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github | BhanuVerma/ComputerVision-master | collect_ground_truth_corr.m | .m | ComputerVision-master/proj2/code/collect_ground_truth_corr.m | 2,009 | utf_8 | f59689693ec3d8d895fededdbc1efe39 | % Local Feature Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by James Hays
function collect_ground_truth_corr()
%An interactive method to specify and then save many point correspondences
%between two photographs, which will be used to generate a projective
%transformation. Run this bef... |
github | BhanuVerma/ComputerVision-master | evaluate_correspondence.m | .m | ComputerVision-master/proj2/code/evaluate_correspondence.m | 3,952 | utf_8 | 35e20889ed67de05a8a5706b3d1fcada | % Local Feature Stencil Code
% Computater Vision
% Written by Henry Hu <henryhu@gatech.edu> and James Hays
% You do not need to modify anything in this function, although you can if
% you want to.
function evaluate_correspondence(imgA, imgB, ground_truth_correspondence_file, scale_factor, x1_est, y1_est, x2_est,... |
github | BhanuVerma/ComputerVision-master | get_bags_of_sifts.m | .m | ComputerVision-master/proj4/code/get_bags_of_sifts.m | 3,406 | utf_8 | 3e4ed6483d00a2b94787728d10ecb530 | % Starter code prepared by James Hays for Computer Vision
%This feature representation is described in the handout, lecture
%materials, and Szeliski chapter 14.
function image_feats = get_bags_of_sifts(image_paths)
% image_paths is an N x 1 cell array of strings where each string is an
% image path on the file... |
github | BhanuVerma/ComputerVision-master | get_image_paths.m | .m | ComputerVision-master/proj4/code/get_image_paths.m | 1,758 | utf_8 | 6558fc62100de00c09423ae495390fac | % Starter code prepared by James Hays for Computer Vision
%This function returns cell arrays containing the file path for each train
%and test image, as well as cell arrays with the label of each train and
%test image. By default all four of these arrays will be 1500x1 where each
%entry is a char array (or string... |
github | BhanuVerma/ComputerVision-master | nearest_neighbor_classify.m | .m | ComputerVision-master/proj4/code/nearest_neighbor_classify.m | 2,210 | utf_8 | 16db3951d4c497fce63b41ee0ef0eee0 | % Starter code prepared by James Hays for Computer Vision
%This function will predict the category for every test image by finding
%the training image with most similar features. Instead of 1 nearest
%neighbor, you can vote based on k nearest neighbors which will increase
%performance (although you need to pick a... |
github | BhanuVerma/ComputerVision-master | get_tiny_images.m | .m | ComputerVision-master/proj4/code/get_tiny_images.m | 1,563 | utf_8 | 399d137dc25f777df19763bc0dd1f6ee | % Starter code prepared by James Hays for Computer Vision
%This feature is inspired by the simple tiny images used as features in
% 80 million tiny images: a large dataset for non-parametric object and
% scene recognition. A. Torralba, R. Fergus, W. T. Freeman. IEEE
% Transactions on Pattern Analysis and Mach... |
github | BhanuVerma/ComputerVision-master | create_results_webpage.m | .m | ComputerVision-master/proj4/code/create_results_webpage.m | 12,303 | utf_8 | 834ce81370a400ed31a615edb2667f6c | % Starter code prepared by James Hays for Computer Vision
% This function creates a webpage (html and images) visualizing the
% classiffication results. This webpage will contain
% (1) A confusion matrix plot
% (2) A table with one row per category, with 3 columns - training
% examples, true positives, false pos... |
github | BhanuVerma/ComputerVision-master | build_vocabulary.m | .m | ComputerVision-master/proj4/code/build_vocabulary.m | 3,043 | utf_8 | cec765e614795da0c93951dc4251518f | % Starter code prepared by James Hays for Computer Vision
%This function will sample SIFT descriptors from the training images,
%cluster them with kmeans, and then return the cluster centers.
function vocab = build_vocabulary( image_paths, vocab_size )
% The inputs are 'image_paths', a N x 1 cell array of image paths... |
github | BhanuVerma/ComputerVision-master | svm_classify.m | .m | ComputerVision-master/proj4/code/svm_classify.m | 2,857 | utf_8 | cf518cb23df56a44bdcd2f9e3413dab6 | % Starter code prepared by James Hays for Computer Vision
%This function will train a linear SVM for every category (i.e. one vs all)
%and then use the learned linear classifiers to predict the category of
%every test image. Every test feature will be evaluated with all 15 SVMs
%and the most confident SVM will "w... |
github | BhanuVerma/ComputerVision-master | cnn_train.m | .m | ComputerVision-master/proj6/code/cnn_train.m | 14,634 | utf_8 | 5122f6154132e1d097535d6adbb68101 | function [net, info] = cnn_train(net, imdb, getBatch, varargin)
%CNN_TRAIN An example implementation of SGD for training CNNs
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providing a suitable
... |
github | BhanuVerma/ComputerVision-master | proj6_part1.m | .m | ComputerVision-master/proj6/code/proj6_part1.m | 4,178 | utf_8 | 46394bd6692f99c29881f0ad36a97761 | function [net, info] = proj6_part1()
%code for Computer Vision, Georgia Tech by James Hays
%based off the MNIST and CIFAR examples from MatConvNet
run(fullfile('..','/matconvnet-1.0-beta16', 'matlab', 'vl_setupnn.m')) ;
%It might actually be problematic to run vl_setup, because VLFeat has a
%version of vl_argp... |
github | BhanuVerma/ComputerVision-master | calculate_projection_matrix.m | .m | ComputerVision-master/proj3/code/calculate_projection_matrix.m | 2,526 | utf_8 | 7afb0851a4133a4fb908cb7457f1c166 | % Projection Matrix Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu, Grady Williams, James Hays
% Returns the projection matrix for a given set of corresponding 2D and
% 3D points.
% 'Points_2D' is nx2 matrix of 2D coordinate of points on the image
% 'Points_3D' is nx3 matr... |
github | BhanuVerma/ComputerVision-master | satya_compute_camera_center.m | .m | ComputerVision-master/proj3/code/satya_compute_camera_center.m | 644 | utf_8 | cb8d25aa6eee03e7ab99d29b0c09cfa4 | % Camera Center Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu, Grady Williams, James Hays
% Returns the camera center matrix for a given projection matrix
% 'M' is the 3x4 projection matrix
% 'Center' is the 1x3 matrix of camera center location in world coordinates
funct... |
github | BhanuVerma/ComputerVision-master | show_correspondence2.m | .m | ComputerVision-master/proj3/code/show_correspondence2.m | 1,692 | utf_8 | cd01aa6b463fdfdebeff4b1b32fd90af | % CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu <henryhu@gatech.edu> and James Hays
% Visualizes corresponding points between two images. Corresponding points
% will be matched by a line of random color.
