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github | anshkumar/Voltage-Stability-Toolbox-master | sim_2_sing.m | .m | Voltage-Stability-Toolbox-master/sim_2_sing.m | 1,529 | utf_8 | edb2a95ab453e6bcbb18ffc1f02797a0 | % This function is called by ode45 to compute values of xdot.
% It is set up to use PoC models.
function [xdot,xx_rem]=sim_2_sing(t,xx,x_rem,no_gen,no_pv,no_pq,...
data,param,CurrentSystem,gen_inertia,gen_damp)
% Partition the states, the second part (xp) is velocity
x=[xx(1:no_gen-1)',x_rem]';
x... |
github | anshkumar/Voltage-Stability-Toolbox-master | savedata.m | .m | Voltage-Stability-Toolbox-master/savedata.m | 729 | utf_8 | 5dbf48d532b0b7386b1f98d016cac9e6 |
function savedata(no_bus,bus_data,no_lines,branch_data,path,name)
%Store edited VST data in current file
%bus_data=[new_bus_nmbr;old_bus_nmbr;bus_name(1:no_bus,1:12)';bus_type(1:no_bus);bus_p;bus_q;bus_condc;bus_suscp;bus_v;bus_angl];
%branch_data=[tap_bus;z_bus;trans_type;brch_r;brch_x;cnrl_bus_nmbr;min_tp_shft... |
github | anshkumar/Voltage-Stability-Toolbox-master | sim_2.m | .m | Voltage-Stability-Toolbox-master/sim_2.m | 1,639 | utf_8 | 320a26169ad8f5e3769153edf3171683 | % This function is called by ode45 to compute values of xdot.
% It is set up to use PoC models.
function [xdot,xx_rem]=sim_2(t,xx,x_rem,no_gen,no_pv,no_pq,...
data,param,CurrentSystem,gen_inertia,gen_damp)
% Partition the states, the second part (xp) is velocity
x=[xx(1:no_gen-1)',x_rem]';
xp=xx(no... |
github | anshkumar/Voltage-Stability-Toolbox-master | energy.m | .m | Voltage-Stability-Toolbox-master/energy.m | 683 | utf_8 | 4edf4885be13e8dd8d4676bbd16140cb | % function to do the evaluation of energy function
function E=energy(i,no_pv,no_gen,no_pq,PPr,angle,voltage)
load c:\YB YB % Ybus
YB=full(imag(YB));% assuming a lossless sys. take out when a real lossless sys is used
u=no_pv+no_gen;
v=no_pq+u;
Pgen=dot(PPr(1:u),angle(1:u)); % gen and PV buses
Pload=dot(P... |
github | kunaljathal/Pitch-Estimation-master | ZeroCrossing.m | .m | Pitch-Estimation-master/ZeroCrossing.m | 1,470 | utf_8 | 2fc975e0c85d48a1ab1357008936275e | % Kunal Jathal
%
% Zero Crossing Rate - Pitch Detection
% ====================================
function [zcr, zc] = ZeroCrossing(x, windowLength)
% Get variables ready
xMean = zeros(1,length(x));
lenX1 = length(x);
windowIndex = 1;
meanIndex = 1;
zc = [];
% Moving Average Filter to smooth out the signa... |
github | kunaljathal/Pitch-Estimation-master | cepstrum.m | .m | Pitch-Estimation-master/cepstrum.m | 638 | utf_8 | f0393e09f81187a7b7a14680c39e7b98 | % Kunal Jathal
%
% Cepstrum Analysis - - Pitch Detection
% =====================================
function sample = cepstrum(inputSignal, fs, minFrequency, maxFrequency)
% Get the DFT
fftLength = 1024;
theFFT = fft(inputSignal.*hamming(length(inputSignal)), fftLength);
% Cepstrum Computation
xCepstrum = ifft(log10(ab... |
github | kunaljathal/Pitch-Estimation-master | chroma.m | .m | Pitch-Estimation-master/chroma.m | 1,578 | utf_8 | ddf6d134545f8fee7262cccd71c50df3 | % Kunal Jathal
%
% Chroma - - Pitch Detection
% ==========================
function chromaPitch = chroma(inputSignal, fs)
% Let's first make our chroma 'constant Q' pitch classes. We will go from
% C0 to C7. C0 is 16.35 Hz. C7 is 84 semitones above C0.
constantC0 = 16.35;
chromaPitches = [constantC0];
for semitone=... |
github | kunaljathal/Pitch-Estimation-master | HarmonicProductSpectrum.m | .m | Pitch-Estimation-master/HarmonicProductSpectrum.m | 982 | utf_8 | 63ceb0c82e5128a52efc29a76960628a | % Kunal Jathal
%
% Harmonic Product Spectrum - Pitch Detection
% ===========================================
function peakPosition = HarmonicProductSpectrum(inputSignal, fs, endFactor)
% DFT
fftLength = 1024;
theFFT = abs(fft(inputSignal.*hamming(length(inputSignal)), fftLength));
tempArray = [];
finalArray = theFFT... |
github | kunaljathal/Pitch-Estimation-master | AutoCorrelation.m | .m | Pitch-Estimation-master/AutoCorrelation.m | 1,577 | utf_8 | 168650866590ee86dccff42ee6fad138 | % Kunal Jathal
%
% Auto Correlation - Pitch Detection
% ==================================
function peakPeriod = AutoCorrelation(inputSignal, thold)
% Get the autocorrelation vector of the signal
autoCorr = xcorr(inputSignal);
% Now we need to get the period between peaks to get the fundamental freq.
