkit-master | matrix2quaternion.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/matrix2quaternion.m | 2,010 | utf_8 | ad7a1983aceaa9953be167eddabb22ae | % MATRIX2QUATERNION - Homogeneous matrix to quaternion
%
% Converts 4x4 homogeneous rotation matrix to quaternion
%
% Usage: Q = matrix2quaternion(T)
%
% Argument: T - 4x4 Homogeneous transformation matrix
% Returns: Q - a quaternion in the form [w, xi, yj, zk]
%
% See Also QUATERNION2MATRIX
% Copyright (c) 2008 ... |
github | jianxiongxiao/ProfXkit-master | harris.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/harris.m | 4,707 | utf_8 | 9123c3c21835dfa4d233abe149d6620d | % HARRIS - Harris corner detector
%
% Usage: cim = harris(im, sigma)
% [cim, r, c] = harris(im, sigma, thresh, radius, disp)
% [cim, r, c, rsubp, csubp] = harris(im, sigma, thresh, radius, disp)
%
% Arguments:
% im - image to be processed.
% sigma - standard... |
github | jianxiongxiao/ProfXkit-master | hnormalise.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/hnormalise.m | 1,010 | utf_8 | 5c1ed3ba361fa6f28b1517af1924af40 | % HNORMALISE - Normalises array of homogeneous coordinates to a scale of 1
%
% Usage: nx = hnormalise(x)
%
% Argument:
% x - an Nxnpts array of homogeneous coordinates.
%
% Returns:
% nx - an Nxnpts array of homogeneous coordinates rescaled so
% that the scale values nx(N,:) are all 1.
%
... |
github | jianxiongxiao/ProfXkit-master | homography2d.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/homography2d.m | 2,493 | utf_8 | 60985e0ab95fe690d769c83adff61080 | % HOMOGRAPHY2D - computes 2D homography
%
% Usage: H = homography2d(x1, x2)
% H = homography2d(x)
%
% Arguments:
% x1 - 3xN set of homogeneous points
% x2 - 3xN set of homogeneous points such that x1<->x2
%
% x - If a single argument is supplied it is assumed ... |
github | jianxiongxiao/ProfXkit-master | iscolinear.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/iscolinear.m | 2,318 | utf_8 | 65025b7413f8f6b4cb16dd1689a5900f | % ISCOLINEAR - are 3 points colinear
%
% Usage: r = iscolinear(p1, p2, p3, flag)
%
% Arguments:
% p1, p2, p3 - Points in 2D or 3D.
% flag - An optional parameter set to 'h' or 'homog'
% indicating that p1, p2, p3 are homogneeous
% coordinates with arbitrary s... |
github | jianxiongxiao/ProfXkit-master | monofilt.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/monofilt.m | 6,435 | utf_8 | 07ef46eb32d19a4d79e9ec304c6cb2d3 | % MONOFILT - Apply monogenic filters to an image to obtain 2D analytic signal
%
% Implementation of Felsberg's monogenic filters
%
% Usage: [f, h1f, h2f, A, theta, psi] = ...
% monofilt(im, nscale, minWaveLength, mult, sigmaOnf, orientWrap)
% 3 4 2 0.65 ... |
github | jianxiongxiao/ProfXkit-master | ransacfithomography.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/ransacfithomography.m | 4,920 | utf_8 | d479d49f7c8e8689283005bcbe340b61 | % RANSACFITHOMOGRAPHY - fits 2D homography using RANSAC
%
% Usage: [H, inliers] = ransacfithomography(x1, x2, t)
%
% Arguments:
% x1 - 2xN or 3xN set of homogeneous points. If the data is
% 2xN it is assumed the homogeneous scale factor is 1.
