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github | iN1k1/Sparse-Re-Identification-master | extract_single_feature.m | .m | Sparse-Re-Identification-master/src/NMLib/extract_single_feature.m | 3,808 | utf_8 | c0b56d947aff86719d319a6bb3e1a488 | function [ bodyParts ] = extract_single_feature( imageRGB, mask, weightImg, featType, featPars, pars )
%EXTRACT_SINGLE_FEATURE Summary of this function goes here
% Detailed explanation goes here
% Get images
featPars.detectTorsoAndLegs = pars.body.detectTorsoAndLegs;
featPars.splitEachBodyPart = pars.body.spli... |
github | iN1k1/Sparse-Re-Identification-master | feature_selection_weights.m | .m | Sparse-Re-Identification-master/src/NMLib/feature_selection_weights.m | 801 | utf_8 | f70952d4f724c639654ae4f36f9100d1 | function [W] = feature_selection_weights(A,B,th)
%#codegen
coder.inline('never')
%W = zeros(size(A,1), size(B,1));
%for ii=1:size(A,1)
% for jj=1:size(B,1)
% rho = mycorr(A(ii,:)',B(jj,:)');
% W(ii,jj) = mean(rho(:));
% end
%end
%tic
W = abs(corr(A',B'));
%toc
%tic
%mycorr(A,B);
%toc
W(isnan(W)) ... |
github | iN1k1/Sparse-Re-Identification-master | colorspace_demo.m | .m | Sparse-Re-Identification-master/src/NMLib/colorspace/colorspace_demo.m | 7,075 | utf_8 | e97534200e6a97850375ea8ec5d419cf | function colorspace_demo(Cmd)
% Demo for colorspace.m - 3D visualizations of various color spaces
% Pascal Getreuer 2006
if nargin == 0
% Create a figure with a drop-down menu
figure('Color',[1,1,1]);
h = uicontrol('Style','popup','Position',[15,10,90,21],...
'BackgroundColor',[1,1,1],'Value',... |
github | iN1k1/Sparse-Re-Identification-master | colorspace.m | .m | Sparse-Re-Identification-master/src/NMLib/colorspace/colorspace.m | 16,178 | utf_8 | 2ca0aee9ae4d0f5c12a7028c45ef2b8d | function varargout = colorspace(Conversion,varargin)
%COLORSPACE Transform a color image between color representations.
% B = COLORSPACE(S,A) transforms the color representation of image A
% where S is a string specifying the conversion. The input array A
% should be a real full double array of size Mx3 or MxN... |
github | iN1k1/Sparse-Re-Identification-master | NM_pdist.m | .m | Sparse-Re-Identification-master/src/NMLib/Dissimilarity/NM_pdist.m | 10,241 | utf_8 | 52ea741a73de887840c0431211e6c159 | % Calculates the distance between sets of vectors.
% Script inherited from
%
% Piotr's Image&Video Toolbox Version 2.0
% Copyright (C) 2007 Piotr Dollar. [pdollar-at-caltech.edu]
% Please email me if you find bugs, or have suggestions or questions!
% Licensed under the Lesser... |
github | iN1k1/Sparse-Re-Identification-master | slmetric_pw.m | .m | Sparse-Re-Identification-master/src/NMLib/Dissimilarity/pwmetric/slmetric_pw.m | 11,952 | utf_8 | 2f5cd151be6d57555cfa60c36d57fd60 | function M = slmetric_pw(X1, X2, mtype, varargin)
%SLMETRIC_PW Compute the metric between column vectors pairwisely
%
% [ Syntax ]
% - M = slmetric_pw(X1, X2, mtype);
% - M = slmetric_pw(X1, X2, mtype, ...);
%
% [ Arguments ]
% - X1, X2: the sample matrices
% - mtype: the string indicating... |
github | iN1k1/Sparse-Re-Identification-master | cont.m | .m | Sparse-Re-Identification-master/src/NMLib/Features/LBP/cont.m | 4,380 | utf_8 | 247a0f2e202329f1896616468e6ecadf | %C computes the VAR descriptor.
% J = CONT(I,R,N,LIMS,MODE) returns either a rotation invariant local
% variance (VAR) image or a VAR histogram of the image I. The VAR values
% are determined for all pixels having neighborhood defined by the input
% arguments. The VAR operator calculates variance on a circumfere... |
github | iN1k1/Sparse-Re-Identification-master | getmapping.m | .m | Sparse-Re-Identification-master/src/NMLib/Features/LBP/getmapping.m | 5,408 | utf_8 | e155c29bb5d9b49691f3fc4c110365de | %GETMAPPING returns a structure containing a mapping table for LBP codes.