% This function provides another method of visualization. You can use
% either this function... |
github | BhanuVerma/ComputerVision-master | satya_estimate_fundamental_matrix.m | .m | ComputerVision-master/proj3/code/satya_estimate_fundamental_matrix.m | 2,447 | utf_8 | 9cee6955e8e8442e26ccfbf43aa8ee89 | % Fundamental Matrix Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu
% Returns the camera center matrix for a given projection matrix
% 'Points_a' is nx2 matrix of 2D coordinate of points on Image A
% 'Points_b' is nx2 matrix of 2D coordinate of points on Image B
% 'F_matrix... |
github | BhanuVerma/ComputerVision-master | show_correspondence.m | .m | ComputerVision-master/proj3/code/show_correspondence.m | 1,464 | utf_8 | 076dfb7ad9b04f695b8600f3a2b027ae | % CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by James Hays
% Visualizes corresponding points between two images. Corresponding points
% will have the same random color.
% You do not need to modify anything in this function, although you can if
% you want to.
function [ h ] = show_correspondence(image1, i... |
github | BhanuVerma/ComputerVision-master | estimate_fundamental_matrix.m | .m | ComputerVision-master/proj3/code/estimate_fundamental_matrix.m | 2,263 | utf_8 | ede125016630de07aa3affa22fe6fcbd | % Fundamental Matrix Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu
% Returns the camera center matrix for a given projection matrix
% 'Points_a' is nx2 matrix of 2D coordinate of points on Image A
% 'Points_b' is nx2 matrix of 2D coordinate of points on Image B
% 'F_matrix... |
github | BhanuVerma/ComputerVision-master | visualize_points.m | .m | ComputerVision-master/proj3/code/visualize_points.m | 572 | utf_8 | da9c0f43cb832a65496f3a3e75247cd4 | % Plot Points Stencil Code
% CS 4495 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu and James Hays
% Visualize the actual 2D points and the projected 2D points calculated
% from the projection matrix
% You do not need to modify anything in this function, although you can if
% you want to.
func... |
github | BhanuVerma/ComputerVision-master | ransac_fundamental_matrix.m | .m | ComputerVision-master/proj3/code/ransac_fundamental_matrix.m | 2,353 | utf_8 | 48214147b2108c5bc6b7d991b00857a9 | % RANSAC Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu
% Find the best fundamental matrix using RANSAC on potentially matching
% points
% 'matches_a' and 'matches_b' are the Nx2 coordinates of the possibly
% matching points from pic_a and pic_b. Each row is a correspondenc... |
github | BhanuVerma/ComputerVision-master | draw_epipolar_lines.m | .m | ComputerVision-master/proj3/code/draw_epipolar_lines.m | 1,345 | utf_8 | aa3d5b100624b23e48ed57312e1718eb | % Draw Epipolar Lines Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu
% Draw the epipolar lines given the fundamental matrix, left right images
% and left right datapoints
% You do not need to modify anything in this function, although you can if
% you want to.
function [... |
github | BhanuVerma/ComputerVision-master | satya_calculate_projection_matrix.m | .m | ComputerVision-master/proj3/code/satya_calculate_projection_matrix.m | 2,306 | utf_8 | 55b72112715a7094b9f393c1adf8ebf1 | % Projection Matrix Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu, Grady Williams, James Hays
% Returns the projection matrix for a given set of corresponding 2D and
% 3D points.
% 'Points_2D' is nx2 matrix of 2D coordinate of points on the image
% 'Points_3D' is nx3 matr... |
github | BhanuVerma/ComputerVision-master | satya_ransac_fundamental_matrix.m | .m | ComputerVision-master/proj3/code/satya_ransac_fundamental_matrix.m | 2,155 | utf_8 | cb44e0933e4a6369c440e27258251e1b | % RANSAC Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu
% Find the best fundamental matrix using RANSAC on potentially matching
% points
% 'matches_a' and 'matches_b' are the Nx2 coordinates of the possibly
% matching points from pic_a and pic_b. Each row is a correspondenc... |
github | BhanuVerma/ComputerVision-master | compute_camera_center.m | .m | ComputerVision-master/proj3/code/compute_camera_center.m | 670 | utf_8 | 4ac27b2ab2cd6c98c959d8eb1192b977 | % Camera Center Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu, Grady Williams, James Hays
% Returns the camera center matrix for a given projection matrix
% 'M' is the 3x4 projection matrix
% 'Center' is the 1x3 matrix of camera center location in world coordinates
funct... |
github | BhanuVerma/ComputerVision-master | plot3dview.m | .m | ComputerVision-master/proj3/code/plot3dview.m | 1,046 | utf_8 | 8ecd2eacaaea26f6277a58f70ac422bf | % Plot Points Stencil Code
% CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by James Hays
% Visualize the actual 3D points and the estimated 3D camera center.
% You do not need to modify anything in this function, although you can if
% you want to.
function plot3dview(Points_3D, camera_center1)
... |
github | BhanuVerma/ComputerVision-master | sift_wrapper.m | .m | ComputerVision-master/proj3/code/sift_wrapper.m | 1,421 | utf_8 | e0b05526fc48791f643a42079874cfb0 | % CS 4476 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu and James Hays
% This is a wrapper for VLFeat's sift functions. It removes duplicate
% points which would otherwise cause problems for RANSAC (because you would
% be likely to sample duplicate points and therefore your linear system
% would no... |
github | BhanuVerma/ComputerVision-master | evaluate_points.m | .m | ComputerVision-master/proj3/code/evaluate_points.m | 652 | utf_8 | 78659c2c660cf624d891888c4312f331 | % Plot Points Stencil Code
% CS 4495 / 6476: Computer Vision, Georgia Tech
% Written by Henry Hu
% Visualize the actual 2D points and the projected 2D points calculated
% from the projection matrix
% You do not need to modify anything in this function, although you can if
% you want to.
