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github | mutual-ai/fundus-vessel-segmentation-tmbe-master | openLabeledData.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Open/openLabeledData.m | 1,602 | utf_8 | c4260da06e1380be67a2afa728f4c9ca |
function [images, labels, masks, numberOfPixels] = openLabeledData(folder, preprocessing_options)
disp(strcat('Loading data from ', [' '], folder));
% Get folder to open images, masks and labels
imagesFolder = strcat(folder, filesep, 'images', filesep);
masksFolder = strcat(folder, filesep, 'masks', ... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | openMultipleImages.m | .m | fundus-vessel-segmentation-tmbe-master/Util/Open/openMultipleImages.m | 569 | utf_8 | c1f3b977469e96da4647334231bd823f | % Open multiple files from a given directory
function [images, allNames] = openMultipleImages(directory)
% Get all file names
allNames = getMultipleImagesFileNames(directory);
% Get all the images in the directory and count the number of pixels
images = cell(length(allNames), 1);
for i = 1:length(al... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | CRFInference.m | .m | fundus-vessel-segmentation-tmbe-master/CRF/CRFInference.m | 1,022 | utf_8 | 7837edae2cf088ed157f65a63832da4d |
function [segmentation] = CRFInference(config, unaryPotentials, mask, pairwiseFeatures, weights)
% CRFInference Obtain the segmentation by minimizing the overall energy of
% the CRF.
% [segmentation] = CRFInference(config, unaryPotentials, mask, pairwiseFeatures, weights)
% OUTPUT: segmentation: binary segmentation
% ... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | FullyCRFWrapperWithGivenPairwises.m | .m | fundus-vessel-segmentation-tmbe-master/CRF/CRF_1.0/FullyCRFWrapperWithGivenPairwises.m | 1,135 | utf_8 | e8923adc7b91ef82683fef7654ccd21f |
function y = FullyCRFWrapperWithGivenPairwises(config, unaryPotentials, mask, pairwiseFeatures, weights)
% FullyCRFWrapperWithGivenPairwises This function wrapps the MEX-function
% that implements the fully connected CRF inference
% y = FullyCRFWrapperWithGivenPairwises(config, unaryPotentials, mask, pairwiseFeatures,... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | LocalNeighborhoodBasedCRF.m | .m | fundus-vessel-segmentation-tmbe-master/CRF/maxflow/LocalNeighborhoodBasedCRF.m | 1,701 | utf_8 | 8710b88627de394a0571009d2870364e |
function [segmentation] = LocalNeighborhoodBasedCRF(unaryPotentials, mask, pairwiseFeatures, weights)
% LocalNeighborhoodBasedCRF This function wrapps the inference on local
% neighborhood based CRFs.
% [segmentation] = LocalNeighborhoodBasedCRF(unaryPotentials, mask, pairwiseFeatures, weights)
% OUTPUT: segmentation:... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getLocalNeighborhoodBasedPairwisePotentials.m | .m | fundus-vessel-segmentation-tmbe-master/CRF/maxflow/getLocalNeighborhoodBasedPairwisePotentials.m | 2,044 | utf_8 | f5902a2fb2e0b08a10a9aae79939cfef |
function [potentials] = getLocalNeighborhoodBasedPairwisePotentials(pairwiseFeatures, labels)
% getLocalNeighborhoodBasedPairwisePotentials This function computes
% efficiently the pairwise potentials.
% [potentials] = getLocalNeighborhoodBasedPairwisePotentials(pairwiseFeatures, labels)
% OUTPUT: potentials: pairwise... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getBunchSegmentations2.m | .m | fundus-vessel-segmentation-tmbe-master/Segmentations/getBunchSegmentations2.m | 3,681 | utf_8 | 58f30e37eb8d717c7d6ad416afa0df23 |
function [segmentations, qualityMeasures] = getBunchSegmentations2(config, data, model)
% getBunchSegmentations2 Segment a number of given images
% [segmentations, qualityMeasures] = getBunchSegmentations2(config, data, model)
% OUTPUT: segmentations: a cell array containing all the segmentations
% qualityMeas... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getWeights.m | .m | fundus-vessel-segmentation-tmbe-master/Segmentations/getWeights.m | 949 | utf_8 | e529a60e17391878aa67899abd53ada9 |
function [W_unaries, W_pairwises, bias] = getWeights(W, config)
% getWeights Separates the weights for unary, pairwise and bias
% [W_unaries, W_pairwises, bias] = getWeights(W, config)
% OUTPUT: W_unaries: weights for the unary potentials
% W_pairwises: weights for the pairwise potentials
% bias: weigh... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getSegmentationFromData2.m | .m | fundus-vessel-segmentation-tmbe-master/Segmentations/getSegmentationFromData2.m | 4,088 | utf_8 | bfaf4d06b74ddab9cbe748168928c5cc |
function [segmentation, qualityMeasures] = getSegmentationFromData2(config, mask, y, X, pairwiseKernels, model)
% getSegmentationFromData2 Segment a given image
% [segmentation, qualityMeasures] = getSegmentationFromData2(config, mask, y, X, pairwiseKernels, model)
% OUTPUT: segmentation: resulting segmentation
% ... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | generateFeatureFilter.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Util/generateFeatureFilter.m | 647 | utf_8 | 329a6b7ca5cbc7f88dd2363f91b8ca74 |
function [featureFilter] = generateFeatureFilter(selectedFeatures, sizes)
% generateFeatureFilter Generate a binary array to filter the features
% [featureFilter] = generateFeatureFilter(selectedFeatures, sizes)
% OUTPUT: featureFilter: a binary array indicating which features are going
% to be used
% INPUT: s... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getPairwiseDeviations.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Extraction/getPairwiseDeviations.m | 2,200 | utf_8 | 77da550272a251f2c95d1900b3f5381f |
function [pairwiseDeviations] = getPairwiseDeviations(pairwiseFeatures, pairwiseDimensionality)
% getPairwiseDeviations Obtain the pairwise deviations for the pairwise
% kernels.