% MAPPING = GETMAPPING(SAMPLES,MAPPINGTYPE) returns a
% structure containing a mapping table for
% LBP codes in a neighbourhood of SAMPLES sampling
% points. Possible values for MAPPINGTYPE are
% 'u2' for uniform LBP
% 'ri... |
github | chinpoo/Tidal-Response-master | runge_kutta.m | .m | Tidal-Response-master/runge_kutta.m | 24,582 | utf_8 | 16bc85886e6d4cfdda0e42ded120ad82 | % func: 4th order Runge-Kutta based propagator matrix method, used to solve
% 1st order ODE in matrix form. Main functionalities:
% 1. Construction of propagator matrix
% 2. Integration of (mode coupling) forcing vectors
% 3. Dealing with traction and Q (due to d_rho) discontinuities
% inp... |
github | chinpoo/Tidal-Response-master | solver.m | .m | Tidal-Response-master/solver.m | 7,292 | utf_8 | 3d2a4874fdef4ca78decf89c1a9d4973 | % func: slove linear equation for each mode, write solutions to file and
% output response
% input args:
% RK: structure of Runge-Kutta method ingredients
% MC: structure of info of a specific mode, including "parent" and coupling coefficients
% MD: structure of 1-D and 3-D model
% outputs... |
github | chinpoo/Tidal-Response-master | mode_coupling.m | .m | Tidal-Response-master/mode_coupling.m | 5,847 | utf_8 | 80789851782d748ce34e19301a9f8c6b | % func: create full mode coupling hierarchy, including "child" modes up to
% 2nd order of perturbation and the associated coupling coefficients.
% input args:
% l0,m0: harmonic of the 0th order "parent" mode (spheroidal)
% l1,m1: harmonic of the eigenstructure
% dir: directory where mode coupl... |
github | chinpoo/Tidal-Response-master | phi_func.m | .m | Tidal-Response-master/phi_func.m | 883 | utf_8 | 7cd877d237885f56b31b924bb42a9919 | % func: compute phi-dependent function of a real form SH and its first and
% second order derivatives, for purpose of numerical integration.
% input args:
% m: harmonic order
% phi: row vector of longitudinal angle "phi"
% outputs:
% f: vector of values of phi-dependent function evalauted... |
github | chinpoo/Tidal-Response-master | create_a_matrix.m | .m | Tidal-Response-master/create_a_matrix.m | 2,100 | utf_8 | fdff0fec5cd7a8e5db80e328c71db89e | % func: create A matrix in matrix equation dX/dr = A*X + F
% input args:
% r: radius
% g0: g0 at radius r
% l_mat: indexing of material layer
% L: harmonic degree of the mode
% mode: spheroidal (1) or toroidal (-1)
% model: structure of the model
% out... |
github | chinpoo/Tidal-Response-master | model_setup.m | .m | Tidal-Response-master/model_setup.m | 7,270 | utf_8 | dc56c66bee1f681dbc18ddfbcb60808b | % func: set up the 1-D and 3-D models before tidal response calculation
% input args:
% fname_1D: input file of 1-D profile of the planetary body
% ***** radius, density, vp, vs *****
% fname_3D: input file of 3-D structure in mu, lambda and rho
% ***** # of layers with 3-D st... |
github | chinpoo/Tidal-Response-master | nodal_mapping.m | .m | Tidal-Response-master/nodal_mapping.m | 625 | utf_8 | 6d44d167058604ca4c806fbf89b1bcbb | % func: mapping layered properties on to grid nodes
% input args:
% val: vector of layered properties from model
% nm: vector of numbers of subdivision in each layer
% outputs:
% nv: vector of nodal values
function [nv] = nodal_mapping(val,nm)
if length(val) ~= le... |
github | chinpoo/Tidal-Response-master | file_sol.m | .m | Tidal-Response-master/file_sol.m | 576 | utf_8 | 99e995a15cf498f1b6090d26fdddf13d | % func: create solution file name
% input args:
% l0,m0: harmonic of the tide
% l1,m1: harmonic of the eigenstructure
% mode,l,m,order: type,degree,order,order of pert of the mode
% outputs:
% fname: output file name
function [fname] = file_sol(mode,l,m,order,l0,m0,l1,m1)
global Output;... |
github | chinpoo/Tidal-Response-master | vsh_expan.m | .m | Tidal-Response-master/vsh_expan.m | 20,214 | utf_8 | 8c46eeb01f8625c51d82ce3ce434f6bc | % func: determine "child" modes and their VSH expansion coefficients for a
% given "parent" mode and an eigenstructure in mu, lambda and rho
% input args:
% l0,m0: harmonic of the "parent" mode
% l1,m1: harmonic of the eigenstructure
% mode: spheroidal (1) or toroidal (-1) of the "parent" mode
... |
github | chinpoo/Tidal-Response-master | theta_func.m | .m | Tidal-Response-master/theta_func.m | 3,090 | utf_8 | 6ae93ad865a68a63c275803c43bc031c | % func: compute prefactorized associated legendre polynomial (theta
% dependent)and its derivatives, for purpose of numerical integration.
