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github | runngezhang/retarget-toolkit-master | tveq_logbarrier.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/sparsecode/l1magic/Optimization/tveq_logbarrier.m | 2,729 | utf_8 | a5c35f5f917e99a4f47c53a7ec323009 | % tveq_logbarrier.m
%
% Solve equality constrained TV minimization
% min TV(x) s.t. Ax=b.
%
% Recast as the SOCP
% min sum(t) s.t. ||D_{ij}x||_2 <= t, i,j=1,...,n
% Ax=b
% and use a log barrier algorithm.
%
% Usage: xp = tveq_logbarrier(x0, A, At, b, lbtol, mu, slqtol, slqmaxiter)
%
% x0 - Nx1 vec... |
github | runngezhang/retarget-toolkit-master | spgdemo.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/sparsecode/spgl1-1.6/spgdemo.m | 16,195 | utf_8 | 629972a6bc0f55788ac56dda78d403a2 | function spgdemo(interactive)
%DEMO Demonstrates the use of the SPGL1 solver
%
% See also SPGL1.
% demo.m
% $Id: spgdemo.m 1079 2008-08-20 21:34:15Z ewout78 $
%
% ----------------------------------------------------------------------
% This file is part of SPGL1 (Spectral Projected Gradient for L1).
%
% ... |
github | runngezhang/retarget-toolkit-master | spg_mmv.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/sparsecode/spgl1-1.6/spg_mmv.m | 2,868 | utf_8 | f3a431d79f903de5c0e56f477785dc0b | function [x,r,g,info] = spg_mmv( A, B, sigma, options )
%SPG_MMV Solve multi-measurement basis pursuit denoise (BPDN)
%
% SPG_MMV is designed to solve the basis pursuit denoise problem
%
% (BPDN) minimize ||X||_1,2 subject to ||A X - B||_2,2 <= SIGMA,
%
% where A is an M-by-N matrix, B is an M-by-G matrix, a... |
github | runngezhang/retarget-toolkit-master | get_code.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/sparsecode/spgl1-1.6/get_code.m | 462 | utf_8 | 97020ec43495bd70d550d9519bedd025 | % distance should be <= size
% code is an array of shifting all possible positions of 2 signals
% which have distance of 'distance'
function [code] = get_code(size, distance)
freq = size / distance;
freq = floor(freq);
code = zeros(size, distance);
for i = 1:1:distance
code(i, i) = 1;
... |
github | runngezhang/retarget-toolkit-master | spgl1.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/sparsecode/spgl1-1.6/spgl1.m | 30,274 | utf_8 | 3b2e1f3e09b4351802afe1dbb8c9fd91 | function [x,r,g,info] = spgl1( A, b, tau, sigma, x, options )
%SPGL1 Solve basis pursuit, basis pursuit denoise, and LASSO
%
% [x, r, g, info] = spgl1(A, b, tau, sigma, x0, options)
%
% ---------------------------------------------------------------------
% Solve the basis pursuit denoise (BPDN) problem
%
% (BPDN) m... |
github | runngezhang/retarget-toolkit-master | oneProjectorMex.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/sparsecode/spgl1-1.6/private/oneProjectorMex.m | 3,747 | utf_8 | f6b6e7a8a040a1d86d7a7df0a386fec6 | function [x, itn] = oneProjectorMex(b,d,tau)
% [x, itn] = oneProjectorMex(b,d,tau)
% Return the orthogonal projection of the vector b >=0 onto the
% (weighted) L1 ball. In case vector d is specified, matrix D is
% defined as diag(d), otherwise the identity matrix is used.
%
% On exit,
% x solves minimize ||b-x... |
github | runngezhang/retarget-toolkit-master | lsqr.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/sparsecode/spgl1-1.6/private/lsqr.m | 11,849 | utf_8 | b60925c5944249161e00049c67d30868 | function [ x, istop, itn, r1norm, r2norm, anorm, acond, arnorm, xnorm, var ]...
= lsqr( m, n, A, b, damp, atol, btol, conlim, itnlim, show )
%
% [ x, istop, itn, r1norm, r2norm, anorm, acond, arnorm, xnorm, var ]...
% = lsqr( m, n, A, b, damp, atol, btol, conlim, itnlim, show );
%
% LSQR solves Ax = b or mi... |
github | runngezhang/retarget-toolkit-master | feature_sign.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/sparsecode/Solver/feature_sign.m | 3,030 | utf_8 | 7e22735a12749708aa0c8d1b6d2ed847 | % Feature sign search
% code by Wang Jinjun @ NEC Research Lab America
% reference
% Efficient sparse coding algorithms
% Honglak Lee Alexis Battle Rajat Raina Andrew Y. Ng
% Computer Science Department
% Stanford University
% Stanford, CA 94305
function [x]=feature_sign(B,y,lambda,init_x)... |
github | runngezhang/retarget-toolkit-master | SolveLasso.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/sparsecode/Solver/SolveLasso.m | 10,877 | utf_8 | 154185b8b87a985e0a059e49009b60dd | function [sols, numIters, activationHist, duals] = SolveLasso(A, y, N, algType, maxIters, lambdaStop, resStop, solFreq, verbose, OptTol)
% SolveLasso: Implements the Lars/Lasso algorithms
% Usage
% [sols, numIters, activationHist, duals] = SolveLasso(A, y, N, algType,
% maxIters, lambdaStop, resStop, solFreq, verbose, ... |
github | runngezhang/retarget-toolkit-master | l2ls_learn_basis_dual.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/fast_sparsecoding/code/l2ls_learn_basis_dual.m | 2,282 | utf_8 | d943b19c90e15814748d824984151253 | function B = l2ls_learn_basis_dual(X, S, l2norm, Binit)
% Learning basis using Lagrange dual (with basis normalization)
%
% This code solves the following problem:
%
% minimize_B 0.5*||X - B*S||^2
% subject to ||B(:,j)||_2 <= l2norm, forall j=1...size(S,1)
%
% The detail of the algorithm is described in the... |
github | runngezhang/retarget-toolkit-master | l1ls_featuresign.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/fast_sparsecoding/code/l1ls_featuresign.m | 7,079 | utf_8 | ed309362051a25e0af5d34273d25f81e | function Xout = l1ls_featuresign (A, Y, gamma, Xinit)
% The feature-sign search algorithm
% L1-regularized least squares problem solver
%
% This code solves the following problem:
%
% minimize_s 0.5*||y - A*x||^2 + gamma*||x||_1
%
% The detail of the algorithm is described in the following paper:
% 'Efficient Spar... |