% initialize s... |
github | kunaljathal/Pitch-Estimation-master | InverseCombFilter.m | .m | Pitch-Estimation-master/InverseCombFilter.m | 976 | utf_8 | b6c7aa3925d8796f4f277b00213149c8 | % Kunal Jathal
%
% Inverse Comb Filtering - Pitch Detection
% ========================================
function delayN = InverseCombFilter(inputSignal, fs, lowerBound, upperBound)
% Pre-emphasis filter (which is basically a simple high pass filter)
b1 = [1 -0.99];
a1 = 1;
x1 = filter(b1, a1, inputSignal);
% Inverse... |
github | kunaljathal/Pitch-Estimation-master | FundamentalFreqComputation.m | .m | Pitch-Estimation-master/FundamentalFreqComputation.m | 7,843 | utf_8 | c953678fa1029735ef8d825d8913d3ae | % Kunal Jathal
%
% Fundamental Frequency Computation
% =================================
%% USAGE
% FundamentalFreqComputation(input, method)
% input - input signal (egs: 'bass_clarinet_fhorn.wav')
% method - Method by which you want to calculate the fundamental frequency
% 'zcr' - Zero Crossing
% ... |
github | lonl/CDBN-master | setup_toolbox.m | .m | CDBN-master/setup_toolbox.m | 758 | utf_8 | 9f10f4553ad94c24f12359ce15615da7 | function setup_toolbox()
% get toolbox root path
[a, b, c] = fileparts(mfilename('fullpath'));
p_root = a;
% add following directories
add_path( p_root );
add_path( fullfile(p_root, 'toolbox') );
add_path( fullfile(p_root, 'toolbox/DBNLIB') );
add_path( fullfile(p_root, 'toolbox/CDBNLIB') );
add_path( fullfile(p_roo... |
github | lonl/CDBN-master | preprocess_train_data2D.m | .m | CDBN-master/toolbox/CDBNLIB/preprocess_train_data2D.m | 1,315 | utf_8 | 049191052f9979dfd3b02bf0a4ab7395 | function [layer] = preprocess_train_data2D(layer)
%%
% Here you should preprocess your code for pooling layer
mod_1 = mod((size(layer.inputdata,1)-layer.s_filter(1))/layer.stride(1)+1,layer.s_pool(1));
if mod_1~=0
layer.inputdata(1:floor(mod_1/2),:,:,:) =[];
layer.inputdata(end-ceil(mod_1/2)+1:end,:,:,:) ... |
github | lonl/CDBN-master | softmaxExercise.m | .m | CDBN-master/toolbox/Softmax/softmaxExercise.m | 4,862 | utf_8 | 89117e520081eee582e48052c26a4c7d | %% CS294A/CS294W Softmax Exercise
function softmaxExercise(inputData,labels,inputData_t,labels_t)
% Instructions
% ------------
%
% This file contains code that helps you get started on the
% softmax exercise. You will need to write the softmax cost function
% in softmaxCost.m and the softmax prediction functio... |
github | lonl/CDBN-master | WolfeLineSearch.m | .m | CDBN-master/toolbox/Softmax/minFunc/WolfeLineSearch.m | 11,106 | utf_8 | f97d9ca0bf8aab87df9aa65e74f98589 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at starting lo... |
github | lonl/CDBN-master | minFunc_processInputOptions.m | .m | CDBN-master/toolbox/Softmax/minFunc/minFunc_processInputOptions.m | 3,551 | utf_8 | ea7fbcf303b9cafeca4045921adad934 |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCu... |
github | lonl/CDBN-master | process_options.m | .m | CDBN-master/toolbox/DBNLIB/process_options.m | 3,819 | utf_8 | 66887446ed8232aa6191418602f9ade5 | %% Process named arguments to a function
%
% This allows you to pass in arguments using name, value pairs
% eg func(x, y, 'u', 0, 'v', 1)
% Or you can pass in a struct with named fields
% eg S.u = 0; S.v = 1; func(x, y, S)
%
% Usage: [var1, var2, ..., varn[, unused]] = ...
% process_options(args, ...
% ... |
github | PolaviejaLab/Victoria-Analysis-master | CollectLikert.m | .m | Victoria-Analysis-master/scripts/CollectLikert.m | 16,195 | utf_8 | 0e5f2636d326823878f782a606324b23 | function [LikertLineScores] = CollectLikert(WindowSpecs, WindowTitle, InstructionsText, InstructionsHeight, ...
ScaleLabels, ScaleLabelsHeight, ScaleSmallestValue, ...
Questions, QuestionWidth, QuestionHeights, RequireAllAnswers, MissingValue)
% CollectLikert: Collect responses to a Likert-type questionnaire.
... |
github | PolaviejaLab/Victoria-Analysis-master | getResponseOrder.m | .m | Victoria-Analysis-master/scripts/getData/getResponseOrder.m | 578 | utf_8 | cb636d691d263b3d2e7e6988d532913e | % GETORDERRESPONSES Takes the questionnaire responses
function [vOrder] = getResponseOrder (protocolFile)
NoiseLevel = table2array(protocolFile(:, 5))';
KnifeOffset = table2array(protocolFile(:, 6))';
vOrder = zeros(1, 3);
for i_order = 1:3
if(KnifeOffset(i_order) == 0)
if (NoiseLevel(i_order) == 0)
... |
github | PolaviejaLab/Victoria-Analysis-master | datenum8601.m | .m | Victoria-Analysis-master/toolbox/datenum8601/datenum8601.m | 9,832 | utf_8 | 225a486e47cf6b1fe0698e242524b789 | function [DtN,Spl,TkC] = datenum8601(Str,Tok)
% Convert an ISO 8601 formatted Date String (timestamp) to a Serial Date Number.