function [Projected... |
github | maximsch2/bhtsne-master | fast_tsne.m | .m | bhtsne-master/scripts/fast_tsne.m | 4,990 | utf_8 | 807aed14165f3377c0010489ac2a86a7 | function mappedX = fast_tsne(X, no_dims, initial_dims, perplexity, theta, alg)
%FAST_TSNE Runs the C++ implementation of Barnes-Hut t-SNE
%
% mappedX = fast_tsne(X, no_dims, initial_dims, perplexity, theta, alg)
%
% Runs the C++ implementation of Barnes-Hut-SNE. The high-dimensional
% datapoints are specified ... |
github | hocarm/MATLAB-Arduino-Tutorial-master | 5.Read_adc.m | .m | MATLAB-Arduino-Tutorial-master/5.Read_adc.m | 324 | utf_8 | f7db747107e0fa37f7a1e32df252afca | %Chuong trinh doc bien tro
function [] = potentiometer()
board = arduino();
finishup = onCleanup(@() exitprogram(board));
disp('press Ctrl-C to exit');
while 1
analog = readVoltage(board,'A0');
disp(['analog = ',num2str(analog)]);
pause(1);
end
end
function exitprogram(b)
clear b;
disp('program has exit');... |
github | hocarm/MATLAB-Arduino-Tutorial-master | 3.Pwm_led_basic.m | .m | MATLAB-Arduino-Tutorial-master/3.Pwm_led_basic.m | 479 | utf_8 | bcad8fc05f0d136a921904eb8948598a | %Chuong trinh PWM sang dan tat dan LED
function [] = led_brightness()
board = arduino('com9','uno');
finishup = onCleanup(@() exitprogram(board));
configurePin(board,'D3', 'PWM'); %su dung pin 3 vi co PWM
disp('press Ctr-C to exit');
while 1
for k = 0:5
writePWMVoltage(board, 'D3',k);
pause(1);
... |
github | hocarm/MATLAB-Arduino-Tutorial-master | 9.read_adc_plot.m | .m | MATLAB-Arduino-Tutorial-master/9.read_adc_plot.m | 606 | utf_8 | 597184c007e1d5a57752ae44f773481e | %Chuong trinh doc bien tro
%Su dung lai chuong trinh nay, sau do hien thi gia tri doc duoc len do thi
function [] = potentiometer()
board = arduino();
finishup = onCleanup(@() exitprogram(board));
disp('press Ctrl-C to exit');
hLine = line(nan, nan, 'Color', 'red');
i = 0;
while 1
analog = readVoltage(board,'A0');
... |
github | hocarm/MATLAB-Arduino-Tutorial-master | 4.pwm_led.m | .m | MATLAB-Arduino-Tutorial-master/4.pwm_led.m | 901 | utf_8 | 32fcf68fcc07df4d8ff1f62c4bec1644 | function [] = led_rgb()
board = arduino('com9','uno');
finishup =onCleanup(@() exitprogram(board));
%Cau hinh pin de PWM
%Luu y chon dung chan pwm la chan 3 5 6 9 10 11
configurePin(board, 'D3','PWM');
configurePin(board, 'D5','PWM');
configurePin(board, 'D6','PWM');
disp('press Ctrl-C to exit');
while 1
disp('yell... |
github | hocarm/MATLAB-Arduino-Tutorial-master | 8.servo_demo.m | .m | MATLAB-Arduino-Tutorial-master/8.servo_demo.m | 543 | utf_8 | 00aca039b27791812e8bd3a92f70da25 | %Chuong trinh giao tiep voi motor servo
function [] =servo_motor()
board = arduino ('com9','uno');
finishup = onCleanup(@() exitprogram(board));
motor = servo(board, 'D10');
disp('press Ctrl-C to exit');
while 1
for pos = 0:0.25:1
disp(['position: ', num2str(pos)]);
writePosition(motor,pos);
... |
github | hocarm/MATLAB-Arduino-Tutorial-master | 2.Led_pushbutton.m | .m | MATLAB-Arduino-Tutorial-master/2.Led_pushbutton.m | 478 | utf_8 | f1204cc4b32a38622ffa8d06855e237a | function [] = les_pushbutton()
pushbutton = 'D12'; %Chon pin 12 gan voi nut nhan
led = 'D13'; %su dung led co san tren board
board = arduino('com9','uno');
finishup = onCleanup(@() exitprogram(board));
configurePin(board, pushbutton, 'DigitalInput');
disp('press Ctr-C to exit');
while 1
state = readDigitalPin(board... |
github | hocarm/MATLAB-Arduino-Tutorial-master | 7.spi_demo.m | .m | MATLAB-Arduino-Tutorial-master/7.spi_demo.m | 486 | utf_8 | 5d2eb4fa3194fa78ee56d543797d5a01 | %Chuong trinh giao tiep SPI
function [] =spi_loopback()
board = arduino ('com9','uno');
finishup = onCleanup(@() exitprogram(board));
spi = spidev(board,10);
k = 3;
m = 10;
n = 30;
disp('press Ctrl-C to exit');
while 1
disp('datain: ');
dataIn = [k m n];
disp(dataIn);
dataOut = writeRead(spi,dataIn);
... |
github | LLNL/COGENT-master | smooth_eqdsk.m | .m | COGENT-master/COGENT/grid_generation/old_grid_generator_flux_aligned/utilities/smooth_eqdsk.m | 6,723 | utf_8 | b41e725c0bab3e9e52f6ef889c18daff | function smooth_eqdsk(input_file, s)
% If a negative value is passed for the smoothing parameter,
% the algorithm will try to select a value automatically
% Open the files
fd_in = fopen(input_file,'r');
output_file = strcat(input_file,'_smoothed');
fd_out = fopen(output_file,'w');
dct_file = strcat(i... |
github | LLNL/COGENT-master | smoothn_mod.m | .m | COGENT-master/COGENT/grid_generation/old_grid_generator_flux_aligned/utilities/smoothn_mod.m | 17,859 | utf_8 | 764c624ffd74ec2f9021696530b0b2a9 | function [z,GammaDCTy,s,exitflag,dz] = smoothn(varargin)
%SMOOTHN Fast smoothing of 1-D to N-D data.
% Z = SMOOTHN(Y) automatically smoothes the uniformly-sampled array Y. Y
% can be any N-D noisy array (time series, images, 3D data,...). Non
% finite data (NaN or Inf) are treated as missing values.
%
% ... |
github | LLNL/COGENT-master | smooth_eqdsk.m | .m | COGENT-master/COGENT/grid_generation/old_grid_generator_flux_aligned/smooth_eq_dsk_matlab/smooth_eqdsk.m | 6,723 | utf_8 | b41e725c0bab3e9e52f6ef889c18daff | function smooth_eqdsk(input_file, s)
% If a negative value is passed for the smoothing parameter,
% the algorithm will try to select a value automatically
% Open the files
fd_in = fopen(input_file,'r');
output_file = strcat(input_file,'_smoothed');
fd_out = fopen(output_file,'w');
dct_file = strcat(i... |
github | LLNL/COGENT-master | smoothn_mod.m | .m | COGENT-master/COGENT/grid_generation/old_grid_generator_flux_aligned/smooth_eq_dsk_matlab/smoothn_mod.m | 17,859 | utf_8 | 764c624ffd74ec2f9021696530b0b2a9 | function [z,GammaDCTy,s,exitflag,dz] = smoothn(varargin)
%SMOOTHN Fast smoothing of 1-D to N-D data.
% Z = SMOOTHN(Y) automatically smoothes the uniformly-sampled array Y. Y
% can be any N-D noisy array (time series, images, 3D data,...). Non
% finite data (NaN or Inf) are treated as missing values.