github | runngezhang/retarget-toolkit-master | sparse_coding.m | .m | retarget-toolkit-master/Matlab/SparseCodingLib/fast_sparsecoding/code/sparse_coding.m | 7,264 | utf_8 | d9c7b55aaa1e94518df8f6a0cf6e52fa | function [B S stat] = sparse_coding(X_total, num_bases, beta, sparsity_func, epsilon, num_iters, batch_size, fname_save, pars, Binit, resample_size)
% Fast sparse coding algorithms
%
% minimize_B,S 0.5*||X - B*S||^2 + beta*sum(abs(S(:)))
% subject to ||B(:,j)||_2 <= l2norm, forall j=1...size(S,1)
%
% The det... |
github | runngezhang/retarget-toolkit-master | calculate_saliency_sc.m | .m | retarget-toolkit-master/Matlab/Saliency/ViaSparseCoding/CoeffSuppress/calculate_saliency_sc.m | 853 | utf_8 | 53ceed31071c47226394ca56fd530ccf | % calcualte the saliency base on the sparse coding result
% param: extra param if necessary
% method:
% - mse: result.mse;
% - avg_coeff_abs_norm1:
% * param is the avg_coeff,
% * calculated by: coeff - avg_coeff, then taking norm 1 of only positive part
% - avg_coeff_mse:
% * param is the avg_coeff,
... |
github | runngezhang/retarget-toolkit-master | calculate_saliency_sc.m | .m | retarget-toolkit-master/Matlab/Saliency/ViaSparseCoding/CoeffSuppress/Version1/calculate_saliency_sc.m | 853 | utf_8 | 53ceed31071c47226394ca56fd530ccf | % calcualte the saliency base on the sparse coding result
% param: extra param if necessary
% method:
% - mse: result.mse;
% - avg_coeff_abs_norm1:
% * param is the avg_coeff,
% * calculated by: coeff - avg_coeff, then taking norm 1 of only positive part
% - avg_coeff_mse:
% * param is the avg_coeff,
... |
github | runngezhang/retarget-toolkit-master | DisplaySparseCodingResult.m | .m | retarget-toolkit-master/Matlab/Saliency/ViaSparseCoding/SaliencySCAverage/DisplaySparseCodingResult.m | 745 | utf_8 | b25b3bf43e6a924eba718393d1c71a72 | % result is the result of sparse coding, generaly should have following
% structure:
% result.x : coeff (result of Ax = y)
% result.mse: mean square error
% result.method: name of method used
% result.param: parameter of the method
% ------
% figure_num is the figure number use in method figure(figure_num) - for... |
github | runngezhang/retarget-toolkit-master | NonzeroIndex.m | .m | retarget-toolkit-master/Matlab/Saliency/ViaSparseCoding/SaliencySCAverage/NonzeroIndex.m | 294 | utf_8 | 4fc06e2c25af7b7d6b97fa955c40303a | % find all index of x which is larger than threshold
function index = NonzeroIndex(x, threshold)
d = size(x);
curr = 1;
index = zeros(1,1);
for i = 1:1:d(1)
if(x(i) > threshold)
index(curr) = i;
curr = curr + 1;
end
end
end |
github | runngezhang/retarget-toolkit-master | ksvd.m | .m | retarget-toolkit-master/Matlab/Saliency/k-svd/ksvdbox13/ksvd.m | 19,222 | utf_8 | 158cc27636f21ddb552757d263827b30 | function [D,Gamma,err,gerr] = ksvd(params,varargin)
%KSVD K-SVD dictionary training.
% [D,GAMMA] = KSVD(PARAMS) runs the K-SVD dictionary training algorithm on
% the specified set of signals, returning the trained dictionary D and the
% signal representation matrix GAMMA.
%
% KSVD has two modes of operation: ... |
github | runngezhang/retarget-toolkit-master | ompdemo.m | .m | retarget-toolkit-master/Matlab/Saliency/k-svd/ompbox10/ompdemo.m | 2,386 | utf_8 | a17de4fcd341818074c30b28a4574b12 | function ompdemo
%OMPDEMO Demonstration of the OMP toolbox.
% OMPDEMO generates a random sparse mixture of cosines and spikes, adds
% noise, and applies OMP to recover the original signal.
%
% To run the demo, type OMPDEMO from the Matlab prompt.
%
% See also OMPSPEEDTEST.
% Ron Rubinstein
% Computer... |
github | KezhiLi/Tracking_Hypo-master | Gui_test1.m | .m | Tracking_Hypo-master/Gui_test1.m | 20,434 | utf_8 | 21a9c95554d2bcb886b6946b63bc72b3 | function varargout = Gui_test1(varargin)
% GUI_TEST1 MATLAB code for Gui_test1.fig
% GUI_TEST1, by itself, creates a new GUI_TEST1 or raises the existing
% singleton*.
%
% H = GUI_TEST1 returns the handle to a new GUI_TEST1 or the handle to
% the existing singleton*.
%
% GUI_TEST1('CALLBACK',hO... |
github | KezhiLi/Tracking_Hypo-master | Gui_test1.m | .m | Tracking_Hypo-master/GUI/Gui_test1.m | 20,611 | utf_8 | c6fd0b7a55777064ee4cf327ba490605 | function varargout = Gui_test1(varargin)
% GUI_TEST1 MATLAB code for Gui_test1.fig
% GUI_TEST1, by itself, creates a new GUI_TEST1 or raises the existing
% singleton*.
%
% H = GUI_TEST1 returns the handle to a new GUI_TEST1 or the handle to
% the existing singleton*.
%
% GUI_TEST1('CALLBACK',hO... |
github | KezhiLi/Tracking_Hypo-master | tesst1.m | .m | Tracking_Hypo-master/GUI/tesst1.m | 8,225 | utf_8 | 5bd16226f36a350d0ad929b316ca30a5 | function varargout = tesst1(varargin)
% TESST1 MATLAB code for tesst1.fig
% TESST1, by itself, creates a new TESST1 or raises the existing
% singleton*.
%
% H = TESST1 returns the handle to a new TESST1 or the handle to
% the existing singleton*.
%
% TESST1('CALLBACK',hObject,eventData,handles,... |
github | KezhiLi/Tracking_Hypo-master | curvspace.m | .m | Tracking_Hypo-master/Forecasting/curvspace.m | 3,226 | utf_8 | fc356dabfe5e37dfb7cdbfdf136de924 | function q = curvspace(p,N)
% CURVSPACE Evenly spaced points along an existing curve in 2D or 3D.
% CURVSPACE(P,N) generates N points that interpolates a curve
% (represented by a set of points) with an equal spacing. Each
% row of P defines a point, which means that P should be a n x 2
% (2D) or a n x 3 (3D) ... |
github | KezhiLi/Tracking_Hypo-master | LineCurvature2D.m | .m | Tracking_Hypo-master/Generate_Frenet_1/Seg_worm_functions/LineCurvature2D.m | 4,069 | utf_8 | 5d68750190861c373b2e3ee9972996e6 | function k=LineCurvature2D(Vertices,Lines)
% This function calculates the curvature of a 2D line. It first fits
% polygons to the points. Then calculates the analytical curvature from
% the polygons;
%
% k = LineCurvature2D(Vertices,Lines)
%
% inputs,
% Vertices : A M x 2 list of line points.