% input args:
% l,m: harmonic degree and order
% theta: row vector of co-latitude angle "theta"
% outputs:
% f: vector of values of theta-dependent fu... |
github | chinpoo/Tidal-Response-master | net_force.m | .m | Tidal-Response-master/net_force.m | 936 | utf_8 | 1cf98c61795de5d1fbec713d5bd377af | % func: compute net force/acceleration on the system caused by coupling of
% tidal force and d_rho, manifested by a 1st order degree "child"
% mode
% input args:
% fP,fP: Plm and Blm components of f_tide = -d_rho*grad(V_td)
% r: vector of nodal radius
% mass: mass of the planet
% ou... |
github | chinpoo/Tidal-Response-master | vis_model.m | .m | Tidal-Response-master/vis_model.m | 1,132 | utf_8 | a31112859e123ffc6cb4580750bad760 | % func: draw circles to visualize radial boundaries in the model
% % input args:
% r0: column vector of radius of material layers, from small to large radius
% r_lower: column vector of radius of lh lower boundaries
% r_upper: column vector of radius of lh upper boundaries
function vis... |
github | chinpoo/Tidal-Response-master | file_vsh.m | .m | Tidal-Response-master/file_vsh.m | 601 | utf_8 | 41ee36bbbe84f5f086963b8cc9564b2c | % func: create output file name for vsh_expan
% input args:
% l0,m0: harmonic of the "parent" mode
% l1,m1: harmonic of the eigenstructure
% mode: spheroidal (1) or toroidal (-1) of the "parent" mode
% dir: output file directory
% outputs:
% fname: output file name
function [fname] = fil... |
github | chinpoo/Tidal-Response-master | compute_mass.m | .m | Tidal-Response-master/compute_mass.m | 1,907 | utf_8 | 75e7f71d35f3a7bd7e83a925f5133f65 | % func: compute the total non-dim mass below certain radius for a given density profile.
% input args:
% r: column vector of radius, from small to large radius
% rho_0: column vector of density profile, from bottom to surface
% R_0: column vector of radius of layer boundary, from bottom to
% ... |
github | SFMWISE2016/SFM10Halton_priAsiopt-master | SFM10Halton_priAsiopt.m | .m | SFM10Halton_priAsiopt-master/SFM10Halton_priAsiopt.m | 2,638 | utf_8 | 13cdc16ff686f8709dd69492b8067d44 | % clearing work&preparing
clear
close all
clc
% parameter setting
S0 =50; % Price of underlying today
X = 50; % Strike at expiry
mu = 0.04; % expected return
sig = 0.1; % expected vol.
r = 0.03; % Risk free rate
dt = 1/365; % time steps
steps = 50; % days to expiry
T = dt*steps; % ... |
github | seandepagnier/RTIMULib2-master | mag_fit_display.m | .m | RTIMULib2-master/RTEllipsoidFit/mag_fit_display.m | 1,220 | utf_8 | 12d8d12ef7f37e898204f4486f64cb9f | %//
%// Copyright (c) 2014, richards-tech
%//
%// This file is part of RTEllipsoidFit
%//
%// RTEllipsoidFit is free software: you can redistribute it and/or modify
%// it under the terms of the GNU General Public License as published by
%// the Free Software Foundation, either version 3 of the License, or
%// (... |
github | mjbays/MATLAB-OPL-Interface-master | createOPLParams.m | .m | MATLAB-OPL-Interface-master/createOPLParams.m | 3,940 | utf_8 | b1097fe4c7225a0e0d05728d05e5361b | % =====================================================================
%> @brief This function converts the needed information in an
% SATP_Parameters class object into an array of OPL_Parameter objects.
%> Author: Dr. Matthew Bays <matthew.bays@navy.mil>
%> Naval Surface Warface Center Panama City Division
%> Created... |
github | mjbays/MATLAB-OPL-Interface-master | postProcessData.m | .m | MATLAB-OPL-Interface-master/postProcessData.m | 2,599 | utf_8 | 0a541031e560764625d1573ecf4c6b12 | %> @file postProcessData.m
%> @brief Function to post-process .txt files output from model file
%> and bring output data back into MATLAB.