%
% ... |
github | LLNL/COGENT-master | rbfcreate.m | .m | COGENT-master/COGENT/grid_generation/bs_grid_generator/add_ghosts/rbfcreate.m | 5,437 | utf_8 | ec3418b4be7fd5f8a3f8733600d42d19 | function options = rbfcreate(x, y, varargin)
%RBFCREATE Creates an RBF interpolation
% OPTIONS = RBFSET(X, Y, 'NAME1',VALUE1,'NAME2',VALUE2,...) creates an
% radial base function interpolation
%
% RBFCREATE with no input arguments displays all property names and their
% possible values.
%
%RBFCREATE P... |
github | LLNL/COGENT-master | plot_mapping_file.m | .m | COGENT-master/COGENT/grid_generation/bs_grid_generator/add_ghosts/plot_mapping_file.m | 10,106 | utf_8 | 1ed75bb82bdc599d5cb1bf9ccb980319 | function plot_mapping_file(file_name)
fd=fopen(file_name,'r');
% Read LCORE
bname = fscanf(fd,'%s',1);
[nr_lcore, nr_lcore_extend, np_lcore, np_lcore_extend]=deal_array(fscanf(fd,'%d',4));
[R_lcore,Z_lcore] = read_block(fd, nr_lcore, nr_lcore_extend, np_lcore, np_lcore_extend);
fprintf('Block %s: n... |
github | LLNL/COGENT-master | add_block_ghosts.m | .m | COGENT-master/COGENT/grid_generation/bs_grid_generator/add_ghosts/add_block_ghosts.m | 7,023 | utf_8 | 9143c95258ac77ad028cface74788f27 | function [interp_R, interp_Z, nr, nr_extend, np, np_extend] = add_block_ghosts(mapping_file, block)
%%%% Set options %%%%%%%%%%%%%%%%%
plot_valid_grid = true;
plot_extrapolated_grid = false;
plot_interpolated_grid = true;
nr_extend = 4;
np_extend = 4;
rgb;
if strcmp(block,'lcore')
color_map = Re... |
github | LLNL/COGENT-master | ProfileGenerator.m | .m | COGENT-master/COGENT/grid_generation/grid_generator/utilities/ProfileGenerator.m | 12,012 | utf_8 | b543edf0298662bd90b07410b219627e | %%%
% This script reads gfile (for geometry data), COGENT grid generated
% toroidal_flux_data (for q and Phi_norm(Psi_norm) verification) and
% an inone file for plasma parameters. The script converts the original
% profiles as functions of rho=Sqrt(Phi_norm) to functions of Psi_norm,
% and provides capabilites to ex... |
github | LLNL/COGENT-master | smooth_eqdsk.m | .m | COGENT-master/COGENT/grid_generation/grid_generator/utilities/smooth_eqdsk.m | 10,447 | utf_8 | a5816d7fa371a87161dd4fea1e1b1560 | function smooth_eqdsk(input_file, s)
% If a negative value is passed for the smoothing parameter,
% the algorithm will try to select a value automatically
% Open the files
fd_in = fopen(input_file,'r');
output_file = strcat(input_file,'_smoothed');
fd_out = fopen(output_file,'w');
dct_file = strcat(i... |
github | LLNL/COGENT-master | smoothn_mod.m | .m | COGENT-master/COGENT/grid_generation/grid_generator/utilities/smoothn_mod.m | 17,859 | utf_8 | 764c624ffd74ec2f9021696530b0b2a9 | function [z,GammaDCTy,s,exitflag,dz] = smoothn(varargin)
%SMOOTHN Fast smoothing of 1-D to N-D data.
% Z = SMOOTHN(Y) automatically smoothes the uniformly-sampled array Y. Y
% can be any N-D noisy array (time series, images, 3D data,...). Non
% finite data (NaN or Inf) are treated as missing values.
%
% ... |
github | LLNL/COGENT-master | smooth_eqdsk.m | .m | COGENT-master/COGENT/grid_generation/sample_grids/DIII-D_119919/smooth_eqdsk.m | 9,778 | utf_8 | f9acfcd05d0cb2c0f69b0ac444ce02a0 | function smooth_eqdsk(input_file, s)
% If a negative value is passed for the smoothing parameter,
% the algorithm will try to select a value automatically
% Open the files
fd_in = fopen(input_file,'r');
output_file = strcat(input_file,'_smoothed');
fd_out = fopen(output_file,'w');
dct_file = strcat(i... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | lmSph.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/lmSph.m | 1,573 | utf_8 | aea525f86d47e94cd097837b658ebdc6 | % [T1Est, kEst, cEst, res] = lmSph(data, extra)
%
% Finds estimates of T1, c, and k=-1+cos(flipangle) using the
% Levenberg-Marquardt algorithm via fminsearch.
% The model c(1-k*exp(-t/T1)) is used, i.e. there is only one phase.
% The residual is the rms error between the data and the fit.
%
% INPUT:
% data - the ... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | T1FitDisplayScan.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/T1FitDisplayScan.m | 2,396 | utf_8 | 3cc85d6b7c7986fd1c9608350be11ff3 | % T1FitDisplayScan(loadStr, saveStr)
%
% written by J. Barral, M. Etezadi-Amoli, E. Gudmundson, and N. Stikov, 2009
% (c) Board of Trustees, Leland Stanford Junior University
function T1FitDisplayScan(loadStr, saveStr)
load(loadStr);
dims = size(data);
if numel(dims) > 3
nbslice = dims(3); % See how many slices t... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | dicomLoadAllSeries.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/dicomLoadAllSeries.m | 11,029 | utf_8 | 582d51c36594f526686fae46bb8d6afc | % s = dicomLoadAllSeries(dicomDir, [studyId=''], [sortByFilenameFlag=false])
%
% Loads all the dicom files found in the specified directory
% (recursively searches all sub-directories, too). A structure
% array is returned, with one entry for each series found in the
% directory tree.
%
% If studyID (a string) is provi... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | lm.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/lm.m | 1,580 | utf_8 | ea97d744238379b8eaea1d0bad2e04ac | % [T1Est, bEst, aEst, res] = lm(data, extra)
%
% Finds estimates of T1, a, and b using the Levenberg-Marquardt
% algorithm via fminsearch. The model a+b*exp(-t/T1) is used.
% The residual is the rms error between the data and the fit.
%
% INPUT:
% data - the data to estimate from
% extra.tVec - vector of TI's used
%... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | rdNlsPr.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/rdNlsPr.m | 3,926 | utf_8 | 5bc74fd6f943616b6a974ebdb0ee3315 | % [T1Est, bMagEst, aMagEst, res] = rdNlsPr(data, nlsS)
%
% Finds estimates of T1, |a|, and |b| using a nonlinear least
% squares approach together with polarity restoration.