% (optional)
% Li... |
github | KezhiLi/Tracking_Hypo-master | frenet_TN.m | .m | Tracking_Hypo-master/Other/frenet_TN.m | 2,405 | utf_8 | 6623bf790c24f5ba81a726129a3cb1ed | function [TT,NN] = frenet_TN(x,y,z)
% FRENET_TN - Frenet-Serret Space Curve Invarients
% A modified simpler version of Frenet, to accelerate the process
%
% [T,N] = frenet_TN(x,y);
% [T,N] = frenet_TN(x,y,z);
%
% Returns the 3 vector and 2 scaler invarients of a space curve defined
% by vectors x,y and z. ... |
github | KezhiLi/Tracking_Hypo-master | frenet.m | .m | Tracking_Hypo-master/Other/frenet.m | 1,769 | utf_8 | 9f780664a4e31575257fafd0e08f8317 | function [T,N,B,k,t,TT,NN,BB] = frenet(x,y,z),
% FRENET - Frenet-Serret Space Curve Invarients
%
% [T,N,B,k,t] = frenet(x,y);
% [T,N,B,k,t] = frenet(x,y,z);
%
% Returns the 3 vector and 2 scaler invarients of a space curve defined
% by vectors x,y and z. If z is omitted then the curve is only a 2D,
% but... |
github | KezhiLi/Tracking_Hypo-master | read_new.m | .m | Tracking_Hypo-master/Other/read_new.m | 3,634 | utf_8 | eee1dfe218ad5a63b4f83b33c2975e5a | function varargout = read_new(obj, varargin)
%READ Read a video file.
%
% VIDEO = READ(OBJ) reads in video frames from the associated file. VIDEO
% is an H x W x B x F matrix where H is the image frame height, W is the
% image frame width, B is the number of bands in the image (e.g. 3 for RGB),
% and F is the... |
github | KezhiLi/Tracking_Hypo-master | uTest_DGradient.m | .m | Tracking_Hypo-master/Other/DGradient/uTest_DGradient.m | 13,843 | utf_8 | b591c8ab816cbf8130f9adfcd3c1df8a | function uTest_DGradient(doSpeed)
% Unit test: DGradient
% This is a routine for automatic testing. It is not needed for processing and
% can be deleted or moved to a folder, where it does not bother.
%
% uTest_DGradient(doSpeed)
% INPUT:
% doSpeed: If ANY(doSpeed) is TRUE, the speed is measured.
% OUTPUT:
% On fai... |
github | sitepoint-editors/matlab-mean-demo-master | savejson.m | .m | matlab-mean-demo-master/jsonlab/savejson.m | 14,350 | utf_8 | 9c4adfaa1058e68cbdad0f92b347d35a | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | sitepoint-editors/matlab-mean-demo-master | loadjson.m | .m | matlab-mean-demo-master/jsonlab/loadjson.m | 16,269 | ibm852 | 137087090c6ffe8c5d3afd55deed4a99 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2011/09/09
% Nedialko Krouchev: http:... |
github | sitepoint-editors/matlab-mean-demo-master | loadubjson.m | .m | matlab-mean-demo-master/jsonlab/loadubjson.m | 13,745 | utf_8 | a9907e776763f4e2e6ddef70aab18b1e | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2013/08/01
%
% $Id: loadubjson.m 410 20... |
github | sitepoint-editors/matlab-mean-demo-master | saveubjson.m | .m | matlab-mean-demo-master/jsonlab/saveubjson.m | 15,038 | utf_8 | 47e779c33fb915f4379d2719f05d43f6 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | petercorke/machinevision-toolbox-matlab-master | frefine.m | .m | machinevision-toolbox-matlab-master/frefine.m | 1,797 | utf_8 | dd5d3b7b775b0b65a01ff3abad086035 | %FREFINE refine estimate of fundamental matrix
%
% fr = frefine(F, uv1, uv2)
%
% Return a refined estimate of fundamental matrix using non-linear
% optimization and enforcing the rank-2 constraint.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
% ... |
github | petercorke/machinevision-toolbox-matlab-master | kdog.m | .m | machinevision-toolbox-matlab-master/kdog.m | 1,914 | utf_8 | 2e1e1fe9b32bd5d5f98d3d5779bd443c | %KDOG Difference of Gaussian kernel
%
% K = KDOG(SIGMA1) is a 2-dimensional difference of Gaussian kernel equal
% to KGAUSS(SIGMA1) - KGAUSS(SIGMA2), where SIGMA1 > SIGMA2. By default
% SIGMA2 = 1.6*SIGMA1. The kernel is centred within the matrix K whose
% half-width H = 3xSIGMA and W=2xH+1.
%
% K = KDOG(SIGMA1, S... |
github | petercorke/machinevision-toolbox-matlab-master | yuvread.m | .m | machinevision-toolbox-matlab-master/yuvread.m | 1,715 | utf_8 | 272c06c1a8f885ce520c963094e26242 | %YUVREAD Read frame from a YUV4MPEG file
%
% [y,u,v] = yuvread(yuv, skip)
% [y,u,v, h] = yuvread(yuv, skip)
%
% Returns the Y, U and V components from the specified frame of
% YUV file. Optionally returns the frame header h.
%
% SEE ALSO: yuvopen yuv2rgb yuv2rgb2
% Copyright (C) 1993-2011, by Peter I. Corke
%
% Thi... |
github | petercorke/machinevision-toolbox-matlab-master | e2h.m | .m | machinevision-toolbox-matlab-master/e2h.m | 1,179 | utf_8 | bd492550b73f5e90ec50b14b77a5bb16 | %E2H Euclidean to homogeneous
%
% H = E2H(E) is the homogeneous representation of a set of points E.
%
% In the Toolbox points are represented as by Euclidean coordinates which are
% the columns of a matrix E, and the number of rows is either 2 or 3 to
% represent 2- or 3-dimensional points. Homogeous representation... |
github | petercorke/machinevision-toolbox-matlab-master | radgrad.m | .m | machinevision-toolbox-matlab-master/radgrad.m | 1,727 | utf_8 | 4787b7c832ea1a81d26898b484b1874b | %RADGRAD Radial gradient
%
% [GR,GT] = RADGRAD(IM) is the radial and tangential gradient of the image IM.
% At each pixel the image gradient vector is resolved into the radial and
% tangential directions.
%
% [GR,GT] = RADGRAD(IM, CENTRE) as above but the centre of the image is
% specified as CENTRE=[X,Y] rather than ... |
github | petercorke/machinevision-toolbox-matlab-master | imono.m | .m | machinevision-toolbox-matlab-master/imono.m | 2,558 | utf_8 | e1d3c6f1346f728d957e48e65d4489a7 | %IMONO Convert color image to monochrome
%
% OUT = IMONO(IM, OPTIONS) is a greyscale equivalent to the color image IM.