% x2 - 2xN or 3xN set of homogeneous po... |
github | jianxiongxiao/ProfXkit-master | fundmatrix.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/fundmatrix.m | 3,961 | utf_8 | 250dfa8051640daab30229f35667f4d6 | % FUNDMATRIX - computes fundamental matrix from 8 or more points
%
% Function computes the fundamental matrix from 8 or more matching points in
% a stereo pair of images. The normalised 8 point algorithm given by
% Hartley and Zisserman p265 is used. To achieve accurate results it is
% recommended that 12 or more poi... |
github | jianxiongxiao/ProfXkit-master | hline.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/hline.m | 1,584 | utf_8 | 7887599478d2ebb7e50fdef565f8f3f5 | % HLINE - Plot 2D lines defined in homogeneous coordinates.
%
% Function for ploting 2D homogeneous lines defined by 2 points
% or a line defined by a single homogeneous vector
%
% Usage: hline(p1,p2) where p1 and p2 are 2D homogeneous points.
% hline(p1,p2,'colour_name') 'black' 'red' 'white' etc
% ... |
github | jianxiongxiao/ProfXkit-master | ransac.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/ransac.m | 9,582 | utf_8 | a864f4e3ec4b4d8e18b346cf8391b28b | % RANSAC - Robustly fits a model to data with the RANSAC algorithm
%
% Usage:
%
% [M, inliers] = ransac(x, fittingfn, distfn, degenfn s, t, feedback, ...
% maxDataTrials, maxTrials)
%
% Arguments:
% x - Data sets to which we are seeking to fit a model M
% It is assumed ... |
github | jianxiongxiao/ProfXkit-master | gaussfilt.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/gaussfilt.m | 892 | utf_8 | 266e718eee73f61a8bc07650565a1692 | % GAUSSFILT - Small wrapper function for convenient Gaussian filtering
%
% Usage: smim = gaussfilt(im, sigma)
%
% Arguments: im - Image to be smoothed.
% sigma - Standard deviation of Gaussian filter.
%
% Returns: smim - Smoothed image.
%
% See also: INTEGGAUSSFILT
% Peter Kovesi
% Centre for Explorti... |
github | jianxiongxiao/ProfXkit-master | derivative5.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/derivative5.m | 4,808 | utf_8 | 989b39a3f681a8cad7375573fa1a7a0f | % 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 | jianxiongxiao/ProfXkit-master | quaternion2matrix.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/quaternion2matrix.m | 1,413 | utf_8 | 7296cadf62f6ca9273e726ffd7e19d95 | % QUATERNION2MATRIX - Quaternion to a 4x4 homogeneous transformation matrix
%
% Usage: T = quaternion2matrix(Q)
%
% Argument: Q - a quaternion in the form [w xi yj zk]
% Returns: T - 4x4 Homogeneous rotation matrix
%
% See also MATRIX2QUATERNION, NEWQUATERNION, QUATERNIONROTATE
% Copyright (c) 2008 Peter Kovesi
... |
github | jianxiongxiao/ProfXkit-master | matchbymonogenicphase.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/matchbymonogenicphase.m | 9,328 | utf_8 | e63225faedcf391fb6411d27d71a208e | % MATCHBYMONOGENICPHASE - match image feature points using monogenic phase data
%
% Function generates putative matches between previously detected
% feature points in two images by looking for points that have minimal
% differences in monogenic phase data within windows surrounding each point.
% Only points that corre... |
github | jianxiongxiao/ProfXkit-master | normalise2dpts.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/normalise2dpts.m | 2,361 | utf_8 | 2b9d94a3681186006a3fd47a45faf939 | % NORMALISE2DPTS - normalises 2D homogeneous points
%
% Function translates and normalises a set of 2D homogeneous points
% so that their centroid is at the origin and their mean distance from
% the origin is sqrt(2). This process typically improves the
% conditioning of any equations used to solve homographies, fun... |
github | jianxiongxiao/ProfXkit-master | hcross.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/hcross.m | 919 | utf_8 | dbb3f3d4ef79e25ca3000ea976409e0c | % HCROSS - Homogeneous cross product, result normalised to s = 1.