% The model +-|ra + rb*exp(-t/T1)| is used.
% The residual is the rms error between the data and the fit.
%
% INPUT:
% data - the absolute dat... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | lmSphMag.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/lmSphMag.m | 1,614 | utf_8 | 1a689ca538a3ad138be8bed99cf9c95d | % [T1Est, kEst, cEst, res] = lmSphMag(data, extra)
%
% Finds estimates of T1, |c|, and k=-1+cos(theta) using the
% Levenberg-Marquardt algorithm via fminsearch.
% The model |c*(1-k*exp(-t/T1))|^2 is used, i.e., there is only one phase
% and we have taken the magnitude-square of the data.
% The residual is the rms err... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | showMontage.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/showMontage.m | 1,171 | utf_8 | 6e7b50137c8eef078eeaa9e7fec67755 | % [figH,m,cbH] = showMontage(imVol, [slices=[]], [cmap=gray(256)], [crop=[]], [numCols=[]], figNum=figure, [flip='none'])
%
% flip options are 'none', 'axial'
%
% This function is part of mrvista: http://white.stanford.edu/software/
% written by B. Dougherty, 2007
% (c) Board of Trustees, Leland Stanford Junior Univer... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | plotData.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/plotData.m | 1,191 | utf_8 | 56eb6657751f9f08ceb37f6b5bad9aa9 | % plotData(data, time, datafit, T1)
%
% Takes a 3D data set, where the third dimension is TIs
% and plots the different datapoints of a voxel selected with the mouse
% inspired by relaxPlotTimeSeries.m in mrvista
% (http://white.stanford.edu/software/)
% written by J. Barral, M. Etezadi-Amoli, E. Gudmundson, and N... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | T1FitDisplaySim.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/T1FitDisplaySim.m | 1,098 | utf_8 | 77dbaf47ebc224ad5caa86ffec81306f | % [sigma,mu] = T1FitDisplaySim(T1Est,extra,myTitle)
%
% written by J. Barral, M. Etezadi-Amoli, E. Gudmundson, and N. Stikov, 2009
% (c) Board of Trustees, Leland Stanford Junior University
function [sigma,mu] = T1FitDisplaySim(T1Est, extra, myTitle)
T1EstTmp = T1Est(:);
T1EstTmp = T1EstTmp(find(T1EstTmp > extra.T1V... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | lmSphPr.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/lmSphPr.m | 2,215 | utf_8 | 8a99243b8cb6e118db955c68c0cf6c82 | % [T1Est, kEst, cEst, res] = lmSphPr(data, extra)
%
% Finds estimates of T1, |c|, and k=-1+cos(theta) using the
% Levenberg-Marquardt algorithm via fminsearch and phase restoration.
% The model |c|*(1-k*exp(-t/T1)) is used, i.e., there is only one phase
% and we have taken the magnitude of the data.
% The residual is ... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | T1SimExperiment.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/T1SimExperiment.m | 2,063 | utf_8 | 8c451488a2818c0c3c49f7517c89e0b4 | % [T1Est, bEst, aEst, res] = ...
% T1SimExperiment(MC,stdNoise,T1,theta,extra,method)
%
% Estimates T1 for simulated experiment.
%
% written by J. Barral, M. Etezadi-Amoli, E. Gudmundson, and N. Stikov, 2009
% (c) Board of Trustees, Leland Stanford Junior University
function [T1Est, bEst, aEst, res] = ...
T1SimExp... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | T1ScanExperiment.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/T1ScanExperiment.m | 5,988 | utf_8 | cb58494cdc275ffa905aa0501697e5ab | % T1ScanExperiment(loadStr, saveStr, method)
%
% loadStr: the data on which the fit will be performed, to be loaded
% saveStr: where results of the fit will be saved
% method: what fitting method to use: RD-NLS, RD-NLS-PR
%
% Estimates T1 together with:
% RD-NLS: a and b parameters to fit the data to a + b*exp(-TI/T... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | makeMontage.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/makeMontage.m | 2,701 | utf_8 | 6dc7e58aa931175f8f319552d4f1114c | % [img,coords] = makeMontage(imgCube, [sliceList], [fileName], [numAcross], [backVal])
%
% Compiles a montage image from the images in imgCube.
% (imgCube is x by y by sliceNum)
% sliceList, if specified, determines which slices to extract.
% (it defaults to all slices if it's empty or omitted)
% The image can be dis... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | getNLSStruct.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/getNLSStruct.m | 1,938 | utf_8 | b3bd2da7f5a80e157019995162fbdaa5 | % nlsS = getNLSStruct( extra, dispOn, zoom)
%
% extra.tVec : defining TIs
% (not called TIVec because it looks too much like T1Vec)
% extra.T1Vec : defining T1s
% dispOn : 1 - display the struct at the end
% 0 (or omitted) - no display
% zoom : 1 (or omitted) - do a n... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | explode.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/explode.m | 1,616 | utf_8 | a91d81b843760c55e8a43dc049566212 | % exploded = explode(separator, stringToExplode, itemWrapper)
%
% Import of a very cool php function. Also optionally allows an itemWrapper,
% which is useful for removing quotes around the cellArray items. Currently,
% itemWrapper can only be one character long.
%
% Easiest to use examples:
% explode(',', {'one,tw... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | customGaussFit.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/customGaussFit.m | 2,284 | utf_8 | 8d6840501fff67cb2dfad57932d4784f | % [sigma,mu,A] = customGaussFit(x,y,h)
%
% this function is doing fit to the function
% y = A * exp( -(x-mu)^2 / (2*sigma^2) )
%
% the fitting is been done by a polyfit
% the lan of the data.
%
% h is the threshold which is the fraction
% from the maximum y height that the data
% is been taken from.
% h should be a num... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | rdNls.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mfiles/rdNls.m | 2,847 | utf_8 | 1ce29c3eef5accdd10777b83a0b697ec | % [T1Hat, bHat, aHat, residual] = rdNls(data, nlsS)
%
% Finds estimates of T1, a, and b using a reduced-dimension nonlinear least
% squares approach. The model a+b*exp(-t/T1) is used, where a and b are
% complex and T1 is real.
% The residual is the rms error between the data and the fit.