%
% Options::
% 'r601' ITU recommendation 601 (default)
% 'r709' ITU recommendation 709
% 'value' HSV value component
%
% Notes::
% - This function returns a greyscale image whether pa... |
github | petercorke/machinevision-toolbox-matlab-master | imorph.m | .m | machinevision-toolbox-matlab-master/imorph.m | 3,107 | utf_8 | 8a9062fdf45458bf353b7068d6a2c185 | %IMORPH Morphological neighbourhood processing
%
% OUT = IMORPH(IM, SE, OP) is the image IM after morphological processing
% with the operator OP and structuring element SE.
%
% The structuring element SE is a small matrix with binary values that indicate
% which elements of the template window are used in the operati... |
github | petercorke/machinevision-toolbox-matlab-master | iconvolve.m | .m | machinevision-toolbox-matlab-master/iconvolve.m | 2,600 | utf_8 | 52cf0c2c894268cf4ac1b04e239ee7cd | %ICONVOLVE Image convolution
%
% C = ICONVOLVE(IM, K, OPTIONS) is the convolution of image IM with the kernel K.
%
% ICONVOLVE(IM, K, OPTIONS) as above but display the result.
%
% Options::
% 'same' output image is same size as input image (default)
% 'full' output image is larger than the input image
% 'vali... |
github | petercorke/machinevision-toolbox-matlab-master | epidist.m | .m | machinevision-toolbox-matlab-master/epidist.m | 753 | utf_8 | 2f5eb4981cd610c924a22a56eee41010 | %EPIDIST Distance of point from epipolar line
%
% D = EPIDIST(F, P1, P2) is the distance of the points P2 (2xM) from the
% epipolar lines due to points P1 (2xN) where F (3x3) is a fundamental matrix
% relating the views containing image points P1 and P2.
%
% D (NxM) is the distance matrix where element D(i,j) is the d... |
github | petercorke/machinevision-toolbox-matlab-master | medfilt1.m | .m | machinevision-toolbox-matlab-master/medfilt1.m | 1,342 | utf_8 | 5d5c899f7fe79b7805353449208c2ee0 | %MEDFILT1 Median filter
%
% Y = MEDFILT1(X, W) is the one-dimensional median filter of the signal X
% computed over a sliding window of width W.
%
% Notes::
% - A median filter performs smoothing but preserves sharp edges, unlike
% traditional smoothing filters.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% Thi... |
github | petercorke/machinevision-toolbox-matlab-master | trainseg.m | .m | machinevision-toolbox-matlab-master/trainseg.m | 1,981 | utf_8 | 4ed3ab1692fb2a6d89ec2a96a6f004e6 | %TRAINSEG Interactively train a color segmentation
%
% map = trainseg(im)
%
% Two windows are displayed, one the bivariant histogram in
% normalized (r,g) coordinates, the other the original image.
%
% For each pixel selected and clicked in the original image a point
% is marked in the bivariant histogram. By selectin... |
github | petercorke/machinevision-toolbox-matlab-master | yuv2rgb.m | .m | machinevision-toolbox-matlab-master/yuv2rgb.m | 1,422 | utf_8 | d2c6e0510aa00dc427e9f8328db2d928 | %YUV2RGB Convert YUV format to RGB
%
% [r,g,b] = yuvread(y, u, v)
% rgb = yuvread(y, u, v)
%
% Returns the equivalent RGB image from YUV components. The Y image is
% halved in resolution.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is... |
github | petercorke/machinevision-toolbox-matlab-master | ssd.m | .m | machinevision-toolbox-matlab-master/ssd.m | 1,177 | utf_8 | 1ec4b1f9d11e62dc8156946f596df4b7 | %SSD Sum of squared differences
%
% M = SSD(I1, I2) is the sum of squared differences between the
% two equally sized image patches I1 and I2. The result M is a scalar that
% indicates image similarity, a value of 0 indicates identical pixel patterns
% and is increasingly positive as image dissimilarity increases.
%
... |
github | petercorke/machinevision-toolbox-matlab-master | rectify.m | .m | machinevision-toolbox-matlab-master/rectify.m | 2,542 | utf_8 | 2734df5ebd09fafcaa4ddb23e34b9cb8 |
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Li... |
github | petercorke/machinevision-toolbox-matlab-master | stdisp.m | .m | machinevision-toolbox-matlab-master/stdisp.m | 3,612 | utf_8 | 6e791960ec7324767edea6e05e0d4cbf | %STDISP Display stereo pair
%
% STDISP(L, R) displays the stereo image pair L and R in adjacent windows.
%
% Two cross-hairs are created. Clicking a point in the left image positions
% black cross hair at the same pixel coordinate in the right image. Clicking
% the corresponding world point in the right image sets th... |
github | petercorke/machinevision-toolbox-matlab-master | Movie.m | .m | machinevision-toolbox-matlab-master/Movie.m | 8,706 | utf_8 | 5d4e71bb7d77fa8dd4795acb9366a757 | %MOVIE Class to read movie file
%
% A concrete subclass of ImageSource that acquires images from a web camera
% built by Axis Communications (www.axis.com).
%
% Methods::
% grab Aquire and return the next image
% size Size of image
% close Close the image source
% char Convert the object parameters to human ... |
github | petercorke/machinevision-toolbox-matlab-master | npq.m | .m | machinevision-toolbox-matlab-master/npq.m | 1,183 | utf_8 | d3d90b0334de6c3865a9c75f6e32ad80 | %NPQ Normalized central image moments
%
% M = NPQ(IM, P, Q) is the PQ'th normalized central moment of the image IM.
% That is UPQ(IM,P,Q)/MPQ(IM,0,0).
%
% Notes::
% - The normalized central moments are invariant to translation and scale.
%
% See also NPQ_POLY, MPQ, UPQ.
% Copyright (C) 1993-2011, by Peter I. Corke
%
... |
github | petercorke/machinevision-toolbox-matlab-master | kdgauss.m | .m | machinevision-toolbox-matlab-master/kdgauss.m | 1,726 | utf_8 | d9b690b8814b2d0cddc73c569b7ad278 | %KDGAUSS Derivative of Gaussian kernel
%
% K = KDGAUSS(SIGMA) is a 2-dimensional derivative of Gaussian kernel (WxW)
% of width (standard deviation) SIGMA and centred within the matrix K whose
% half-width H = 3xSIGMA and W=2xH+1.
%
% K = KDGAUSS(SIGMA, H) as above but the half-width is explictly specified.