% ======================================================================
%> @brief Function to post-process .txt files output from model file
%> and bring output data back into MATL... |
github | mjbays/MATLAB-OPL-Interface-master | importOPLParams.m | .m | MATLAB-OPL-Interface-master/matlab/importOPLParams.m | 6,833 | utf_8 | 0da8b4016dd877a08520d2ecbacfb570 | % =====================================================================
%> @brief this function takes in a list of OPL_Parameter class objects
%> and writes them to a dat file. There must be a parameter
%> name for each parameter value (i.e. the lengths of the arrays are equal)
%>
%> Author: Michael Yahknis (NREIP I... |
github | mjbays/MATLAB-OPL-Interface-master | importMatDat.m | .m | MATLAB-OPL-Interface-master/matlab/importMatDat.m | 3,196 | utf_8 | 64b1ce0d8dcb3646e01d22d5a142f9ba | % =====================================================================
%> @brief this function takes in a cell array of parameter names and a cell array of
%>parameter values and writes them to a dat file. There must be a parameter
%>name for each parameter value (i.e. the lengths of the arrays are equal)
%>
%> Auth... |
github | mjbays/MATLAB-OPL-Interface-master | callOPL.m | .m | MATLAB-OPL-Interface-master/matlab/callOPL.m | 4,985 | utf_8 | 2a64fb390fe1d17f0b27715194fb1e84 | % =====================================================================
%> @brief this matlab script loads the Java API for OPL, specifies the OPL model and
%> data file, adds additional parameters to the data file, then returns the
%> OPL solution as a MATLAB struct (soln)
%> Author: Michael Yahknis (NREIP Internship... |
github | gfacciol/ipol-matlab-master | computeColor.m | .m | ipol-matlab-master/tvl1flow_3/Matlab/computeColor.m | 3,142 | utf_8 | a36a650437bc93d4d8ffe079fe712901 | function img = computeColor(u,v)
% computeColor color codes flow field U, V
% According to the c++ source code of Daniel Scharstein
% Contact: schar@middlebury.edu
% Author: Deqing Sun, Department of Computer Science, Brown University
% Contact: dqsun@cs.brown.edu
% $Date: 2007-10-31 21:20:30 (Wed, 31 O... |
github | vllab/roadscene-master | untitled.m | .m | roadscene-master/labeling tool/untitled.m | 7,114 | utf_8 | 172918f44d3d1b50324a20608522aba3 | function varargout = untitled(varargin)
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @untitled_OpeningFcn, ...
'gui_OutputFcn', @untitled_O... |
github | GinYM/age_gender_detection-master | pushbutton1_Callback.m | .m | age_gender_detection-master/age-gender-detection/pushbutton1_Callback.m | 704 | utf_8 | a7889e12785deeee3ec34aba5864ad94 | % Add the gender classification after the face dection. (without preprocessing)
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structu... |
github | GinYM/age_gender_detection-master | JGetFaces.m | .m | age_gender_detection-master/age-gender-detection/JGetFaces.m | 1,051 | utf_8 | 4366617b9956bfeed24fe0b96171c2ae | %% Face dection
function [I_faces,box]= JGetFaces(faceDetector, I)
[w h b]=size(I);
if w>300
I=imresize(I,[300 300*h/w]);
end
[w h b]=size(I);
if h>400
I=imresize(I,[400*w/h 400]);
end
% dection
bbox = step(faceDetector, I);
sexs=zeros(size(bbox, 1),1);
n=size(bbox, 1);
text_str = cell(n,1);
position = zeros(n,... |
github | GinYM/age_gender_detection-master | GenderRec.m | .m | age_gender_detection-master/age-gender-detection/GenderRec.m | 308 | utf_8 | b76eb63455a5d3bac80ebfcccc168d88 | % Gender Classification Function
function class = GenderRec(image)
load data
class = 0;
image = reshape(image,6400,1);
Tt_DAT = double(image);
tt_dat = disc_set'*Tt_DAT;
tt_dat = tt_dat./( repmat(sqrt(sum(tt_dat.*tt_dat)), [par.nDim,1]) );
class = CRC_RLS(tr_dat,Proj_M,tt_dat,trls);
end |
github | tonyabracadabra/Deep-Subspace-Clustering-master | Misclassification.m | .m | Deep-Subspace-Clustering-master/SSC_1.0/Misclassification.m | 734 | utf_8 | 3b553cd5fc50c024270bd4ba8de1c2fc | %--------------------------------------------------------------------------
% This function takes the groups resulted from spectral clutsering and the
% ground truth to compute the misclassification rate.