%
% INPUT:
% data - the dat... |
github | mathieuboudreau/Gold-Standard-Inversion-Recovery-T1-Mapping-master | T1ScanExperiment_quiet.m | .m | Gold-Standard-Inversion-Recovery-T1-Mapping-master/mb_mfiles/T1ScanExperiment_quiet.m | 5,562 | utf_8 | 93720c1048001933e4c7e4e8300e04c9 | % T1ScanExperiment(loadStr, saveStr, method)
%
% loadStr: the data on which the fit will be performed, to be loaded
% saveStr: where results of the fit will be saved
% method: what fitting method to use: RD-NLS, RD-NLS-PR
%
% Estimates T1 together with:
% RD-NLS: a and b parameters to fit the data to a + b*exp(-TI/T... |
github | bdairobot/Firmware-master | ellipsoid_fit.m | .m | Firmware-master/Tools/Matlab/ellipsoid_fit.m | 6,102 | utf_8 | b8fff7152313707a347ab528f7fbce9b | % Copyright (c) 2009, Yury Petrov
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions... |
github | guevaracodina/pat12-master | pat_ica_figures_run.m | .m | pat12-master/pat_ica_figures_run.m | 9,335 | utf_8 | b39151ccb196a7435ffe376308e69d01 | function out = pat_ica_figures_run(job)
%function CellsortICAplot(mode, ica_filters, ica_sig, f0, tlims, dt, ratebin, plottype, ICuse, spt, spc)
% CellsortICAplot(mode, ica_filters, ica_sig, f0, tlims, dt, ratebin, plottype, ICuse, spt, spc)
%
% Display the results of ICA analysis in the form of paired spatial filters... |
github | guevaracodina/pat12-master | pat_reslice.m | .m | pat12-master/pat_reslice.m | 13,729 | utf_8 | 721fb122d11623ebc6bb2e515e7d3952 | function rVO = pat_reslice(P,flags)
% Rigid body reslicing of images
% FORMAT rVO = pat_reslice(P,flags)
%
% P - matrix or cell array of filenames {one string per row}
% All operations are performed relative to the first image.
% ie. Coregistration is to the first image, and resampling
% ... |
github | guevaracodina/pat12-master | pat_realign.m | .m | pat12-master/pat_realign.m | 19,146 | utf_8 | eedd1ff926fb34cd89df55b1efe110c9 | function P = pat_realign(P,flags)
% Estimation of within modality rigid body movement parameters
% FORMAT P = pat_realign(P,flags)
%
% P - matrix of filenames {one string per row}
% All operations are performed relative to the first image.
% ie. Coregistration is to the first image, and resampling
%... |
github | guevaracodina/pat12-master | pat_extract_bmode_cfg.m | .m | pat12-master/pat_extract_bmode_cfg.m | 2,465 | utf_8 | 346425a4ba8546b5b951767cdabb418f | function extract_bmode1 = pat_extract_bmode_cfg
% Graphical interface configuration function for pat_extract_bmode_run.
% This code is part of a batch job configuration system for MATLAB. See help
% matlabbatch for a general overview.
%_______________________________________________________________________________
% Co... |
github | guevaracodina/pat12-master | pat_choose_pc_run.m | .m | pat12-master/pat_choose_pc_run.m | 3,878 | utf_8 | 44e784fc00798506c90f370aa240ff22 | function out = pat_choose_pc_run(job)
% Inputs:
% fn - movie file name. Must be in TIFF format.
% mixedfilters - N x X matrix of N spatial signal mixtures sampled at X
% spatial points.
%
% Outputs:
% PCuse - vector of indices of the PCs to be kept for dimensional
% reduction
PATmat=job.PATmat;
% Loop over ... |
github | guevaracodina/pat12-master | VsiParseXmlExtended.m | .m | pat12-master/segmentation/VsiParseXmlExtended.m | 10,570 | utf_8 | 5dd9bc049259010025f3d889e9e846d3 | % VsiParseXml.m
% Copyright VisualSonics 1999-2010
% A. Needles, J. Mehi, G. Sundar
% Revision: 1.3 Dec 7 2010
% A function to parse xml parameter files from IQ data export on the Vevo 2100
% and read selected parameters
function [ReturnParam] = ParseXml_allModes(filename, ModeName)
try
xDoc = xmlread(fi... |
github | guevaracodina/pat12-master | VsiParseXml.m | .m | pat12-master/segmentation/VsiParseXml.m | 9,559 | utf_8 | bd525bcbb8540f16f0a52fea2663eef6 | % VsiParseXml.m
% Copyright VisualSonics 1999-2010
% A. Needles, J. Mehi, G. Sundar
% Revision: 1.3 Dec 7 2010
% A function to parse xml parameter files from IQ data export on the Vevo 2100
% and read selected parameters
function [ReturnParam] = ParseXml_allModes(filename, ModeName)
try
xDoc = xmlread(fi... |
github | guevaracodina/pat12-master | VsiBModeReconstructRFExtended.m | .m | pat12-master/segmentation/VsiBModeReconstructRFExtended.m | 2,978 | utf_8 | f1e61c03f3dc0c31ba124b8efa13571a | % VsiBModeReconstructRF.m
% A script to open IQ files from B-Mode data export on the Vevo 2100
% and reconstruct the RF signal
% Authors: A. Needles, J. Mehi
% Copyright VisualSonics 1999-2010
% Revision: 1.0 June 28 2010
% Revision: 1.1 July 22 2010: for software version 1.2 or higher
function [handles] = Vsi... |
github | guevaracodina/pat12-master | VsiReorderChannels.m | .m | pat12-master/apoe_batch/VsiReorderChannels.m | 2,621 | utf_8 | 2b2b30b55ae92aac5cece2f4007af098 | % VsiReorderChannels.m
% Copyright VisualSonics 1999-2010
% A. Needles
% Revision 1.0: Dec. 7, 2010.
% A function to reorder unbeamformed IQ data on the Vevo 2100 from system
% channels to transducer elements
function [IdataPlanar, QdataPlanar] = ReorderChannels(Idata, Qdata, ApertureStart, ApertureEnd, samples... |
github | guevaracodina/pat12-master | VsiParseXmlModif.m | .m | pat12-master/apoe_batch/VsiParseXmlModif.m | 13,097 | utf_8 | 869257e84e82a8d05bfd94bbe901054c | % VsiParseXml.m
% Copyright VisualSonics 1999-2010
% A. Needles, J. Mehi, G. Sundar
% Revision: 1.3 Dec 7 2010
% A function to parse xml parameter files from IQ data export on the Vevo 2100
% and read selected parameters
function [ReturnParam] = ParseXml_allModes(filename, ModeName)
try
xDoc = xmlread(fi... |
github | guevaracodina/pat12-master | VsiBModeReconstructRFModif.m | .m | pat12-master/apoe_batch/VsiBModeReconstructRFModif.m | 4,615 | utf_8 | 7ea94652477b1b1288543acae8cb1472 | % VsiBModeReconstructRF.m
% A script to open IQ files from B-Mode data export on the Vevo 2100
% and reconstruct the RF signal
% Authors: A. Needles, J. Mehi
% Copyright VisualSonics 1999-2010
% Revision: 1.0 June 28 2010
% Revision: 1.1 July 22 2010: for software version 1.2 or higher
function [handles, abs_d... |
github | guevaracodina/pat12-master | VsiReorderChannels.m | .m | pat12-master/Vevo_LAZR/VsiReorderChannels.m | 2,621 | utf_8 | 2b2b30b55ae92aac5cece2f4007af098 | % VsiReorderChannels.m
% Copyright VisualSonics 1999-2010
% A. Needles
% Revision 1.0: Dec. 7, 2010.