%
% Function to form cross product between two points, or lines,
% in homogeneous coodinates. The result is normalised to lie
% in the scale = 1 plane.
%
% Usage: c = hcross(a,b)
%
% Copyright (c) 2000-2005 Peter Kovesi
% School of Computer Science & ... |
github | jianxiongxiao/ProfXkit-master | matchbycorrelation.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/peter/matchbycorrelation.m | 7,076 | utf_8 | 12d7e8d4ad6e140c94444ddc3682d518 | % MATCHBYCORRELATION - match image feature points by correlation
%
% Function generates putative matches between previously detected
% feature points in two images by looking for points that are maximally
% correlated with each other within windows surrounding each point.
% Only points that correlate most strongly with... |
github | jianxiongxiao/ProfXkit-master | vl_compile.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/vl_compile.m | 5,060 | utf_8 | 978f5189bb9b2a16db3368891f79aaa6 | function vl_compile(compiler)
% VL_COMPILE Compile VLFeat MEX files
% VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command
% works only under Windows and is used to re-build problematic
% binaries. The preferred method of compiling VLFeat on both UNIX
% and Windows is through the provided Makefile... |
github | jianxiongxiao/ProfXkit-master | vl_noprefix.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/vl_noprefix.m | 1,875 | utf_8 | 97d8755f0ba139ac1304bc423d3d86d3 | function vl_noprefix
% VL_NOPREFIX Create a prefix-less version of VLFeat commands
% VL_NOPREFIX() creats prefix-less stubs for VLFeat functions
% (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs
% are included in the VLFeat binary distribution anyways. Moreover,
% on UNIX platforms, the stub... |
github | jianxiongxiao/ProfXkit-master | vl_override.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/misc/vl_override.m | 4,654 | utf_8 | e233d2ecaeb68f56034a976060c594c5 | function config = vl_override(config,update,varargin)
% VL_OVERRIDE Override structure subset
% CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds
% of the structure UPDATE to the corresponding fields of the
% struture CONFIG.
%
% Usually CONFIG is interpreted as a list of paramters with their
... |
github | jianxiongxiao/ProfXkit-master | vl_quickvis.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/quickshift/vl_quickvis.m | 3,696 | utf_8 | 27f199dad4c5b9c192a5dd3abc59f9da | function [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts)
% VL_QUICKVIS Create an edge image from a Quickshift segmentation.
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. RATIO controls the tradeoff
% bet... |
github | jianxiongxiao/ProfXkit-master | vl_demo_aib.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/demo/vl_demo_aib.m | 2,928 | utf_8 | 590c6db09451ea608d87bfd094662cac | function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X... |
github | jianxiongxiao/ProfXkit-master | vl_demo_alldist.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/demo/vl_demo_alldist.m | 5,460 | utf_8 | 6d008a64d93445b9d7199b55d58db7eb | function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 's... |
github | jianxiongxiao/ProfXkit-master | vl_demo_kdtree_sift.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/demo/vl_demo_kdtree_sift.m | 6,822 | utf_8 | 191589ff45e0f5cdb79b1eed1b1bb906 | function vl_demo_kdtree_sift
% VL_DEMO_KDTREE_SIFT
% Demonstrates the use of a kd-tree forest to match SIFT
% features. If FLANN is present, this function runs a comparison
% against it.
% AUTORIGHS
rand('state',0) ;
randn('state',0);
do_median = 0 ;
do_mean = 1 ;
% try to setup flann
if ~exist('flann_search'... |
github | jianxiongxiao/ProfXkit-master | vl_tpsu.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/imop/vl_tpsu.m | 1,755 | utf_8 | 09f36e1a707c069b375eb2817d0e5f13 | function [U,dU,delta]=vl_tpsu(X,Y)
% VL_TPSU Compute the U matrix of a thin-plate spline transformation
% U=VL_TPSU(X,Y) returns the matrix
%
% [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ]
% [ ]
% [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ]
%
% where X... |
github | jianxiongxiao/ProfXkit-master | vl_xyz2lab.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/imop/vl_xyz2lab.m | 1,570 | utf_8 | 09f95a6f9ae19c22486ec1157357f0e3 | function J=vl_xyz2lab(I,il)
% VL_XYZ2LAB Convert XYZ color space to LAB
% J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format.