%
% (c) 2015 Stephen Cobeldick
%
% ### Function ###
%
% Syntax:
% DtN = datenum8601(Str)
% DtN = datenum8601(Str,Tok)
% [DtN,Spl,TkC] = datenum8601(...)
%
% By default the function automatic... |
github | PolaviejaLab/Victoria-Analysis-master | plotcorrelations.m | .m | Victoria-Analysis-master/script_cementery/plotcorrelations.m | 268 | utf_8 | 35092aac6eb6963b5074cef2888c7b6b | %% ownership no offset/ownership offset
function [] = plotcorrelations (vector1, vector2)
scatter (vector1, vector2);
line([9 0], [9 0], 'color', [0.827451 0.827451 0.827451]);
xlim([0.5, 7.5]);
set(gca, 'XTick', 1:7);
ylim([0.5, 7.5]);
set(gca, 'YTick', 1:7);
end |
github | QianMo/opencv_contrib-master | modelConvert.m | .m | opencv_contrib-master/doc/tutorials/ximgproc/training/scripts/modelConvert.m | 2,117 | utf_8 | dd8b0dc376b1da49ced7529a9e3a7723 | function modelConvert(model, outname)
%% script for converting Piotr's matlab model into YAML format
outfile = fopen(outname, 'w');
fprintf(outfile, '%%YAML:1.0\n\n');
fprintf(outfile, ['options:\n'...
' numberOfTrees: 8\n'...
' numberOfTreesToEvaluate: 4\n'...
... |
github | QianMo/opencv_contrib-master | modelConvert.m | .m | opencv_contrib-master/modules/ximgproc/tutorials/scripts/modelConvert.m | 2,117 | utf_8 | dd8b0dc376b1da49ced7529a9e3a7723 | function modelConvert(model, outname)
%% script for converting Piotr's matlab model into YAML format
outfile = fopen(outname, 'w');
fprintf(outfile, '%%YAML:1.0\n\n');
fprintf(outfile, ['options:\n'...
' numberOfTrees: 8\n'...
' numberOfTreesToEvaluate: 4\n'...
... |
github | hlkcrcck/Optic-Flow-Horn-Schunck-master | computeColor.m | .m | Optic-Flow-Horn-Schunck-master/MATLAB/WEBCAM/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 | hlkcrcck/Optic-Flow-Horn-Schunck-master | computeColor.m | .m | Optic-Flow-Horn-Schunck-master/MATLAB/VİDEO/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 | rteammco/visual-similarity-search-master | hist_search.m | .m | visual-similarity-search-master/hist_search.m | 2,610 | utf_8 | 2be051941e2b5c1d31df11b9c97025bd | function [ ] = hist_search( ref_img, dest_path )
%SEARCH Search for the best matching image using RGB histogram similarity.
% TODO - use get_histograms to load all files and compute histograms
% first, then search in-memory for the top N similar images.
% Read in the reference image.
ref = imread(ref_img);... |
github | hechengjin/ExMail-master | apmtest.m | .m | ExMail-master/mozilla/media/webrtc/trunk/src/modules/audio_processing/test/apmtest.m | 9,470 | utf_8 | ad72111888b4bb4b7c4605d0bf79d572 | function apmtest(task, testname, filepath, casenumber, legacy)
%APMTEST is a tool to process APM file sets and easily display the output.
% APMTEST(TASK, TESTNAME, CASENUMBER) performs one of several TASKs:
% 'test' Processes the files to produce test output.
% 'list' Prints a list of cases in the test set,... |
github | hechengjin/ExMail-master | plot_neteq_delay.m | .m | ExMail-master/mozilla/media/webrtc/trunk/src/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,563 | utf_8 | 8b6a66813477863da513b1e6971dbc97 | function [delay_struct, delayvalues] = plot_neteq_delay(delayfile, varargin)
% InfoStruct = plot_neteq_delay(delayfile)
% InfoStruct = plot_neteq_delay(delayfile, 'skipdelay', skip_seconds)
%
% Henrik Lundin, 2006-11-17
% Henrik Lundin, 2011-05-17
%
try
s = parse_delay_file(delayfile);
catch
error(lasterr);
e... |
github | hechengjin/ExMail-master | exportfig.m | .m | ExMail-master/mozilla/media/webrtc/trunk/src/modules/video_coding/codecs/test_framework/exportfig.m | 14,995 | utf_8 | d7427be6e56c37d4aec2f2c91c9a6341 | function exportfig(varargin)
%EXPORTFIG Export a figure to Encapsulated Postscript.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies
% parameters th... |
github | hechengjin/ExMail-master | plotBenchmark.m | .m | ExMail-master/mozilla/media/webrtc/trunk/src/modules/video_coding/codecs/test_framework/plotBenchmark.m | 11,672 | utf_8 | a80ed712ca3895c1e7b6383d4cc07d38 | function plotBenchmark(fileNames, export)
%PLOTBENCHMARK Plots and exports video codec benchmarking results.
% PLOTBENCHMARK(FILENAMES, EXPORT) parses the video codec benchmarking result
% files given by the cell array of strings FILENAME. It plots the results and
% optionally exports each plot to an appropriatel... |
github | puarun/AVproject-master | extractMASKnfeatures.m | .m | AVproject-master/extractMASKnfeatures.m | 10,871 | utf_8 | 840893d1b2cd952e7e343ded39d5e233 | function [FeatureArray1,MASK,Tw,Ts,fs,c,yn,y,yclean1]=extractMASKnfeatures(Noise,SpeechNoisy,SNR,vFeatures,M,aSt,aE,fps)
%This script will generate features from the sound file.
% In addition it also calls some functions written by Kamil Wojcicki, 2011.