% groups: [grp1,grp2,grp3] for three different forms of Spectral Clustering
% s: ground truth vector
% Missrate: 3x... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | BuildAdjacency.m | .m | Deep-Subspace-Clustering-master/SSC_1.0/BuildAdjacency.m | 1,036 | utf_8 | 1788e227e213caedb31998d4a254034c | %--------------------------------------------------------------------------
% This function takes a NxN coefficient matrix and returns a NxN adjacency
% matrix by choosing only the K strongest connections in the similarity
% graph
% CMat: NxN coefficient matrix
% K: number of strongest edges to keep; if K=0 use all the... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | missclassGroups.m | .m | Deep-Subspace-Clustering-master/SSC_1.0/missclassGroups.m | 1,099 | utf_8 | 6ffcc03323c664987bd37aae59e91b29 | %--------------------------------------------------------------------------
% [miss,index] = missclass(Segmentation,RefSegmentation,ngroups)
% Computes the number of missclassified points in the vector Segmentation.
% Segmentation: 1 by sum(npoints) or sum(ngroups) by 1 vector containing
% the label for each group, r... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SparseCoefRecovery.m | .m | Deep-Subspace-Clustering-master/SSC_1.0/SparseCoefRecovery.m | 3,615 | utf_8 | cc831870c242b6a31b96a4e5f1475ff7 | %--------------------------------------------------------------------------
% This function takes the D x N matrix of N data points and write every
% point as a sparse linear combination of other points.
% Xp: D x N matrix of N data points
% cst: 1 if using the affine constraint sum(c)=1, else 0
% Opt: type of optimiza... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | DataProjection.m | .m | Deep-Subspace-Clustering-master/SSC_1.0/DataProjection.m | 1,505 | utf_8 | cef106e68a393a7e5deed6b368101a9b | %--------------------------------------------------------------------------
% This function takes the D x N data matrix with columns indicating
% different data points and project the D dimensional data into the r
% dimensional space. Different types of projections are possible:
% (1) Projection using PCA
% (2) Project... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SpectralClustering.m | .m | Deep-Subspace-Clustering-master/SSC_1.0/SpectralClustering.m | 2,037 | utf_8 | fa07e79a2d0dd4c5b0919934bbd7c796 | %--------------------------------------------------------------------------
% This function takes a NxN matrix CMat as adjacency of a graph and
% computes the segmentation of data from spectral clustering.
% CMat: NxN adjacency matrix
% n: number of groups for segmentation
% K: number of largest coefficients to choose... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | OutlierDetection.m | .m | Deep-Subspace-Clustering-master/SSC_1.0/OutlierDetection.m | 1,322 | utf_8 | b6f1a1dc98d866c5be38ea52873cbb09 | %--------------------------------------------------------------------------
% This function takes the coefficient matrix resulted from sparse
% representation using \ell_1 minimization. If a point cannot be written as
% a linear combination of other points, it should be an outlier. The
% function detects the indices of... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | solver.m | .m | Deep-Subspace-Clustering-master/smop/solver.m | 1,287 | utf_8 | d0509bdafb0321c2615cf57baf658a04 | function mv = solver(ai,af,w)
rand(1,2,3);
%
% Copyright 2004 The MathWorks, Inc.
nBlocks = max(ai(:));
[m,n] = size(ai);
% Make increment tables
% N=1, E=2, S=3, W=4
I = [0 1 0 -1];
J = [1 0 -1 0];
a = ai;
mv = [];
while ~isequal(af,a)
% Pick a random block
bid = ceil(rand()*nBlocks... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | fastsolver.m | .m | Deep-Subspace-Clustering-master/smop/fastsolver.m | 57,053 | utf_8 | 1df580dcdc8c9a5f9f974b699be11e0d | function moves=solver(A,B,w0)
[moves,optmove,optscore]=cbest(A,B,w0);
curscore=sum(w0(moves(:,1)));
lots=1;
if length(moves)-optmove<20||curscore/optscore<1.05
lots=2; return
else
lenw=length(w0);
[xx,nseq]=sort(rand(1,lenw));
A1=A;
B1=B;
w01=w0;
for i=1:lenw
A1(A==i)=nseq(i);
... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolveHomotopy_CBM_std.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolveHomotopy_CBM_std.m | 14,150 | utf_8 | 0f34b57c5581f7989098a76c1cbe6c28 | %% This function is modified from Matlab Package: L1-Homotopy
% BPDN_homotopy_function.m
%
% Solves the following basis pursuit denoising (BPDN) problem
% min_x \lambda ||x||_1 + 1/2*||y-Ax||_2^2
%
% Inputs:
% A - m x n measurement matrix
% y - measurement vector
% lambda - final value of regularization p... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolveHomotopy.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolveHomotopy.m | 13,840 | utf_8 | c7cf9926f2cb60f4cb78b7128b78d740 | %% This function is modified from Matlab Package: L1-Homotopy
% BPDN_homotopy_function.m
%
% Solves the following basis pursuit denoising (BPDN) problem
% min_x \lambda ||x||_1 + 1/2*||b-Ax||_2^2
%
% Inputs:
% A - m x n measurement matrix
% b - measurement vector
% lambda - final value of regularization p... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolvePDIPA_CBM.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolvePDIPA_CBM.m | 7,473 | utf_8 | 46c8caaf1b54eb2d43e340a937410116 | % The following primal-dual interior-point algorithm is modified from l1eq_pd.m
%
% Solve
% min_x ||x||_1 s.t. Ax = b
%
% Recast as linear program
% min_{x,u} sum(u) s.t. -u <= x <= u, Ax=b
% and use primal-dual interior point method
%
% Usage: xp = l1eq_pd(x0, A, At, b, pdtol, pdmaxiter, cgtol, cgmaxiter)
%
% x0 ... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | sesoptn_t.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/sesoptn_t.m | 30,952 | utf_8 | a0175a14e1485fd13ea137b9795d1499 | function [x,diff_x, tt, ids, report] =sesoptn_t(x, x00, recData, t00, func_u, func_x, multA, multAadj, options, par)
%Sequential subspace optimization (SESOP) combined with Truncated Newton
%
% minimize func_u(Ax) +func_x(x)
%
% Call: [x,report] =sesoptn(x, func_u, func_x, multA, multAadj, ... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolveTFOCS.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolveTFOCS.m | 3,104 | UNKNOWN | 79f747816204ea855d4ca1b81a1d346d | % Copyright �2011. The Regents of the University of California (Regents).