%
% VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55,
% D65, D75, D93. The default illuminatn is E.
%
% See also: VL_XYZ2LUV(), VL_HELP().
% Copyright ... |
github | jianxiongxiao/ProfXkit-master | vl_test_twister.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_twister.m | 1,162 | utf_8 | 1ae9040a416db503ad73600f081d096b | function results = vl_test_twister(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_illegal_args()
vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ;
function te... |
github | jianxiongxiao/ProfXkit-master | vl_test_kdtree.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_kdtree.m | 2,448 | utf_8 | 66f429ff8286089a34c193d7d3f9f016 | function results = vl_test_kdtree(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = single(randn(10, 1000)) ;
s.Q = single(randn(10, 10)) ;
function test_nearest(s)
for tmethod = {'median', 'mean'}
for type = {@single, @double}
conv = type{1} ;
tmethod = char(tmethod) ;... |
github | jianxiongxiao/ProfXkit-master | vl_test_imwbackward.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_imwbackward.m | 514 | utf_8 | 33baa0784c8f6f785a2951d7f1b49199 | function results = vl_test_imwbackward(varargin)
% VL_TEST_IMWBACKWARD
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
function test_identity(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,... |
github | jianxiongxiao/ProfXkit-master | vl_test_pegasos.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_pegasos.m | 2,852 | utf_8 | 45a09a3bfefa3facd439fefbb7f1a903 | function results = vl_test_pegasos(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 Xn] ;
s... |
github | jianxiongxiao/ProfXkit-master | vl_test_alphanum.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_alphanum.m | 1,624 | utf_8 | 2da2b768c2d0f86d699b8f31614aa424 | function results = vl_test_alphanum(varargin)
% VL_TEST_ALPHANUM
vl_test_init ;
function s = setup()
s.strings = ...
{'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Al... |
github | jianxiongxiao/ProfXkit-master | vl_test_imintegral.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_imintegral.m | 1,429 | utf_8 | 4750f04ab0ac9fc4f55df2c8583e5498 | function results = vl_test_imintegral(varargin)
% VL_TEST_IMINTEGRAL
vl_test_init ;
function state = setup()
state.I = ones(5,6) ;
state.correct = [ 1 2 3 4 5 6 ;
2 4 6 8 10 12 ;
3 6 9 12 15 18 ;
4 8 12 ... |
github | jianxiongxiao/ProfXkit-master | vl_test_sift.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_sift.m | 1,318 | utf_8 | 806c61f9db9f2ebb1d649c9bfcf3dc0a | function results = vl_test_sift(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
s.I = im2single(imread(fullfile(vl_root,'data','box.pgm'))) ;
[s.ubc.f, s.ubc.d] = ...
vl_ubcread(fullfile(vl_root,'data','box.sift')) ;
function test_ubc_descriptor(s)
err = [] ;
[f, d] = vl_sift(s.I,...