%
% Notes:... |
github | petercorke/machinevision-toolbox-matlab-master | igamm.m | .m | machinevision-toolbox-matlab-master/igamm.m | 2,921 | utf_8 | df98f8ea630dcd264c1bc2d61cbbc1c1 | %IGAMM Gamma correction
%
% OUT = IGAMM(IM, GAMMA) is a gamma corrected version of the image IM. All
% pixels are raised to the power GAMMA. Gamma encoding can be performed with
% GAMMA > 1 and decoding with GAMMA < 1.
%
% OUT = IGAMM(IM, 'sRGB') is a gamma decoded version of IM using the sRGB
% decoding function ... |
github | petercorke/machinevision-toolbox-matlab-master | camcald4.m | .m | machinevision-toolbox-matlab-master/camcald4.m | 1,930 | utf_8 | 122d1846af6a05f71ab2e04216e66ca4 | % CAMCALD4 Compute partial camera calibration from four coplanar points
%
% C = CAMCALD4(D)
%
% Solve the camera calibration using a least squares technique.
% Input is a table of data points, D, with each row of the form [X Y u v]
% where (X, Y) are the world coordinate of the planar points with respect
% to some ... |
github | petercorke/machinevision-toolbox-matlab-master | mkgrid.m | .m | machinevision-toolbox-matlab-master/mkgrid.m | 2,210 | utf_8 | cd67fcdd38348816237692eadfc0d01c | %MKGRID Create grid of points
%
% P = MKGRID(D, S, OPTIONS) is a set of points (3 x D^2) that define a DxD planar
% grid of points with side length S. The points are the columns of P.
% If D is a 2-vector the grid is D(1)xD(2) points. If S is a 2-vector the
% side lengths are S(1)xS(2).
%
% By default the grid lies ... |
github | petercorke/machinevision-toolbox-matlab-master | iscolor.m | .m | machinevision-toolbox-matlab-master/iscolor.m | 1,092 | utf_8 | 3aca102e4947fba328d4df19e7ae4037 | %ISCOLOR Test for color image
%
% ISCOLOR(IM) is true (1) if IM is a color image, that is, it its third
% dimension is equal to three.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
%... |
github | petercorke/machinevision-toolbox-matlab-master | pclviewer.m | .m | machinevision-toolbox-matlab-master/pclviewer.m | 1,291 | utf_8 | f58f1e96b93686f38351e26b2c2db93f | %PCLVIEWER View a point cloud using PCL
%
% PCLVIEWER(P) writes the point cloud P (MxN) to a temporary file and invokes
% the PCL point cloud viewer for fast display and visualization. The columns of P
% represent the 3D points.
%
% If M=3 then the rows are x, y, z.
% If M=6 then the rows are x, y, z, R, G, B where R,... |
github | petercorke/machinevision-toolbox-matlab-master | yuv2rgb2.m | .m | machinevision-toolbox-matlab-master/yuv2rgb2.m | 1,736 | utf_8 | fc221f13ae85d3fc2e85f3e6d88331b6 | %YUV2RGB Convert YUV format to RGB
%
% [r,g,b] = yuvread2(y, u, v)
% rgb = yuvread(y, u, v)
%
% Returns the equivalent RGB image from YUV components. The UV images are
% doubled in resolution so the resulting color image is original size.
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The M... |
github | petercorke/machinevision-toolbox-matlab-master | iopen.m | .m | machinevision-toolbox-matlab-master/iopen.m | 1,670 | utf_8 | 1b1c17c01b26e09195b5cb14d242efdb | %IOPEN Morphological opening
%
% OUT = IOPEN(IM, SE, OPTIONS) is the image IM after morphological opening
% with the structuring element SE. This is a morphological erosion followed
% by dilation.
%
% OUT = IOPEN(IM, SE, N, OPTIONS) as above but the structuring element
% SE is applied N times, that is N erosions fo... |
github | petercorke/machinevision-toolbox-matlab-master | loadpcd.m | .m | machinevision-toolbox-matlab-master/loadpcd.m | 8,446 | utf_8 | 9ca2d26237c905d4aa1ccc9abd7bcbf4 | %LOADPCD Load a point cloud from a PCD format file
%
% P = LOADPCD(FNAME) is a set of points loaded from the PCD format
% file FNAME.
%
% For an unorganized point cloud the columns of P represent the 3D points,
% and the rows are: x, y, z, r, g, b, a depending on the FIELDS in the file.
%
% For an organized point clo... |
github | petercorke/machinevision-toolbox-matlab-master | idisp.m | .m | machinevision-toolbox-matlab-master/idisp.m | 20,442 | utf_8 | 80127a9f619e6db0adeac51319d894ff | %IDISP Interactive image display tool
%
% IDISP(IM, OPTIONS) displays an image and allows interactive investigation
% of pixel values, linear profiles, histograms and zooming. The image is
% displayed in a figure with a toolbar across the top. If IM is a cell
% array of images, they are first concatenated (horizontal... |
github | petercorke/machinevision-toolbox-matlab-master | ktriangle.m | .m | machinevision-toolbox-matlab-master/ktriangle.m | 1,771 | utf_8 | e831c5fcfd63ec2cba7e5b45bbb40175 | %KTRIANGLE Triangular kernel
%
% K = KTRIANGLE(W) is a triangular kernel within a rectangular matrix K. The
% dimensions K are WxW if W is scalar or W(1) wide and W(2) high. The triangle
% is isocles and is full width at the bottom row of the kernel and with its
% apex in the top row.
%
% Examples::
% >> ktri... |
github | petercorke/machinevision-toolbox-matlab-master | kgauss.m | .m | machinevision-toolbox-matlab-master/kgauss.m | 1,447 | utf_8 | c23f9de3d8de8fa424dbaae5d60696c3 | %KGAUSS Gaussian kernel
%
% K = KGAUSS(SIGMA) is a 2-dimensional Gaussian kernel of standard deviation
% SIGMA, and centred within the matrix K whose half-width is H=2xSIGMA and
% W=2xH+1.
%
% K = KGAUSS(SIGMA, H) as above but the half-width H is specified.
%
% Notes::
% - The volume under the Gaussian kernel is one.
... |
github | petercorke/machinevision-toolbox-matlab-master | rectify2.m | .m | machinevision-toolbox-matlab-master/rectify2.m | 1,521 | utf_8 | fcc1a0c095cebcf1aea713c177149139 |
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Li... |
github | petercorke/machinevision-toolbox-matlab-master | ibbox.m | .m | machinevision-toolbox-matlab-master/ibbox.m | 1,394 | utf_8 | 2b1423e59663200cef53951fbf8311d9 | %IBBOX Find bounding box
%
% BOX = IBBOX(P) is the minimal bounding box that contains the points
% described by the columns of P (2xN).
%
% BOX = IBBOX(IM) as above but the box minimally contains the non-zero
% pixels in the image IM.
%
% Notes::
% - The bounding box is a 2x2 matrix [XMIN XMAX; YMIN YMAX].