% A function to reorder unbeamformed IQ data on the Vevo 2100 from system
% channels to transducer elements
function [IdataPlanar, QdataPlanar] = ReorderChannels(Idata, Qdata, ApertureStart, ApertureEnd, samples... |
github | guevaracodina/pat12-master | VsiOpenRawBmode8.m | .m | pat12-master/Vevo_LAZR/VsiOpenRawBmode8.m | 1,638 | utf_8 | fab44496b2d19d67bba473e3a35ba910 | % VsiOpenRawBmode8.m
% Copyright VisualSonics 1999-2012
% A. Needles
% Revision: 1.0 Oct 24 2012
% A function to open RAW 8-bit B-Mode files from data export on the Vevo 2100
% and read selected parameters
function [Rawdata, WidthAxis, DepthAxis] = VsiOpenRawBmode8(fnameBase, ModeName, iframe)
% Set up fil... |
github | guevaracodina/pat12-master | script_coregistration.m | .m | pat12-master/Vevo_LAZR/script_coregistration.m | 13,310 | utf_8 | 1661dd11d3ce93ea561cd8531b835e1b | function script_coregistration
clear; clc;
fprintf('Manual coregistration PA-mode to B-mode\n')
% PAT list (56 subjects [21 OS + 28 RS] )
job.PATmat = {
% 'F:\Edgar\Data\PAT_Results\2012-11-09-16-18-31_ctl03\PAT.mat'
% % Alignment error in 2012-11-09-16-18-31_ctl03
... |
github | guevaracodina/pat12-master | VsiOpenRawPa.m | .m | pat12-master/Vevo_LAZR/VsiOpenRawPa.m | 1,807 | utf_8 | 177319b582038fa244bebd16894586f0 | % VsiOpenRawPa.m
% Copyright VisualSonics 1999-2010
% A. Needles
% Revision: 1.0 Dec 3 2010
% A function to open RAW PA-Mode files from data export on the Vevo 2100
% and read selected parameters
function [Rawdata, WidthAxis, DepthAxis] = VsiOpenRawPa(fnameBase, ModeName, iframe)
% Set up file names
fname... |
github | guevaracodina/pat12-master | pat_raw_bmode_read_cfg.m | .m | pat12-master/Vevo_LAZR/pat_raw_bmode_read_cfg.m | 2,364 | utf_8 | 01d5d3c7e7eeafdd1f6686374a4f728f | function extract_rawB1 = pat_raw_bmode_read_cfg
% Graphical interface configuration function for pat_raw_bmode_read_run.
% This code is part of a batch job configuration system for MATLAB. See help
% matlabbatch for a general overview.
%_______________________________________________________________________________
% C... |
github | guevaracodina/pat12-master | script_recover_nifti_files.m | .m | pat12-master/Vevo_LAZR/script_recover_nifti_files.m | 13,640 | utf_8 | 22e3046c10a3d96b24c8300cccebdee0 | function script_coregistration
clear; clc;
fprintf('Manual coregistration PA-mode to B-mode\n')
% PAT list (56 subjects [21 OS + 28 RS] )
job.PATmat = {
% 'F:\Edgar\Data\PAT_Results\2012-11-09-16-18-31_ctl03\PAT.mat'
% % Alignment error in 2012-11-09-16-18-31_ctl03
... |
github | guevaracodina/pat12-master | pat_raw2bmp_bmode.m | .m | pat12-master/Vevo_LAZR/pat_raw2bmp_bmode.m | 2,702 | utf_8 | 97344aaf358a932bf8fe9d838ad276cb | function fileNameTXT = pat_raw2bmp_bmode(rawBmodeFname, dir_patmat, bmp_dir)
% Extraction of B-Mode RAW data. Saves frames as a series of figures (.BMP).
% Always compresses images as .PNG files in order to save disk space.
%_______________________________________________________________________________
% Copyright... |
github | guevaracodina/pat12-master | pat_raw_pamode_read_cfg.m | .m | pat12-master/Vevo_LAZR/pat_raw_pamode_read_cfg.m | 2,452 | utf_8 | d45984f781d812b41e35aadb3e7f4838 | function extract_rawPA1 = pat_raw_pamode_read_cfg
% Graphical interface configuration function for pat_raw_pamode_read_run.
% This code is part of a batch job configuration system for MATLAB. See help
% matlabbatch for a general overview.
%_______________________________________________________________________________
... |
github | guevaracodina/pat12-master | VsiOpenRawBmode32.m | .m | pat12-master/Vevo_LAZR/VsiOpenRawBmode32.m | 1,641 | utf_8 | bff30cc7f769940dd45db3b2470fdfb8 | % VsiOpenRawBmode32.m
% Copyright VisualSonics 1999-2012
% A. Needles
% Revision: 1.0 Oct 24 2012
% A function to open RAW 32-bit B-Mode files from data export on the Vevo 2100
% and read selected parameters
function [Rawdata, WidthAxis, DepthAxis] = VsiOpenRawBmode32(fnameBase, ModeName, iframe)
% Set up ... |
github | embotech/Y2F-master | findPathPartition.m | .m | Y2F-master/Y2F/findPathPartition.m | 1,942 | utf_8 | 66414776ed3466ec87295148ee4b7e79 | function partition = findPathPartition( G )
%FINDPATHPARTITION Applies path partitioning algorithm to find a maximal
%path partition of the graph G. G has the format described in EMPTYGRAPH.
%
% This file is part of the y2f project: http://github.com/embotech/y2f,
% a project maintained by embotech under the MIT open-... |
github | embotech/Y2F-master | compileSolverInterfaceCode.m | .m | Y2F-master/Y2F/@optimizerFORCES/compileSolverInterfaceCode.m | 10,360 | utf_8 | 16bd3a54501b8a92de9d379c7eeef507 | function [ success ] = compileSolverInterfaceCode( self )
%COMPILESOLVERINTERFACECODE Compiles the MEX code generated by
%GENERATECINTERFACECODE and GENERATEMEXINTERFACECODE.
%
% This file is part of the y2f project: http://github.com/embotech/y2f,
% a project maintained by embotech under the MIT open-source license.