... |
github | jianxiongxiao/ProfXkit-master | vl_test_binsum.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_binsum.m | 1,301 | utf_8 | 5bbd389cbc4d997e413d809fe4efda6d | function results = vl_test_binsum(varargin)
% VL_TEST_BINSUM
vl_test_init ;
function test_three_args()
vl_assert_almost_equal(...
vl_binsum([0 0], 1, 2), [0 1]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, 1), [0 7]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ;
function te... |
github | jianxiongxiao/ProfXkit-master | vl_test_lbp.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_lbp.m | 1,056 | utf_8 | 3b5cca50109af84014e56a4280a3352a | function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_one_on()
I = {} ;
I{1} = [0 0 0 ; 0 0 1 ; 0 0 0] ;
I{2} = [0 0 0 ; 0 0 0 ; 0 0 1] ;
I{3} = [0 0 0 ; 0 0 0 ; 0 1 0] ;
I{4} = [0 0 0 ; 0 0 0 ; 1 0 0] ;
I{5} = [0 0 0 ; 1 0 0 ; 0 0 0] ;
I{6} = [1 0 0 ; 0 0 0 ; 0 0 0] ;
I{7} = [0 1 0 ;... |
github | jianxiongxiao/ProfXkit-master | vl_test_colsubset.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_colsubset.m | 828 | utf_8 | be0c080007445b36333b863326fb0f15 | function results = vl_test_colsubset(varargin)
% VL_TEST_COLSUBSET
vl_test_init ;
function s = setup()
s.x = [5 2 3 6 4 7 1 9 8 0] ;
function test_beginning(s)
vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ;
vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ;
function test_ending(s)
vl_assert_equa... |
github | jianxiongxiao/ProfXkit-master | vl_test_alldist.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_alldist.m | 2,373 | utf_8 | 9ea1a36c97fe715dfa2b8693876808ff | function results = vl_test_alldist(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
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_alldist(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equa... |
github | jianxiongxiao/ProfXkit-master | vl_test_grad.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_grad.m | 434 | utf_8 | 4d03eb33a6a4f68659f868da95930ffb | function results = vl_test_grad(varargin)
% VL_TEST_GRAD
vl_test_init ;
function s = setup()
s.I = rand(150,253) ;
s.I_small = rand(2,2) ;
function test_equiv(s)
vl_assert_equal(gradient(s.I), vl_grad(s.I)) ;
function test_equiv_small(s)
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
function test_equiv... |
github | jianxiongxiao/ProfXkit-master | vl_test_whistc.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_whistc.m | 1,384 | utf_8 | 81c446d35c82957659840ab2a579ec2c | function results = vl_test_whistc(varargin)
% VL_TEST_WHISTC
vl_test_init ;
function test_acc()
x = ones(1, 10) ;
e = 1 ;
o = 1:10 ;
vl_assert_equal(vl_whistc(x, o, e), 55) ;
function test_basic()
x = 1:10 ;
e = 1:10 ;
o = ones(1, 10) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
x = linspace(-1,11,100) ;
o =... |
github | jianxiongxiao/ProfXkit-master | vl_test_dsift.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_imsmooth.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_phow.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_kmeans.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_imarray.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_homkermap.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_slic.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/xtest/vl_test_slic.m | 229 | utf_8 | 42c827b383cca74cae2540e5da870bbf | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(imread(fullfile(vl_root,'data','a.jpg'))) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1, 'verbose') ;
|