% Copyrigh... |
github | petercorke/machinevision-toolbox-matlab-master | pilu.m | .m | machinevision-toolbox-matlab-master/pilu.m | 1,475 | utf_8 | 45f50bbc510e6e782f57ed7c0bd20644 | %JUNK
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of ... |
github | petercorke/machinevision-toolbox-matlab-master | mkcube.m | .m | machinevision-toolbox-matlab-master/mkcube.m | 3,324 | utf_8 | 2ec9d1f6c809a6b50860858218ffe6ac | %MKCUBE Create cube
%
% P = MKCUBE(S, OPTIONS) is a set of points (3x8) that define the
% vertices of a cube of side length S and centred at the origin.
%
% [X,Y,Z] = MKCUBE(S, OPTIONS) as above but return the rows of P as three
% vectors.
%
% [X,Y,Z] = MKCUBE(S, 'edge', OPTIONS) is a mesh that defines the edges of
%... |
github | petercorke/machinevision-toolbox-matlab-master | savepgm.m | .m | machinevision-toolbox-matlab-master/savepgm.m | 1,170 | utf_8 | ec276b86d0f15e718939b6b0bcbe4288 | %SAVEPGM Write a PGM format file
%
% SAVEPGM(filename, im)
%
% Saves the specified image array in a binary (P5) format PGM image file.
%
% SEE ALSO: loadpgm
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redis... |
github | petercorke/machinevision-toolbox-matlab-master | startup_mvtb.m | .m | machinevision-toolbox-matlab-master/startup_mvtb.m | 2,769 | utf_8 | cd575de1cfd2ce3ff4217d6c8d534b58 | %STARTUP_MVTB Initialize MATLAB paths for Machine Vision Toolbox
%
% Adds demos, data, contributed code and examples to the MATLAB path.
%
% Notes::
% - This sets the paths for the current session only.
% - To make the settings persistent across sessions you can:
% - Add this script to your MATLAB startup.m script.
%... |
github | petercorke/machinevision-toolbox-matlab-master | rg_addticks.m | .m | machinevision-toolbox-matlab-master/rg_addticks.m | 1,308 | utf_8 | 1f9be9bbed355352dcba69d2ae5dfc5f | %RG_ADDTICKS Label spectral locus
%
% RG_ADDTICKS() adds wavelength ticks to the spectral locus.
%
% See also XYCOLOURSPACE.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under ... |
github | petercorke/machinevision-toolbox-matlab-master | iscale.m | .m | machinevision-toolbox-matlab-master/iscale.m | 2,235 | utf_8 | 0dd23d79337b45e5172e201fd7ba4826 | %ISCALE Scale an image
%
% OUT = ISCALE(IM, S) is a version of IM scaled in both directions by S
% which is a real scalar. S>1 makes the image larger, S<1 makes it smaller.
%
% Options::
% 'outsize',S set size of OUT to HxW where S=[W,H]
% 'smooth',S initially smooth image with Gaussian of standard deviation
... |
github | petercorke/machinevision-toolbox-matlab-master | zssd.m | .m | machinevision-toolbox-matlab-master/zssd.m | 1,338 | utf_8 | 262493b5eb6950c6fe1abce34f2e8bd5 | %ZSSD Sum of squared differences
%
% M = ZSSD(I1, I2) is the zero-mean sum of squared differences between the
% two equally sized image patches I1 and I2. The result M is a scalar that
% indicates image similarity, a value of 0 indicates identical pixel patterns
% and is increasingly positive as image dissimilarity i... |
github | petercorke/machinevision-toolbox-matlab-master | ncc.m | .m | machinevision-toolbox-matlab-master/ncc.m | 1,353 | utf_8 | ab9f98632c72cf2ddfc29bb208abe709 | %NCC Normalized cross correlation
%
% M = NCC(I1, I2) is the normalized cross-correlation between the
% two equally sized image patches I1 and I2. The result M is a scalar in
% the interval -1 (non match) to 1 (perfect match) that indicates similarity.
%
% Notes::
% - A value of 1 indicates identical pixel patterns.
... |
github | petercorke/machinevision-toolbox-matlab-master | savepnm.m | .m | machinevision-toolbox-matlab-master/savepnm.m | 1,916 | utf_8 | 704d4a9934ea395a26d64fc98f472958 | %SAVEPNM Write a PNM format file
%
% SAVEPNM(filename, image)
%
% Saves the image in a binary greyscale (P5) or color (P6)
% format image file depending on the number of planes.
%
% If the maximum pixel value is less than 1 assume image is
% normalized in range 0-1, so values are scaled up to range 0-255.
%
% SEE ALSO:... |
github | petercorke/machinevision-toolbox-matlab-master | npq_poly.m | .m | machinevision-toolbox-matlab-master/npq_poly.m | 1,538 | utf_8 | 2e0ede82b0a0687a2376657d70b0e687 | %NPQ_POLY Normalized central polygon moments
%
% M = NPQ_POLY(V, P, Q) is the PQ'th normalized central moment of the
% polygon with vertices described by the columns of V.
%
% Notes::
% - The points must be sorted such that they follow the perimeter in
% sequence (counter-clockwise).
% - If the points are clockwi... |
github | petercorke/machinevision-toolbox-matlab-master | xv.m | .m | machinevision-toolbox-matlab-master/xv.m | 1,118 | utf_8 | 58e49f214dd9a1c90184f55b11f5cb30 | %XV Display image using XV utility
%
% xv(im)
%
% Pipe image to the XV display utility
%
% SEE ALSO: idisp pnmfilt
% XV is available from ftp://ftp.cis.upenn.edu/pub/xv
%
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free so... |
github | petercorke/machinevision-toolbox-matlab-master | homoTrans.m | .m | machinevision-toolbox-matlab-master/homoTrans.m | 1,632 | utf_8 | f3b7b9f60b7d8113c6c6b5d212ae1ebd | % HOMOTRANS - homogeneous transformation of points
%
% Function to perform a transformation on homogeneous points/lines
% The resulting points are normalised to have a homogeneous scale of 1
%
% Usage:
% t = homoTrans(P,v);
%
% Arguments:
% P - 3 x 3 or 4 x 4 transformation matrix
% v - ... |
github | petercorke/machinevision-toolbox-matlab-master | distance.m | .m | machinevision-toolbox-matlab-master/distance.m | 1,169 | utf_8 | ecadc570b23235092ad4f8aeda59a74d | %DISTANCE Euclidean distances between sets of points
%
% D = DISTANCE(A,B) is the Euclidean distances between L-dimensional points
% described by the matrices A (LxM) and B (LxN) respectively. The distance
% D is MxN and element D(I,J) is the distance between points A(I) and B(J).