% Author: Arun.P.U. % November 7 2013
%clear all; close ... |
github | puarun/AVproject-master | SSBoll79.m | .m | AVproject-master/SSBoll79.m | 6,436 | utf_8 | 0b42462bc9bda6cf44ef7884271bf2d6 | function output=SSBoll79(signal,fs,IS)
% OUTPUT=SSBOLL79(S,FS,IS)
% Spectral Subtraction based on Boll 79. Amplitude spectral subtraction
% Includes Magnitude Averaging and Residual noise Reduction
% S is the noisy signal, FS is the sampling frequency and IS is the initial
% silence (noise only) length in secon... |
github | puarun/AVproject-master | GetFeaturesFromMarkers.m | .m | AVproject-master/GetFeaturesFromMarkers.m | 8,768 | utf_8 | 38e2eee4e4bcaa6980ee0fa8b1c0719e | function lipFeatures = GetFeaturesFromMarkers( filePath)
% [x, dx, d2x, features] = GetVideoFeaturesFromMarkers( filePath, subtractDrift, relativeTo)
% This function extracts video features that have been recorded by the
% Luxland API. It includes 66 video features positioned around the mouth,
% chin, eyes, and no... |
github | puarun/AVproject-master | designMelfilterbank.m | .m | AVproject-master/designMelfilterbank.m | 4,004 | utf_8 | c5b5987a8dbd69b34316e15b26321561 | function [b,c]=designMelfilterbank(M,fs)
%This function will desin a mel spaced FIR filter bank.
%The default sampling frequency is 22050.
%The number of filters, width of transition bands can be set.
%It uses the fir2 function to do filter design.
%Author: Arun.P.U.
%Date: October 31, 2013
if ~exist('M'... |
github | puarun/AVproject-master | makeFeatures.m | .m | AVproject-master/makeFeatures.m | 6,925 | utf_8 | 9591ce64b1c73f4e88cfa78e339c5bba | function makeFeatures(Snratio)
% This function will make features at the specified SNR (Snratio) and
% noise type (white noise by default). The path to save the files is specified at the end of the function.
% The .mat file saved will have two variables TrainFeat and TestFeat. The
% cutting point is specified by p... |
github | puarun/AVproject-master | myspectrogram.m | .m | AVproject-master/myspectrogram.m | 15,122 | utf_8 | f68befd041723edb22e607819eb3ab56 | %______________________________________________________________________________________________________________________
%
% @file myspectrogram.m
% @date 17/09/2007
% @author Kamil Wojcicki
% @affiliation Signal Processing Laboratory, Griffith University, Nathan QLD4111, Australia
% @brief ... |
github | puarun/AVproject-master | testGMMBClassifier.m | .m | AVproject-master/GMMBayes/testGMMBClassifier.m | 5,851 | utf_8 | 5494502a7c1ac17ce4fe2794898b4517 | function C2=testGMMBClassifier(bayesS,SNR,meaA1,sigA1,filepath,vName,fps,k)
% This function will make use of the generated GMM-Bayes model and produce
% the output files. The input parameters are the following:
% bayesS: GMM-Bayes model
% SNR: signal to noise ratio
% meaA1: mean Array
% sigA1: standard devaitatio... |
github | puarun/AVproject-master | featureAnalysisGMMBayes.m | .m | AVproject-master/GMMBayes/featureAnalysisGMMBayes.m | 7,591 | utf_8 | f616db6eb779cd4d31fbf5200cfed968 | function [bayesS,cmat,meaA,sigA,postprobA] = featureAnalysisGMMBayes( TrainFeat,TrainMASK,TestFeat,TestMASK,feat1 )
% This function will make use of the GMMBayes toolbox to fit a model.
% We start off by adding a path to the toolbox
%loadOracleFeatures
addpath('gmmbayestb-vOriginal\');
meaA={};
sigA={};... |
github | puarun/AVproject-master | gmmb_weightprior.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_weightprior.m | 824 | utf_8 | a652d9dd9fcd0da31407449944e9668e | %GMMB_WEIGHTPRIOR Multiply PDF values with constant priors
%
% P = GMMB_WEIGHTPRIOR(pdfmat, bayesS)
%
% pdfmat = N x K matrix of PDF values at N points
% in K different PDFs (the output of gmmb_pdf)
% bayesS = the bayesS struct used to compute pdfmat,
% used fields: apriories
% ... |
github | puarun/AVproject-master | warning_wrap.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/warning_wrap.m | 439 | utf_8 | 4aa0f5e7934935bec99e5232ba880349 | % WARNING_WRAP() warning function wrapper
% to allow Matlab R13 style warning calls in Matlab R12
% $Name: $
% $Id: warning_wrap.m,v 1.1 2004/11/02 08:32:22 paalanen Exp $
% Pekka Paalanen, 2004
function [] = warning_wrap(varargin);
old_version = strcmp(version('-release'), '12');
if old_version
if nargin > 1
... |
github | puarun/AVproject-master | gmmb_demo01.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_demo01.m | 5,164 | utf_8 | c9d0c0fda5690d47b226f73dca70c825 | % GMMB_DEMO01 Demostrate GMMBayes mixture learning and data classification.
% This demo generates some Gaussian mixture distributed data,
% divides it into training and test set, runs Figueiredo-Jain
% algorithm on the training set and classifies the test set.
%
%
% References:
%
% Author(s):
% ... |
github | puarun/AVproject-master | gmmb_pdf.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_pdf.m | 915 | utf_8 | ed30b790138e13a3e3b8a6221d63a55e | %GMMB_PDF - (Complex range) multivariate Gaussian mixture model pdf
%
% p = gmmb_pdf(data, bayesS)
%
% data = N x D matrix
% bayesS = 1 x K struct array, the bayesS struct,
% fields used:
% mu = D x C matrix
% sigma = D x D x C matrix array
% weight = C x 1 vector
%
% p = N x K matrix
%
% D dimensions, N point... |
github | puarun/AVproject-master | gmmb_decide.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_decide.m | 680 | utf_8 | 5724bab6b308e8c6e1566f5572dba2e0 | %GMMB_DECIDE Make decisions; choose index of the max value.