% All Rights Reserved. Contact The Office of Technology Licensing,
% UC Berkeley, 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620,
% (510) 643-7201, for commercial licensing opportunities.
% Authors: Arvind Ganesh, Allen Y. Yang, Z... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolveTFOCS_CBM.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolveTFOCS_CBM.m | 3,330 | UNKNOWN | ce4446cb69825b133f5ed50d2054c9a7 | % Copyright �2011. The Regents of the University of California (Regents).
% All Rights Reserved. Contact The Office of Technology Licensing,
% UC Berkeley, 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620,
% (510) 643-7201, for commercial licensing opportunities.
% Authors: Arvind Ganesh, Allen Y. Yang, Z... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolveL1LS_CBM.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolveL1LS_CBM.m | 9,927 | utf_8 | c360b6fc0783d48f23c2222e88381039 | %% This function is modified from Matlab Package l1_ls
function [x_out, e_out, ntiter ,timeSteps, errorSteps, idSteps, status] = SolveL1LS_CBM(A,y,varargin)
%
% l1-Regularized Least Squares Problem Solver
%
% l1_ls solves problems of the following form:
%
% minimize ||A*x-y||^2 + lambda*sum|x_i|,
%
%... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolvePALM_CBM.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolvePALM_CBM.m | 5,422 | utf_8 | f69afccf1f624b94f8c3409e58f5d709 | function [x, e, nIter, timeSteps, errorSteps, idSteps] = SolvePALM_CBM(A, b, varargin)
t0 = tic ;
DEBUG = 0 ;
STOPPING_TIME = -2;
STOPPING_GROUND_TRUTH = -1;
STOPPING_DUALITY_GAP = 1;
STOPPING_SPARSE_SUPPORT = 2;
STOPPING_OBJECTIVE_VALUE = 3;
STOPPING_SUBGRADIENT = 4;
STOPPING_INCREMENTS = 5 ;
STOPPING_DEFAULT = STOP... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolveSpaRSA_CBM.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolveSpaRSA_CBM.m | 24,182 | utf_8 | d32d88819ae2e9c963667a722e8b1198 | %% This function is modified from Matlab Package SpaRSA
function [x,e,iter]= SolveSpaRSA_CBM(A,y,varargin)
% SpaRSA version 2.0, December 31, 2007
%
% This function solves the convex problem
%
% arg min_x = 0.5*|| y - A x ||_2^2 + lambda phi(x)
%
% using the SpaRSA algorithm, which is described in "Sparse Reconstruct... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolveHomotopy_CBM.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolveHomotopy_CBM.m | 14,594 | utf_8 | 7023317b6856d4c1f1bafed7270d3519 | %% This function is modified from Matlab Package: L1-Homotopy
% BPDN_homotopy_function.m
%
% Solves the following basis pursuit denoising (BPDN) problem
% min_x \lambda ||x||_1 + 1/2*||y-Ax||_2^2
%
% Inputs:
% A - m x n measurement matrix
% y - measurement vector
% lambda - final value of regularization p... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolvePDIPA_CBM_std.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolvePDIPA_CBM_std.m | 6,360 | utf_8 | 3054b659ae8e10500554d0a3ae42763c | % The following primal-dual interior-point algorithm is modified from l1eq_pd.m
%
% Solve
% min_x ||x||_1 s.t. Ax = b
%
% Recast as linear program
% min_{x,u} sum(u) s.t. -u <= x <= u, Ax=b
% and use primal-dual interior point method
%
% Usage: xp = l1eq_pd(x0, A, At, b, pdtol, pdmaxiter, cgtol, cgmaxiter)
%
% x0 ... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolveAMP.m | .m | Deep-Subspace-Clustering-master/l1benchmark/L1Solvers/SolveAMP.m | 5,334 | utf_8 | 024bbdb6f07bcb1c7f924f9c5d9e6dc7 | function [x_t, nIter, timeSteps, errorSteps, nShrinkage] = SolveAMP(A, b, varargin)
% Solve
% min_x ||x||_1 s.t. Ax = b
t0 = tic;
DEBUG = 0 ;
DISPLAY = 0 ;
STOPPING_TIME = -2;
STOPPING_GROUND_TRUTH = -1;
STOPPING_DUALITY_GAP = 1;
STOPPING_SPARSE_SUPPORT = 2;
STOPPING_OBJECTIVE_VALUE = 3;
STOPPING_SUBGRADIENT = 4;
... |
github | tonyabracadabra/Deep-Subspace-Clustering-master | SolveHomotopy.m | .m | Deep-Subspace-Clustering-master/supporting_files/SolveHomotopy.m | 14,717 | utf_8 | d4d261ef1d6b7363d521954ca7f67154 | % Test script for comparing fast L-1 solvers via Gaussian projections
% Copyright ?010. The Regents of the University of California (Regents).