%
% Example::
% A = rand(400,100... |
github | petercorke/machinevision-toolbox-matlab-master | lzfd.m | .m | machinevision-toolbox-matlab-master/lzfd.m | 2,169 | utf_8 | 1280ce0a291d2c9d98b06a4673a98535 | %LZFD LZF decompression
%
% OUT = LZFD(IN) is the decompressed version of the uint8 array IN.
%
% OUT = LZFD(IN, LEN) as above but sets the internal working buffer to length
% LEN which should exceed the expected uncompressed data size.
%
% Notes::
% - LZF is an algorithm that is efficient and gives reasonable compres... |
github | petercorke/machinevision-toolbox-matlab-master | VideoCamera.m | .m | machinevision-toolbox-matlab-master/VideoCamera.m | 1,645 | utf_8 | 905abf199734746557a669c5d06989b5 | %VideoCamera Abstract class to read from local video camera
%
% A concrete subclass of ImageSource that acquires images from a local
% camera using the MATLAB Image Acquisition Toolbox (imaq). This Toolbox
% provides a multiplatform interface to a range of cameras, and this
% class provides a simple wrapper.
%
% This ... |
github | petercorke/machinevision-toolbox-matlab-master | ihough.m | .m | machinevision-toolbox-matlab-master/ihough.m | 3,299 | utf_8 | 75cdfd0229a2c94c03d6319faa7e5cf7 | %IHOUGH Hough transform
%
% params = IHOUGH
% H = IHOUGH(IM)
% H = IHOUGH(IM, params)
%
% Compute the Hough transform of the image IM data.
%
% The appropriate Hough accumulator cell is incremented by the
% absolute value of the pixel value if it exceeds
% params.edgeThresh times the maximum value found.
%
% Pixels wi... |
github | petercorke/machinevision-toolbox-matlab-master | iisum.m | .m | machinevision-toolbox-matlab-master/iisum.m | 1,337 | utf_8 | 5886b6678d654298ebca7e61cdd53892 | %IISUM Sum of integral image
%
% S = IISUM(II, U1, V1, U2, V2) is the sum of pixels in the rectangular image
% region defined by its top-left (U1,V1) and bottom-right (U2,V2). II is
% a precomputed integral image.
%
% See also INTGIMAGE.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machi... |
github | petercorke/machinevision-toolbox-matlab-master | lambda2xy.m | .m | machinevision-toolbox-matlab-master/lambda2xy.m | 1,512 | utf_8 | 31eb6d800fdab9b912d1747faf933af3 | % XY = LAMBDA2XY(LAMBDA) is the xy-chromaticity coordinate (1x2) for
% illumination at the specific wavelength LAMBDA [metres]. If LAMBDA is a
% vector (Nx1), then P (Nx2) is a vector whose elements are the luminosity
% at the corresponding elements of LAMBDA.
%
% XY = LAMBDA2XY(LAMBDA, E) is the rg-chromaticity coor... |
github | petercorke/machinevision-toolbox-matlab-master | addcircle.m | .m | machinevision-toolbox-matlab-master/addcircle.m | 1,355 | utf_8 | 09c8edd8231b6b08d8795ea318d5db4c | %ADDCIRCLE Add a circle to the current plot
%
% addcircle(center, radius)
% addcircle(center, radius, linestyle)
%
% Returns the graphics handle for the circle.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can... |
github | petercorke/machinevision-toolbox-matlab-master | iroi.m | .m | machinevision-toolbox-matlab-master/iroi.m | 3,004 | utf_8 | 059da0243b9eaba21f28035b24d5e396 | %IROI Extract region of interest
%
% OUT = IROI(IM,RECT) is a subimage of the image IM described by the
% rectangle RECT=[umin,umax; vmin,vmax].
%
% OUT = IROI(IM,C,S) as above but the region is centered at C=(U,V) and
% has a size S. If S is scalar then W=H=S otherwise S=(W,H).
%
% OUT = IROI(IM) as above but the im... |
github | petercorke/machinevision-toolbox-matlab-master | mpq_poly.m | .m | machinevision-toolbox-matlab-master/mpq_poly.m | 2,164 | utf_8 | 43737d5d1c3569e98a2f5d1c65f39f40 | %MPQ_POLY Polygon moments
%
% M = MPQ_POLY(V, P, Q) is the PQ'th moment of the polygon with vertices
% described by the columns of V.
%
% Notes::
% - The points must be sorted such that they follow the perimeter in
% sequence (counter-clockwise).
% - If the points are clockwise the moments will all be negated, so... |
github | petercorke/machinevision-toolbox-matlab-master | ianimate.m | .m | machinevision-toolbox-matlab-master/ianimate.m | 4,629 | utf_8 | b0ef7d51a1127c21789d7d2b3c095333 | %IANIMATE Display an image sequence
%
% IANIMATE(IM, OPTIONS) displays a greyscale image sequence IM (HxWxN) or
% a color image sequence IM (HxWx3xN) where N is the number of frames in
% the sequence, or a cell-array of length N and the elements are either
% greyscale (HxW) or color (HxWx3).
%
% IANIMATE(IM, FEATURES,... |
github | petercorke/machinevision-toolbox-matlab-master | colorseg.m | .m | machinevision-toolbox-matlab-master/colorseg.m | 276 | utf_8 | 88e6c057c2d28e9605722cf5891711b0 | %COLORSEG Color image segmentation using k-means
%
% THIS FUNCTION IS DEPRECATED, USE COLORKMEANS INSTEAD
%
% Notes::
% - deprecated. Use COLORKMEANS instead.
%
% See also COLORKMEANS.
function [a,b] = colorseg(x, y, z)
error('Deprecated: use colorkmeans() instead');
|
github | petercorke/machinevision-toolbox-matlab-master | imoments.m | .m | machinevision-toolbox-matlab-master/imoments.m | 3,742 | utf_8 | 08adfcfb058d1e9e1bc0de1c01138a1b | %IMOMENTS Image moments
%
% F = IMOMENTS(IM) is a RegionFeature object that describes the greyscale
% moments of the image IM.
%
% F = IMOMENTS(U, V) as above but the moments are computed from the pixel
% coordinates given as vectors U (Nx1) and V (Nx1). All pixels are equally
% weighted and is effectively a binary... |
github | petercorke/machinevision-toolbox-matlab-master | count_unique.m | .m | machinevision-toolbox-matlab-master/count_unique.m | 4,594 | utf_8 | 43fb1619f7b1942b4edd2d1364824b8a | function [uniques,numUnique] = count_unique(x,option)
%COUNT_UNIQUE Determines unique values, and counts occurrences
% [uniques,numUnique] = count_unique(x)
%
% This function determines unique values of an array, and also counts the
% number of instances of those values.