%
% labels = GMMB_DECIDE(p_in)
%
% p_in = N x K matrix
% labels = N x 1 matrix of integers
%
% The labels will be index of the maximum value on each row,
% except if the max value is zero, index will also be zero.
%
% Author(s):
% Pekka Paal... |
github | puarun/AVproject-master | getargs.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/getargs.m | 961 | utf_8 | ac4d7afa967d058951e7c426dd46fd3e | %GETARGS parse variable argument list into a struct
%
% S = GETARGS(defaultS, varglist)
%
% varglist - a cell array of name, value pairs
% defaultS - struct containing the default values
%
% Example:
% function foo(par1, varargin);
% args = struct( 'param1', 0, 'param2', eye(3) );
% args = getargs( args, varargi... |
github | puarun/AVproject-master | gmmb_em_init_fcm1.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_em_init_fcm1.m | 1,107 | utf_8 | f0c6fa874e79200354d0e4aa4bb054cc | % GMMB_EM_INIT_FCM1
%
% initS = gmmb_em_init_fcm1(data, C, verbose)
%
% Create an initialization structure for EM,
% called from gmmb_em, see gmmb_em.
%
% Fuzzy C-means clustering means, uniform weight and covariance
% Requires the Fuzzy Logic Toolbox.
%
% Author(s):
% Pekka Paalanen <pekka.paalanen@lut.fi>
%
% Copy... |
github | puarun/AVproject-master | gmmb_cmeans.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_cmeans.m | 879 | utf_8 | be5822456cca29c243e8f88269cf18f8 | % GMMB_CMEANS simple c-means clustering
%
% T = CMEANS(data, nclust, count)
% [T, CLUST] = CMEANS(...)
%
% data input data, N x D matrix
% nclust number of clusters
% count number of iterations
%
% T output, data labels, 1 x N vector
% CL output, cluster centers, nclust x D matrix
%
% Author: Jarmo Il... |
github | puarun/AVproject-master | gmmb_em_init_cmeans1.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_em_init_cmeans1.m | 1,019 | utf_8 | 5358ce62f8702788294ad39e9f2a0bbc | % GMMB_EM_INIT_CMEANS1
%
% initS = gmmb_em_init_cmeans1(data, C)
%
% Create an initialization structure for EM,
% called from gmmb_em, see gmmb_em.
%
% C-means clustering means, uniform weight and covariance
%
% Author(s):
% Pekka Paalanen <pekka.paalanen@lut.fi>
%
% Copyright:
%
% Bayesian Classifier with Gaussia... |
github | puarun/AVproject-master | gmmb_em_init_cmeans2.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_em_init_cmeans2.m | 1,021 | utf_8 | 314fd05538eab3611ed1bd55c84a6afa | % GMMB_EM_INIT_CMEANS2
%
% initS = gmmb_em_init_cmeans1(data, C)
%
% Create an initialization structure for EM,
% called from gmmb_em, see gmmb_em.
%
% C-means clustering means, cluster weight and covariance
%
% Author(s):
% Pekka Paalanen <pekka.paalanen@lut.fi>
%
% Copyright:
%
% Bayesian Classifier with Gaussia... |
github | puarun/AVproject-master | gmmb_covfixer.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_covfixer.m | 2,853 | utf_8 | a01e2d16c259353889811d3e774ed6d5 | %GMMB_COVFIXER - force matrix to be a valid covariance matrix
%
% covmatrix = GMMB_COVFIXER(matrix)
% Matrix is forced (complex conjugate) symmetric,
% positive definite and its diagonal real valued.
%
% [covmatrix, loops] = GMMB_COVFIXER(...)
% loops - number of rounds the positive definite fixer had to run.
%
% ... |
github | puarun/AVproject-master | gmmb_fj.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_fj.m | 10,924 | utf_8 | c510adf337130b3e9232f621d099db7b | %GMMB_FJ - Figueiredo-Jain estimated GMM parameters
% Produces a bayesS struct without 'apriories'.
%
% Works with complex numbers directly.
%
% estimate = GMMB_FJ(data[, parameters])
% [estimate, stats] = GMMB_FJ(...)
%
% Parameters (default):
% maxloops maximum number of loops per a CEM run (500)
% Cmax the m... |
github | puarun/AVproject-master | gmmb_cmvnpdf.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_cmvnpdf.m | 1,723 | utf_8 | c974e831cba1d2598f656a3137fc2515 | %GMMB_CMVNPDF - Compute the value of Gaussian PDF (real or complex range)
%
% Y = GMMB_CMVNPDF(X, MU, SIGMA)
% Computes the D-dimensional (complex) Gaussian PDF with parameters
% MU and SIGMA in points X(i,:) -> Y(i), i=1..N.
% X: N x D matrix of row vectors
% MU: 1 x D vector
% SIGMA: D ... |
github | puarun/AVproject-master | gmmb_normalize.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_normalize.m | 665 | utf_8 | 87652ff33f3f68f1fb90af6ba4862d6f | %GMMB_NORMALIZE Normalize a matrix so that row sums are one.
%
% p_out = GMMB_NORMALIZE(p_in)
%
% p_in = N x K matrix
% p_out = N x K matrix
%
% If an unnormalized row sum would be zero,
% the row is left untouched.