% All Rights Reserved. Contact The Office of Technology Licensing,
% UC Berkeley, 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620,
% (510) 643-7201, for commer... |
github | codetaobeibei/Coursera-Robotics-Estimation-and-Learning-master | detectBall.m | .m | Coursera-Robotics-Estimation-and-Learning-master/assignment1/detectBall.m | 1,361 | utf_8 | 35564bcaf923cd345fd90890cbb04b73 | % Robotics: Estimation and Learning
% WEEK 1
%
% Complete this function following the instruction.
function [segI, loc] = detectBall(I)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Hard code your learned model parameters here
%
mu = [149.7198 144.5757 60.8763];
sig = diag([180.8987 128.4632 339.5755]);
thre... |
github | codetaobeibei/Coursera-Robotics-Estimation-and-Learning-master | occGridMapping_2.m | .m | Coursera-Robotics-Estimation-and-Learning-master/assignment3/AssignmentWEEK3/occGridMapping_2.m | 2,093 | utf_8 | 4e0e01e16936d07ae93bc3c7abe14f51 | % Robotics: Estimation and Learning
% WEEK 3
%
% Complete this function following the instruction.
function myMap = occGridMapping(ranges, scanAngles, pose, param)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
% Parameters
%
% % the number of grids for 1 meter.
r = param.resol;
% the initial map size in pixels
myMap = z... |
github | codetaobeibei/Coursera-Robotics-Estimation-and-Learning-master | occGridMapping.m | .m | Coursera-Robotics-Estimation-and-Learning-master/assignment3/AssignmentWEEK3/occGridMapping.m | 2,124 | utf_8 | 1abf6da9a2a3e3122f3f44e1d1f915c1 | % Robotics: Estimation and Learning
% WEEK 3
%
% Complete this function following the instruction.
function myMap = occGridMapping(ranges, scanAngles, pose, param)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
% Parameters
%
% % the number of grids for 1 meter.
r = param.resol;
% the initial map size in pixels
myMap = z... |
github | codetaobeibei/Coursera-Robotics-Estimation-and-Learning-master | particleLocalization.m | .m | Coursera-Robotics-Estimation-and-Learning-master/assignment4/AssignmentWEEK4/particleLocalization.m | 5,199 | utf_8 | 9e45daebe018c7eae3f52aad927b4bc4 | % Robotics: Estimation and Learning
% WEEK 4
%
% Complete this function following the instruction.
function myPose = particleLocalization(ranges, scanAngles, map, param)
% Number of poses to calculate
N = size(ranges, 2);
%n = size(scanAngles, 1);
% Output format is [x1 x2, ...; y1, y2, ...; z1, z2, ...]
myPose = z... |
github | brennanyama/robotx-master | controller_comm_with_ros.m | .m | robotx-master/controller_comm_with_ros.m | 9,368 | utf_8 | c2884a7c41de253949166e91959c03af | % To do: should be modified to allow the goal-vars to change on
% the fly.
function heading_velocity_controller_and_simulation()
% Clear workspace
clear all;
close all;
clc;
%ros node initiate
rosinit
% Simulation setup
[dt,tend,N,m,I,bu,bv,bpsi,theta,w,l,K,x,T,u,u... |
github | brennanyama/robotx-master | convG2M.m | .m | robotx-master/pathplanning/Version_01/convG2M.m | 465 | utf_8 | 8f77425695c483c18b0a121c02bfa1ed | %% convGrid2Mat -- used to convert an occupancy grid to a matrix, which
% then can be used in Dstar.