... |
github | embotech/Y2F-master | optimizerFORCES.m | .m | Y2F-master/Y2F/@optimizerFORCES/optimizerFORCES.m | 52,134 | utf_8 | 18358d01fc67fb25ed91c16dd6ecdc32 | function [self, success] = optimizerFORCES( constraint,objective,codeoptions,parameters,solverOutputs,parameterNames,outputNames,mode )
%OPTIMIZERFORCES Generates a FORCESPRO solver from a YALMIP problem formulation
%
% solver = OPTIMIZERFORCES(constraint,objective,codeoptions,parameters,solverOutputs)
% generates ... |
github | embotech/Y2F-master | buildSolver.m | .m | Y2F-master/Y2F/@optimizerFORCES/buildSolver.m | 1,797 | utf_8 | f202d45f6f2d442b8e343c358be59140 | function [ success ] = buildSolver( self )
%BUILDSOLVER Generates FORCESPRO solver(s) as well as MEX wrapper to call
%main controller.
%
% This file is part of the y2f project: http://github.com/embotech/y2f,
% a project maintained by embotech under the MIT open-source license.
%
% (c) Gian Ulli and embotech AG, Zuric... |
github | embotech/Y2F-master | compileSimulinkInterfaceCode.m | .m | Y2F-master/Y2F/@optimizerFORCES/compileSimulinkInterfaceCode.m | 9,254 | utf_8 | 7d6e85051217d990fcdd06e5a217d5fa | function [ success ] = compileSimulinkInterfaceCode( self )
%COMPILESIMULINKINTERFACECODE Compiles the MEX code generated by
%GENERATESIMULINKINTERFACECODE. Important: This function has to be called
%AFTER COMPILESOLVERINTERFACECODE.
%
% This file is part of the y2f project: http://github.com/embotech/y2f,
% a project... |
github | JianqiangRen/Global_Priors_RGBD_Saliency_Detection-master | edison_wrapper.m | .m | Global_Priors_RGBD_Saliency_Detection-master/edison_matlab_interface/edison_wrapper.m | 6,089 | utf_8 | d11842abf8938da0d7ae3bf0ff7303a2 | function [varargout] = edison_wrapper(rgbim, featurefun, varargin)
%
% Performing mean_shift operation on image
%
% Usage:
% [fimage labels modes regSize grad conf] = edison_wrapper(rgbim, featurefunc, ...)
%
% Inputs:
% rgbim - original image in RGB space
% featurefunc - converting RGB to some feature space in ... |
github | Jaia89/VisibilityGraphMotifs-master | NVG_motifs.m | .m | VisibilityGraphMotifs-master/NVG_motifs.m | 2,103 | utf_8 | c1b43c3435d4928c2afffd4af3ee7232 | % ****************************************************************************
% (NATURAL) VISIBILITY GRAPH MOTIF PROFILE
%
% This code can be redistributed and/or modified under the terms of the
% GNU General Public License as published by the Free Software Foundation,
% either version 3 of the License, or (at you... |
github | Jaia89/VisibilityGraphMotifs-master | HVG_motifs.m | .m | VisibilityGraphMotifs-master/HVG_motifs.m | 2,050 | utf_8 | 306aee7c5cfd355261aec86f7ddeb5db | % ****************************************************************************
% HORIZONTAL VISIBILITY GRAPH MOTIF PROFILE
%
% This code can be redistributed and/or modified under the terms of the
% GNU General Public License as published by the Free Software Foundation,
% either version 3 of the License, or (at you... |
github | yxie/Digit-Recognition-master | LDA.m | .m | Digit-Recognition-master/SVM/LDA.m | 947 | utf_8 | 66f7fbe19487992ed4736c5d63ef2859 | %INPUT:
%X: data, row is observation, column is feature
%y: label
function [W] = LDA(X, y)
numFeatures = size(X, 2);
labels = unique(y);
%disp(labels);
numLabels = length(labels);
% mean
mu = zeros(numLabels, numFeatures);
for i=1:numLabels
mu(i,:) = mean( X((y==labels(i)),:) ); %row... |
github | nvtu/Object-Retrieval-From-Videos-master | tf_idf.m | .m | Object-Retrieval-From-Videos-master/Demo Code/tf_idf.m | 1,252 | utf_8 | a19354ba8f46a8c962a628b5ae806279 | function tf_idf(loadDir, saveDir, fileName, saveName)
% % loadDir = directory to load quantize file
% % saveDir = directory to save quantize file after using tfidf
% % fileName = name of quantize file needed to load
% % saveName = name of quantize file after using tfidf needed to save
q=load(fullfile(loadDir... |
github | beczkowb/morphology-master | dilation_reconstruction.m | .m | morphology-master/dilation_reconstruction.m | 324 | utf_8 | 9da740e60e5acca35058686427e31825 | % mask to obrazek oryginalny
% image jest obrazem ze znacznikami
function [ rec_image ] = dilation_reconstruction( image, mask, se, loops )
rec_image = image;
% powtarzamy dylację geodezyjną wiele razy tak by wyniki przestasły się
% zmieniać
for i=1: loops
rec_image = geo_dilation(rec_image, mask, se);
end
end |
github | beczkowb/morphology-master | geo_dilation.m | .m | morphology-master/geo_dilation.m | 671 | utf_8 | d305173804c33ed328c8ef9e0eae2e02 | % image to obazek wejściowy (początkowy)
% mask to maska
function [ geo_image ] = geo_dilation( image, mask, se )
rows = size(image, 1);
cols = size(image, 2);
geo_image = image;
% wykonujemy dylację
dilated_image = dilate(image, se);
% przetwarzamy do postaci w której możemy porównywać piksele metodą
% porządkową
m... |
github | beczkowb/morphology-master | erosion_reconstruction.m | .m | morphology-master/erosion_reconstruction.m | 340 | utf_8 | 0b7d5884de83a5aa21f687cc74c60bb6 | % mask to obrazek oryginalny
% image to obraz-marker (obraz ze znacznikami)
function [ rec_image ] = erosion_reconstruction( image, mask, se, loops )
rec_image = image;
% wielokrotnie powtarzamy operację erozji geodezyjnej, aż wyniki przestaną
% się zmieniać
for i=1: loops
rec_image = geo_erosion(rec_image, mask... |
github | beczkowb/morphology-master | geo_erosion.m | .m | morphology-master/geo_erosion.m | 653 | utf_8 | a9357cb174141c9bc9a7087dd4394d0c | % image to obrazek wejściowy
% mask to maska
function [ geo_image ] = geo_erosion( image, mask, se )
rows = size(image, 1);
cols = size(image, 2);
geo_image = zeros(rows, cols, 3);
eroded_image = erode(image, se);
% przetwarzamy obrazki do postaci w której można porównywać piksele metodą
% porządkową
mixed_eroded_im... |
github | amincheloh/speex-prebuilt-master | echo_diagnostic.m | .m | speex-prebuilt-master/deps/speex/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | deepsemantic/image_captioning-master | classification_demo.m | .m | image_captioning-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
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