%
% Author(s):
% Pekka Paalanen <pekka.paalanen@lut.fi>
%
% Copyright:
%
% Bayesian... |
github | puarun/AVproject-master | gmmb_create.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_create.m | 2,072 | utf_8 | 3c41b4ffdb0147fb508e95ea1aa77ed8 | %GMMB_CREATE - Construct new Bayesian classifier with Gaussian mixture model pdf
%
% S = GMMB_CREATE(data, class, method [, parameters]) Generates a
% Bayesian classifier for one or several classes having GMM
% distribution with estimated mean values, variances and
% apriories. Classifier is returned in... |
github | puarun/AVproject-master | gmmb_classify.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_classify.m | 3,750 | utf_8 | 22f3a634c6e7868ff8dad0901146738e | %GMMB_CLASSIFY Classify data using Bayesian or Mahalanobis distance classifier.
%
% T = GMMB_CLASSIFY(S, data, ...) Classifies D dimensional data (N points)
% using Gaussian Mixture Model
% Bayesian classifier in struct S into K classes.
% S is a bayesS struct, see readme.txt.
%
% See also GMMB_CREA... |
github | puarun/AVproject-master | gmmb_em.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_em.m | 6,712 | utf_8 | 3b0a389ca9bcb2defbb120f57143dfea | %GMMB_EM - EM estimated GMM parameters
%
% estS = gmmb_em(data)
% estS = gmmb_em(data, <params>...)
% [estS, stats] = gmmb_em(...)
%
% This version works with complex numbers too.
%
% data = N x D matrix
% params can be a list of 'name', value -pairs.
% stats is a matrix, row (cov fixes, loops, final log-likelihood... |
github | puarun/AVproject-master | gmmb_mkcplx.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_mkcplx.m | 853 | utf_8 | fd2db06c8cfe585b0b4354fb24caeca1 | % data = GMMB_MKCPLX(mu, sigma, N) Generate complex Gaussian data
%
% Generates N points of complex valued data according to
% complex Gaussian distribution with parameters (mu, sigma).
%
% data = N x D list of vectors (complex)
% mu = 1 x D vector (complex)
% sigma = D x D matrix (complex, positive semi-defin... |
github | puarun/AVproject-master | gmmb_gem.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_gem.m | 2,803 | utf_8 | 1b5f99a5473c52b4cb47b4f9fa3f21e4 | %GMMB_GEM - Greedy EM estimated GMM parameters
% Produces a bayesS struct without 'apriories'
% This is just a wrapper for the Vlassis Greedy EM algorithm implementation.
%
% estimate = GMMB_GEM(data[, parameters])
% [estimate,stats] = GMMB_GEM(...)
%
% Parameters (default):
% verbose print some progress numbers (... |
github | puarun/AVproject-master | gmmbvl_kmeans.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmbvl_kmeans.m | 4,057 | utf_8 | a371853ae59987d2a0f8e35bbd1097b3 | function [Er,M,nb] = gmmbvl_kmeans(X,T,kmax,dyn,bs, killing, pl)
% gmmbvl_kmeans - clustering with k-means (or Generalized Lloyd or LBG) algorithm
%
% [Er,M,nb] = gmmbvl_kmeans(X,T,kmax,dyn,dnb,killing,p)
%
% X - (n x d) d-dimensional input data
% T - (? x d) d-dimensional test data
% kmax - (maximal) numb... |
github | puarun/AVproject-master | gmmb_version.m | .m | AVproject-master/GMMBayes/gmmbayestb-vOriginal/gmmb_version.m | 204 | utf_8 | 70b307a0c5585ed6d7f883e5032010f1 | %GMMB_VERSION - Version string for GMMBayes toolbox
%
% version = gmmb_version();
%
% $Id: gmmb_version.m,v 1.2 2004/11/02 09:11:15 paalanen Exp $
function version = gmmb_version();
version = 'base3';
|
github | puarun/AVproject-master | checkPerformance.m | .m | AVproject-master/LinearRegression/checkPerformance.m | 18,056 | utf_8 | 406d2432cb1a9a706c89a1ada4ac1dcf | function [X1,Y1,AUC1,Beta,tArray,Parray,meaA,sigA,T1,coeffA,yHat]=checkPerformance(featset,TrainFeat,TestFeat,TrainMASK,TestMASK)
% This function will fit a linear model and prune the features
% if there is a bad fit according to the criteria p<0.05 one at a time.
% Pruning means zeroing out that feature by assigni... |
github | puarun/AVproject-master | testPerformance.m | .m | AVproject-master/LinearRegression/testPerformance.m | 6,493 | utf_8 | 6e90355610e9d100e1d01a313b2aa4a2 | function C2=testPerformance(featset,BeatArray,tArray,SNR,meaA1,sigA1,filepath,coeff,doPCA,MASKa,MASKv,vName,fps)
% featset='video';
% Beta=B45_nostd;
%Flags
if ~exist('writeFile','var');writeFile=1;end
if ~exist('doPCA','var');doPCA=1;nfu=15;end
rocCurve=0;
visualizeSeparation=0;
... |
github | puarun/AVproject-master | myspectrogram.m | .m | AVproject-master/LinearRegression/myspectrogram.m | 15,122 | utf_8 | f68befd041723edb22e607819eb3ab56 | %______________________________________________________________________________________________________________________
%
% @file myspectrogram.m
% @date 17/09/2007
% @author Kamil Wojcicki
% @affiliation Signal Processing Laboratory, Griffith University, Nathan QLD4111, Australia
% @brief ... |
github | puarun/AVproject-master | speechTest.m | .m | AVproject-master/TestGUI/speechTest.m | 24,828 | utf_8 | 20f5f673feb9a189763b5d542fdb41a8 | function varargout = speechTest(varargin)
% SPEECHTEST MATLAB code for speechTest.fig
% SPEECHTEST, by itself, creates a new SPEECHTEST or raises the existing
% singleton*.