function occupancyMatrix = convG2M(occupancyGrid)
% converts this BinaryOccupancyGrid from ROS into a matrix for Dstar
% compatibility
gSize = occupancyGrid.GridSize;
tempMat = zeros(gSize(1), gSize(2... |
github | brennanyama/robotx-master | boundmatch.m | .m | robotx-master/pathplanning/Version_02/vision/boundmatch.m | 1,663 | utf_8 | 12568d3fa4ed8e87bcb114db1d77d013 | %BOUNDMATCH Match boundary profiles
%
% X = BOUNDMATCH(R1, R2) is the correlation of the two boundary profiles
% R1 and R2. Each is an Nx1 vector of distances from the centroid of
% an object to points on its perimeter at equal angular increments spanning
% 2pi radians. X is also Nx1 and is a correlation whose peak i... |
github | brennanyama/robotx-master | kdog.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | e2h.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | radgrad.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | imono.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | epidist.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | ssd.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | stdisp.m | .m | robotx-master/pathplanning/Version_02/vision/stdisp.m | 3,367 | utf_8 | 94cdbecfef9670513151b3c9e4d914eb | %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 | brennanyama/robotx-master | npq.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | kdgauss.m | .m | robotx-master/pathplanning/Version_02/vision/kdgauss.m | 1,516 | utf_8 | 1c010fe23b545267d63c523f0c63784a | %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 | brennanyama/robotx-master | igamm.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | mkgrid.m | .m | robotx-master/pathplanning/Version_02/vision/mkgrid.m | 2,114 | utf_8 | f6cbc82206e9c19ebf95c1f1cf89bd4d | %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 | brennanyama/robotx-master | iscolor.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | iopen.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | idisp.m | .m | robotx-master/pathplanning/Version_02/vision/idisp.m | 19,503 | utf_8 | 2892cfa387967a11719caafb844886b8 | %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 | brennanyama/robotx-master | ktriangle.m | .m | robotx-master/pathplanning/Version_02/vision/ktriangle.m | 1,778 | utf_8 | ac9766d386a5ebff0cf74a2d526b0e6e | %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 | brennanyama/robotx-master | kgauss.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | ibbox.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | mkcube.m | .m | robotx-master/pathplanning/Version_02/vision/mkcube.m | 2,880 | utf_8 | 2e620564e4da962a9bb63acd6bacc3ab | %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 | brennanyama/robotx-master | rg_addticks.m | .m | robotx-master/pathplanning/Version_02/vision/rg_addticks.m | 1,266 | utf_8 | 3dd4cc765e61a34cb352f420c2a79fea | %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 | brennanyama/robotx-master | iscale.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | zssd.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | ncc.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | npq_poly.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | distance.m | .m | robotx-master/pathplanning/Version_02/vision/distance.m | 1,169 | utf_8 | 54582fe57ff9308e120581b82b716bfd | %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 D(J).
%
% Example::
% A = rand(400,100... |
github | brennanyama/robotx-master | VideoCamera.m | .m | robotx-master/pathplanning/Version_02/vision/VideoCamera.m | 1,555 | utf_8 | dc00061c1651d90be25a4c14ea16da24 | %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 | brennanyama/robotx-master | iisum.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | lambda2xy.m | .m | robotx-master/pathplanning/Version_02/vision/lambda2xy.m | 1,508 | utf_8 | 6e85b20df5227dd4d798c294394be636 | % 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 | brennanyama/robotx-master | iroi.m | .m | robotx-master/pathplanning/Version_02/vision/iroi.m | 3,003 | utf_8 | 27f925f6d3134a6fae0ba9598b7b8292 | %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 | brennanyama/robotx-master | mpq_poly.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | ianimate.m | .m | robotx-master/pathplanning/Version_02/vision/ianimate.m | 4,047 | utf_8 | 037273ca327f959b0ea5a2ab61ac6278 | %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.
%
% IANIMATE(IM, FEATURES, OPTIONS) as above but with point features overlaid.
% FEATURES (Nx1) is a cell array whose ... |
github | brennanyama/robotx-master | colorseg.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | imoments.m | .m | robotx-master/pathplanning/Version_02/vision/imoments.m | 3,740 | utf_8 | c5fea88b41622e9569b440271b2c0ae3 | %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 | brennanyama/robotx-master | col2im.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | epiline.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | iclose.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | kcircle.m | .m | robotx-master/pathplanning/Version_02/vision/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 | brennanyama/robotx-master | intgimage.m | .m | robotx-master/pathplanning/Version_02/vision/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... |
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