github | jianxiongxiao/ProfXkit-master | vl_test_imdisttf.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_argparse.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_binsearch.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_plotframe.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/plotop/vl_plotframe.m | 5,410 | utf_8 | 8c48bac1c5d80dba361b67cd135103d9 | 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 the... |
github | jianxiongxiao/ProfXkit-master | vl_roc.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/plotop/vl_roc.m | 6,848 | utf_8 | 3d7ed746da2d3f389ad56c8e36f006d7 | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
% VL_ROC Compute ROC curve
% [TP,TN] = VL_ROC(LABELS, SCORES) computes the receiver operating
% characteristic (ROC curve). LABELS are the ground thruth labels (+1
% or -1) and SCORE is the scores assigned to them by a classifier
% (higher scores correspond... |
github | jianxiongxiao/ProfXkit-master | vl_click.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_ubcread.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_plotsiftdescriptor.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | vl_test_twister.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/test/vl_test_twister.m | 1,166 | utf_8 | 1e18a0b343ffe164ec9c941e18575c05 | function vl_test_twister
% VL_TEST_TWISTER
% test seed by scalar
rand('twister',1) ; a = rand ;
vl_twister('state',1) ; b = vl_twister ;
check(a,b,'twister: seed by scalar + VL_TWISTER()') ;
% read state
rand('twister') ; a = rand('twister') ;
vl_twister('state') ; b = vl_twister('state') ;
check(a,b,'twister: read s... |
github | jianxiongxiao/ProfXkit-master | vl_test_imintegral.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/test/vl_test_imintegral.m | 1,257 | utf_8 | d5ad8d073e99ff451cc1b692da99ec6d | function vl_test_imintegral
I = ones(5,6);
correct = [1 2 3 4 5 6;
2 4 6 8 10 12;
3 6 9 12 15 18;
4 8 12 16 20 24;
5 10 15 20 25 30;];
if ~all(all(slow_imintegral(I) == correct))
fpri... |
github | jianxiongxiao/ProfXkit-master | vl_test_sift.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/test/vl_test_sift.m | 1,849 | utf_8 | cfae71614a40aebf645eb42102ca53f3 | function vl_test_sift
% VL_TEST_SIFT Test VL_SIFT implementation(s)
I = vl_test_pattern(101);
% run various instances of the code
[a0,b0] = vl_sift(single(I),'verbose','peaktresh',0,'levels',4) ;
[a1,b1] = cmd_sift(I,'--first-octave=0 --peak-tresh=0 --levels=4') ;
[a2,b2] = cmd_sift(I,'--first-octave=0',1) ;
[a3,... |
github | jianxiongxiao/ProfXkit-master | vl_test_binsum.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/test/vl_test_binsum.m | 1,030 | utf_8 | c69da861d697e8228e243a385f5ba545 | function vl_test_binsum
% VL_TEST_BINSUM Test VL_BINSUM function
testh({[0 0], 1, 2}, [0 1] ) ;
testh({[1 7], -1, 1}, [0 7] ) ;
testh({[1 7], -1, [1 2 2 2 2 2 2 2]}, [0 0] ) ;
testh({eye(3), [1 1 1], [1 2 3], 1 }, 2*eye(3)) ;
testh({eye(3), [1 1 1]', [1 2 3]', 2 }, 2*eye... |
github | jianxiongxiao/ProfXkit-master | vl_test_imsmooth.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/test/vl_test_imsmooth.m | 1,566 | utf_8 | 27ae6791e4ca852539a031b78ae7a00b | function vl_test_imsmooth
I = im2double(imread('data/spots.jpg')) ;
I = max(min(imresize(I,2),1),0) ;
I = single(I) ;
global fign ;
fign = 1 ;
step = 1 ;
ker = 'gaussian' ;
testmany(I,'triangular',1) ;
testmany(I,'triangular',2) ;
testmany(I,'gaussian',1) ;
testmany(I,'gaussian',2) ;
function testmany(I,ker,step)... |
github | jianxiongxiao/ProfXkit-master | vl_test_hikmeans.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/test/vl_test_hikmeans.m | 2,037 | utf_8 | f57532e5de667fbe2f6cb9c714f20457 | function vl_test_hikmeans