%
% H = SPEECHTEST returns the handle to a new SPEECHTEST or the handle to
% the existing singleton*.
%
% SPEECHTEST('CALL... |
github | vishal16babu/itsp2015-master | interface3.m | .m | itsp2015-master/interface3.m | 3,824 | utf_8 | fe84d6f61418c70a26e343e5c779817c | function varargout = interface3(varargin)
% INTERFACE3 MATLAB code for interface3.fig
% INTERFACE3, by itself, creates a new INTERFACE3 or raises the existing
% singleton*.
%
% H = INTERFACE3 returns the handle to a new INTERFACE3 or the handle to
% the existing singleton*.
%
% INTERFACE3('CALL... |
github | vishal16babu/itsp2015-master | interface_manual.m | .m | itsp2015-master/interface_manual.m | 5,785 | utf_8 | e4a3fa339292cfc9995fc048333f6122 | function varargout = interface_manual(varargin)
% INTERFACE_MANUAL MATLAB code for interface_manual.fig
% INTERFACE_MANUAL, by itself, creates a new INTERFACE_MANUAL or raises the existing
% singleton*.
%
% H = INTERFACE_MANUAL returns the handle to a new INTERFACE_MANUAL or the handle to
% the exis... |
github | vishal16babu/itsp2015-master | interface2.m | .m | itsp2015-master/interface2.m | 4,062 | utf_8 | e15c6c5498d2ebd8a8c0202a95eec0d6 | function varargout = interface2(varargin)
% INTERFACE2 MATLAB code for interface2.fig
% INTERFACE2, by itself, creates a new INTERFACE2 or raises the existing
% singleton*.
%
% H = INTERFACE2 returns the handle to a new INTERFACE2 or the handle to
% the existing singleton*.
%
% INTERFACE2('CALL... |
github | vishal16babu/itsp2015-master | interface.m | .m | itsp2015-master/interface.m | 5,516 | utf_8 | 809f02ae97a9f6e4a0bdb65f9e59352e | function varargout = interface(varargin)
% INTERFACE MATLAB code for interface.fig
% INTERFACE, by itself, creates a new INTERFACE or raises the existing
% singleton*.
%
% H = INTERFACE returns the handle to a new INTERFACE or the handle to
% the existing singleton*.
%
% INTERFACE('CALLBACK',hO... |
github | vishal16babu/itsp2015-master | timeout.m | .m | itsp2015-master/timeout.m | 4,336 | utf_8 | ecbef9b8bc083839ed1ca082d769e435 | function when=timeout(song,FS)
% written by Nick Berndsen
% last updated 12-10-06
% song is the sound vector
% FS is the sampling frequency
%--------------------- define things -------------------------------------%
P=5200/44100*FS; % length of filter (5200 recommended, I think...)
N=length(song); ... |
github | vishal16babu/itsp2015-master | interface_tuning.m | .m | itsp2015-master/interface_tuning.m | 4,090 | utf_8 | e6615e619d0b95a4671c1ce5128e3a49 | function varargout = interface_tuning(varargin)
% INTERFACE_TUNING MATLAB code for interface_tuning.fig
% INTERFACE_TUNING, by itself, creates a new INTERFACE_TUNING or raises the existing
% singleton*.
%
% H = INTERFACE_TUNING returns the handle to a new INTERFACE_TUNING or the handle to
% the exis... |
github | vishal16babu/itsp2015-master | interface1.m | .m | itsp2015-master/interface1.m | 3,817 | utf_8 | a26c4b4dd51280040c6d290e6679d275 | function varargout = interface1(varargin)
% INTERFACE1 MATLAB code for interface1.fig
% INTERFACE1, by itself, creates a new INTERFACE1 or raises the existing
% singleton*.
%
% H = INTERFACE1 returns the handle to a new INTERFACE1 or the handle to
% the existing singleton*.