%
% This uses the MATLAB builtin... |
github | petercorke/machinevision-toolbox-matlab-master | col2im.m | .m | machinevision-toolbox-matlab-master/col2im.m | 1,438 | utf_8 | 0c0ae0676adafa43e80361def43406f1 | %COL2IM Convert pixel vector to image
%
% OUT = COL2IM(PIX, IMSIZE) is an image (HxWxP) comprising the pixel values in
% PIX (NxP) with one row per pixel where N=HxW. IMSIZE is a 2-vector (N,M).
%
% OUT = COL2IM(PIX, IM) as above but the dimensions of OUT are the same as IM.
%
% Notes::
% - The number of rows in PIX ... |
github | petercorke/machinevision-toolbox-matlab-master | epiline.m | .m | machinevision-toolbox-matlab-master/epiline.m | 1,624 | utf_8 | 4b7a7138ce9f4c394ce955ac6fc6e0e0 | %EPILINE Draw epipolar lines
%
% EPILINE(F, P) draws epipolar lines in current figure based on points P (2xN)
% and the fundamental matrix F (3x3). Points are specified by the columns of P.
%
% EPILINE(F, P, LS) as above but draw lines using the line style arguments LS.
%
% H = EPILINE(F, P, LS) as above but return a ... |
github | petercorke/machinevision-toolbox-matlab-master | iclose.m | .m | machinevision-toolbox-matlab-master/iclose.m | 1,680 | utf_8 | b416e08613f8f6e52dd5be47136e9009 | %ICLOSE Morphological closing
%
% OUT = ICLOSE(IM, SE, OPTIONS) is the image IM after morphological closing
% with the structuring element SE. This is a morphological dilation followed
% by an erosion.
%
% OUT = ICLOSE(IM, SE, N, OPTIONS) as above but the structuring element
% SE is applied N times, that is N erosi... |
github | petercorke/machinevision-toolbox-matlab-master | kcircle.m | .m | machinevision-toolbox-matlab-master/kcircle.m | 1,697 | utf_8 | d3014472523e6ecd385d70a1c00cc401 | %KCIRCLE Circular structuring element
%
% K = KCIRCLE(R) is a square matrix (WxW) where W=2R+1 of zeros with a maximal
% centred circular region of radius R pixels set to one.
%
% K = KCIRCLE(R,W) as above but the dimension of the kernel is explicitly
% specified.
%
% Notes::
% - If R is a 2-element vector the result... |
github | petercorke/machinevision-toolbox-matlab-master | intgimage.m | .m | machinevision-toolbox-matlab-master/intgimage.m | 1,256 | utf_8 | 0133716c4fee569cc111c5b6b4a55dc3 | %INTIMAGE Compute integral image
%
% OUT = INTIMAGE(IM) is an integral image corresponding to IM.
%
% Integral images can be used for rapid computation of summations over
% rectangular regions.
%
% Examples::
% Create integral images for sum of pixels over rectangular regions
% i = intimage(im);
%
% Create inte... |
github | petercorke/machinevision-toolbox-matlab-master | niblack.m | .m | machinevision-toolbox-matlab-master/niblack.m | 2,299 | utf_8 | daf8228a65e78fd0a74e07e411f1866f | %NIBLACK Adaptive thresholding
%
% T = NIBLACK(IM, K, W2) is the per-pixel (local) threshold to apply to
% image IM. T has the same dimensions as IM. The threshold at each pixel is
% a function of the mean and standard deviation computed over a WxW window,
% where W=2*w2+1.
%
% [T,M,S] = NIBLACK(IM, K, W2) as above... |
github | petercorke/machinevision-toolbox-matlab-master | idisplabel.m | .m | machinevision-toolbox-matlab-master/idisplabel.m | 1,647 | utf_8 | f0c499b2833ab29ec220e9bd2db724d8 | %IDISPLABEL Display an image with mask
%
% IDISPLABEL(IM, LABELIMAGE, LABELS) displays only those image pixels which
% belong to a specific class. IM is a greyscale (HxW) or color (HxWx3) image,
% and LABELIMAGE (HxW) contains integer pixel class labels for the
% corresponding pixels in IM. The pixel classes to be... |
github | petercorke/machinevision-toolbox-matlab-master | houghoverlay.m | .m | machinevision-toolbox-matlab-master/houghoverlay.m | 1,795 | utf_8 | ab0fa4f2afc7160ace1285f4fc79cb21 | %HOUGHOVERLAY Overlay lines on image.
%
% houghoverlay(p)
% houghoverlay(p, ls)
% handles = houghoverlay(p, ls)
%
% Overlay lines, one per row of p, onto the current figure. The row
% is interpretted as offset and theta, the Hough transform line
% representation.
%
% The optional argument, ls, gives the line style in ... |
github | petercorke/machinevision-toolbox-matlab-master | luminos.m | .m | machinevision-toolbox-matlab-master/luminos.m | 4,447 | utf_8 | 1954b09e77ca9eed2544187c928428f6 | %LUMINOS Photopic luminosity function
%
% P = LUMINOS(LAMBDA) is the photopic luminosity function for the wavelengths
% in LAMBDA [m]. If LAMBDA is a vector (Nx1), then P (Nx1) is a vector whose
% elements are the luminosity at the corresponding elements of LAMBDA.
%
% Luminosity has units of lumens which are the int... |
github | petercorke/machinevision-toolbox-matlab-master | loadstereo.m | .m | machinevision-toolbox-matlab-master/loadstereo.m | 1,171 | utf_8 | 81df479ebe7730f9940c76650bffd221 | %LOADSTEREO load & unmultiplex stereo image
%
% [left,right] = loadstereo(im)
% left = loadstereo(im)
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU ... |
github | petercorke/machinevision-toolbox-matlab-master | idouble.m | .m | machinevision-toolbox-matlab-master/idouble.m | 1,906 | utf_8 | 7cb0421b4ed83c451d6e308ca99babc1 | %IDOUBLE Convert integer image to double
%
% IMD = IDOUBLE(IM, OPTIONS) is an image with double precision elements in the
% range 0 to 1 corresponding to the elements of IM. The integer pixels IM
% are assumed to span the range 0 to the maximum value of their integer class.
%
% Options::
% 'single' Return an array ... |
github | petercorke/machinevision-toolbox-matlab-master | cos4correct.m | .m | machinevision-toolbox-matlab-master/cos4correct.m | 1,770 | utf_8 | 2509c031e4fb777fa549217cb1639038 |
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Li... |
github | petercorke/machinevision-toolbox-matlab-master | ipaste.m | .m | machinevision-toolbox-matlab-master/ipaste.m | 2,699 | utf_8 | f0315d214201ff2f629797cab225dd0c | %IPASTE Paste an image into an image
%
% OUT = IPASTE(IM, IM2, P, OPTIONS) is the image IM with the subimage IM2
% pasted in at the position P=[U,V].
%
% Options::
% 'centre' The pasted image is centred at P, otherwise P is the top-left
% corner of the subimage in IM (default)
% 'zero' the coordinate... |
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