% VL_TEST_HIKMEANS Test VL_HIKMEANS function
K = 2;
nleaves = 2;
data = uint8(rand(2,100)*255);
[tree,A] = vl_hikmeans(data,K,nleaves,'verbose','verbose');
%keyboard;
K = 3 ;
nleaves = 100 ;
data = uint8(rand(2,1000) * 255) ;
datat = uint8(rand(2,10000)* 255) ;
[... |
github | jianxiongxiao/ProfXkit-master | vl_test_homkmap.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/test/vl_test_homkmap.m | 1,493 | utf_8 | a78c933efd15a4279e2724ba4441ad76 | function vl_test_homkmap
x = 2.^(-12:.1:0) ;
L = .3 ;
n = 4 ;
V = vl_homkmap(x, n, L, 'kchi2') ;
V_ = featureMap('chi2', n, L, x, 1) ;
V
V_
figure(1) ; clf ;
subplot(1,2,1) ;
semilogx(x,V_','-') ; hold on ;
semilogy(x,V','--') ;
subplot(1,2,2);
plot(x,V_','-') ; hold on ;
plot(x,V','--') ;
function psi = feat... |
github | jianxiongxiao/ProfXkit-master | vl_test_aibhist.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/test/vl_test_aibhist.m | 2,263 | utf_8 | d46c6fa557ab0d00e465eaedd060add9 | % VL_TEST_AIBHIST
function vl_test_aibhist
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
C = 1:K*K ;
Pcx = zeros(3,K*K) ;
f1 = quantize(X1,D,K) ;
f2 = quantize(X2,D,K) ;... |
github | jianxiongxiao/ProfXkit-master | vl_test_ikmeans.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/toolbox/test/vl_test_ikmeans.m | 1,552 | utf_8 | 1d5747a991a0d81ed4f7a2c90cd2a213 | function vl_test_ikmeans
% VL_TEST_IKMEANS Test VL_IKMEANS function
fprintf('test_ikmeans: Testing VL_IKMEANS and IKMEANSPUSH\n')
% -----------------------------------------------------------------------
fprintf('test_ikmeans: Testing Lloyd algorithm\n')
K = 3 ;
data = uint8(rand(2,1000) * 255) ;
datat = ... |
github | jianxiongxiao/ProfXkit-master | phow_caltech101.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/apps/phow_caltech101.m | 11,269 | utf_8 | 91ef403a7a3865b32e7a5673350fec49 | 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 | jianxiongxiao/ProfXkit-master | sift_mosaic.m | .m | ProfXkit-master/SiftFu/SiftFu/SIFTransac/vlfeat/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 | jianxiongxiao/ProfXkit-master | off2im.m | .m | ProfXkit-master/RenderMe/RenderDepth/off2im.m | 2,233 | utf_8 | fc56dd8b10c07046b05234a11bc34c68 | function depth = off2im(offfile, ratio, xzRot, tilt, objz, objx)
if ~exist('xzRot', 'var')
xzRot = rand() * pi*2;
end
if ~exist('tilt', 'var')
tilt = - rand() * 0.1 * pi;
end
if ~exist('objz', 'var')
objz = 1.5 + rand() * 4; % 1.5 to 5.5
end
if ~exist('objx', 'var')
objx = (rand()*0.5-0.25) .* objz;
... |
github | jianxiongxiao/ProfXkit-master | quaternion.m | .m | ProfXkit-master/GCBreader/quaternion.m | 87,509 | utf_8 | fb98a96f24335e62ddb5b837a2fb1f97 | classdef quaternion
% classdef quaternion, implements quaternion mathematics and 3D rotations
%
% Properties (SetAccess = protected):
% e(4,1) components, basis [1; i; j; k]: e(1) + i*e(2) + j*e(3) + k*e(4)
% i*j=k, j*i=-k, j*k=i, k*j=-i, k*i=j, i*k=-j, i*i = j*j = k*k = -1
%
% Constructors:
% q ... |
github | jianxiongxiao/ProfXkit-master | fill_depth_cross_bfx.m | .m | ProfXkit-master/segmentGraph/CrossBilateralFiltering/fill_depth_cross_bfx.m | 1,675 | utf_8 | 8f5319ababaa10742c0b8552d969f7c2 | % In-paints the depth image using a cross-bilateral filter. The operation
% is implemented via several filterings at various scales. The number of
% scales is determined by the number of spacial and range sigmas provided.
% 3 spacial/range sigmas translated into filtering at 3 scales.
%
% Args:
% imgRgb - the RGB im... |
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