%
% INTERFACE1('CALL... |
github | parloma/parloma_hand-master | plot_Plane.m | .m | parloma_hand-master/wrist_matlab/IK/plot_Plane.m | 1,141 | utf_8 | cc99801b9e4c5893e26859e8212a18be | %% Copyright (C) 2015 Politecnico di Torino
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
% This program is distribute... |
github | parloma/parloma_hand-master | IKPenguinWrist.m | .m | parloma_hand-master/wrist_matlab/IK/IKPenguinWrist.m | 11,746 | utf_8 | 9fda55061d71c94725937d392e711a22 | %% Copyright (C) 2015 Politecnico di Torino
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
% This program is distribute... |
github | parloma/parloma_hand-master | IKPenguinWristwithRPYoutput.m | .m | parloma_hand-master/wrist_matlab/FK/IKPenguinWristwithRPYoutput.m | 11,895 | utf_8 | 58c89a353ea553d87c94e551f9b2a4d3 | %% Copyright (C) 2015 Politecnico di Torino
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
% This program is distribute... |
github | parloma/parloma_hand-master | FKPenguinEquations.m | .m | parloma_hand-master/wrist_matlab/FK/FKPenguinEquations.m | 2,326 | utf_8 | e479fe0eb8cd38cb9119e22974de9406 | %% Copyright (C) 2015 Politecnico di Torino
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
% This program is distribute... |
github | parloma/parloma_hand-master | qplot.m | .m | parloma_hand-master/wrist_matlab/external/KinematicsLibrary/qplot.m | 658 | utf_8 | 5be0920a3bf4eac34fb82870d985c589 | %PLOT plot a quaternion object as a rotated coordinate frame
% Copright (C) Peter Corke 1999
function qplot(Q)
axis([-1 1 -1 1 -1 1])
R=quat2rot(Q);
x1 = R(:,1);
y1 = R(:,2);
z1 = R(:,3);
hold on
plot3([0;1], [0;0], [0;0] ,'r')
plot3([0;0], [0;1], [0;0] ,'r')
plot3([0;0], [0;0], [0;1] ,'r')
text(1,... |
github | victorgan/sun2014quantitative-master | denoise_robust2.m | .m | sun2014quantitative-master/ijcv_flow_code/utils/denoise_robust2.m | 3,458 | utf_8 | df87e1aa42b9d1cf97b545f04334f531 | function I = denoise_robust2(In, lambda, method, num_iter)
% function I = denoise_robust(In, lambda, gt)
%
% Denoise with a robust function by gradient descent
% (I-In)^2 + lambda*rho(f*I), where f is filter
%
% Author: Deqing Sun, Department of Computer Science, Brown University
% Contact: dqsun@cs.brown.edu
% $... |
github | victorgan/sun2014quantitative-master | computeColor.m | .m | sun2014quantitative-master/ijcv_flow_code/utils/flowColorCode/computeColor.m | 3,178 | utf_8 | 399f4a33a2991cc15b437bc679aa251d | 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 | leonid-pishchulin/humanshape-master | showmodel.m | .m | humanshape-master/shapemodel/showmodel.m | 2,097 | utf_8 | c37ff1de4f5efff759cd61ac623f18a5 | %
% showmodel(vertices, faces, color, [facelist], [surface], [colors])
%
% faces - for every face coordinates of the corners as column vectors
% [v1 v2 v3]
%
function vertices = showmodel(vertices, faces, color, facelist, surface, colors)
if ~exist('surface','var')
surface = 2;
end
if ~exist(... |
github | leonid-pishchulin/humanshape-master | checkAngle.m | .m | humanshape-master/fitting/checkAngle.m | 1,265 | utf_8 | 01e35892b26b9eef30efe81497da2054 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | readLandmarks.m | .m | humanshape-master/fitting/readLandmarks.m | 2,240 | utf_8 | 1b351ecba605ff7c60018473c06734a2 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | fitPoseShape.m | .m | humanshape-master/fitting/fitPoseShape.m | 5,804 | utf_8 | b354c1eba2aa815ef69219e29bd83b9f | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | getOptionsOptimizer.m | .m | humanshape-master/fitting/getOptionsOptimizer.m | 1,916 | utf_8 | 2a504188eee71acb352fc68c55233e99 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | printpdf.m | .m | humanshape-master/fitting/printpdf.m | 973 | utf_8 | 7447cd485bb63b8a8395994ed8920a50 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | NRDgradientFunction.m | .m | humanshape-master/fitting/NRDgradientFunction.m | 2,957 | utf_8 | f91af412338193c466ba1e93904c32cb | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | getWeightReduceFactor.m | .m | humanshape-master/fitting/getWeightReduceFactor.m | 1,476 | utf_8 | 0e1d70ed1c998efafd5279a4e0791e6a | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | getVertexNN.m | .m | humanshape-master/fitting/getVertexNN.m | 1,202 | utf_8 | 0c7a54bea878d216f9adf4511132d553 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | getNormals.m | .m | humanshape-master/fitting/getNormals.m | 1,093 | utf_8 | e444949ca34bbc0010a7cccfc61043c1 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | m2mm.m | .m | humanshape-master/fitting/m2mm.m | 880 | utf_8 | e0316a70b3a2683a949663909553fbb9 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | visModelVP.m | .m | humanshape-master/fitting/visModelVP.m | 1,925 | utf_8 | d77b67547991291f5d3a69ecaaf61935 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | eval_get_color.m | .m | humanshape-master/fitting/eval_get_color.m | 1,465 | utf_8 | f9f1f03a88313ef7ef9f736222f8e42f | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | NRDFunction.m | .m | humanshape-master/fitting/NRDFunction.m | 2,832 | utf_8 | d84b7cccf2c71746a8146276aee38e92 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | fitPoseShapeJoint.m | .m | humanshape-master/fitting/fitPoseShapeJoint.m | 7,810 | utf_8 | 57753f29d1c9b8415f5504352cb51c20 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | getHeadTopIdx.m | .m | humanshape-master/fitting/getHeadTopIdx.m | 1,488 | utf_8 | 073ff8612e6213b57a1235c96e483228 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | getNormals1Face.m | .m | humanshape-master/fitting/getNormals1Face.m | 1,181 | utf_8 | a8a6c8d44b0db927734f4b8abb7a14bb | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | read_ply.m | .m | humanshape-master/fitting/read_ply.m | 16,850 | utf_8 | 50f05d3c50f987546d3d857bb505e3b4 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | getNNheadAndBody.m | .m | humanshape-master/fitting/getNNheadAndBody.m | 2,716 | utf_8 | ff296d5cd235d248ae231a2cacf009eb | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | fitMesh.m | .m | humanshape-master/fitting/fitMesh.m | 3,283 | utf_8 | d0512303cf1ecf7a4b7f8ac175cdffda | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | visFit.m | .m | humanshape-master/fitting/visFit.m | 1,854 | utf_8 | 6cd07af1ea48b2c1c423feee49a0a0fb | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | getVertexFaces.m | .m | humanshape-master/fitting/getVertexFaces.m | 1,015 | utf_8 | b89d27c6923519bcf1b250470fc59d16 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | register.m | .m | humanshape-master/fitting/register.m | 1,788 | utf_8 | 3b8f3a4bc17100f65f0bcc7afa699e3a | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
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