% [pairwiseDeviations] = getPairwiseDeviations(pairwiseFeatures, pairwiseDimensionality)
% OUTPUT: pairwiseDeviations: pairwise deviations.
... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | extractFeaturesFromImage.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Extraction/extractFeaturesFromImage.m | 2,319 | utf_8 | 4a4591c4abb9d54ff3cb1b2a11590302 |
function [X] = extractFeaturesFromImage(image, mask, config, selectedFeatures, isUnary)
% extractFeaturesFromImage Extract features from a given image
% [X] = extractFeaturesFromImage(image, mask, config, selectedFeatures, isUnary)
% OUTPUT: X: features extracted from the image
% INPUT: image: grayscale image
% ... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | getPairwiseFeatures.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Extraction/getPairwiseFeatures.m | 725 | utf_8 | 1de7c0d9c830474a33b0007408b7221e |
function [pairwiseKernels] = getPairwiseFeatures(pairwiseFeatures, deviations)
% getPairwiseFeatures Divide the pairwise features by the given deviations
% [pairwiseKernels] = getPairwiseFeatures(pairwiseFeatures, deviations)
% OUTPUT: pairwiseKernels: pairwise kernels.
% INPUT: pairwiseFeatures: a cell array containi... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | extractFeaturesFromImages.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Extraction/extractFeaturesFromImages.m | 1,194 | utf_8 | 500836a03713a199de34615a079151ae |
function [features, dimensionality] = extractFeaturesFromImages(images, masks, config, selectedFeatures, unary)
% extractFeaturesFromImages Extract features from a given list of images
% [features, dimensionality] = extractFeaturesFromImages(images, masks, config, selectedFeatures, unary)
% OUTPUT: features: a cell-ar... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | Nguyen2013.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Features/Nguyen2013.m | 1,643 | utf_8 | 4ab129078ac8edb1fb5da9cf4eab647f |
function [features] = Nguyen2013(I, mask, unary, options)
% Nguyen2013 Compute the Nguyen et al features
% I = Nguyen2013(I, mask, unary, options)
% OUTPUT: features: Nguyen et al features
% INPUT: I: grayscale image
% mask: a binary mask representing the FOV
% unary: a boolean flag indicating if the fea... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | Intensities.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Features/Intensities.m | 1,183 | utf_8 | 42888f145af97afa70a41437db9c2f19 |
function I = Intensities(I, mask, unary, options)
% Intensities Compute the intensity feature
% I = Intensities(I, mask, unary, options)
% OUTPUT: I: image intensities
% INPUT: I: grayscale image
% mask: a binary mask representing the FOV
% unary: a boolean flag indicating if the feature is unary or
% ... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | Zana2001.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Features/Zana2001.m | 2,436 | utf_8 | 62b421c7d6bd232faff593192c50ea38 |
function [zanan] = Zana2001(I, mask, unary, options)
% Zana2001 Compute the Zana and Klein feature
% I = Zana2001(I, mask, unary, options)
% OUTPUT: features: Zana and Klein features
% INPUT: I: grayscale image
% mask: a binary mask representing the FOV
% unary: a boolean flag indicating if the feature i... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | standardize.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Features/nguyen/standardize.m | 672 | utf_8 | 7687fffab3ec991e9f9bcc5309167fab | function simg = standardize(img,mask,wsize)
if (nargin == 2 || wsize == 0)
simg = globalstandardize(img,mask);
else
img(mask == 0) = 0;
img_mean = nlfilter(img,[wsize, wsize],@getmean);
img_std = nlfilter(img,[wsize, wsize],@getstd);
simg = (img - img_mean)./img_std;
simg(img_std == 0) = 0;
... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | get_lineresponse.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Features/nguyen/get_lineresponse.m | 677 | utf_8 | f71fb684af70d74e5c016f5557606e04 |
function [R, bestResponse] = get_lineresponse(I, angles, W, L)
% img: extended inverted gc
% W: window size, L: line length
% R: line detector response
% Compute the average
avgresponse = imfilter(I, fspecial('average', W), 'replicate');
% Compute the responses
imglinestrength = zeros(si... |
github | mutual-ai/fundus-vessel-segmentation-tmbe-master | get_linemask.m | .m | fundus-vessel-segmentation-tmbe-master/Features/Features/nguyen/get_linemask.m | 2,106 | utf_8 | 59787d6412677376c9e70a2b0d490acb | function linemask = get_linemask(theta,masksize)
% (theta,masksize)
% Create a mask for line with angle theta
if theta > 90
mask = getbasemask(180- theta,masksize);
linemask = rotatex(mask);
else
linemask = getbasemask(theta,masksize);
end
% imshow(linemask,'InitialMagnification','fit');
end
function rotatedm... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawprobellipse.m | .m | SLAM-Algorithms-Octave-master/2_Unscented_Transform/octave/tools/drawprobellipse.m | 1,803 | utf_8 | 90c41a3bebf740e86100f47974753eb3 | %DRAWPROBELLIPSE Draw elliptic probability region of a Gaussian in 2D.
% DRAWPROBELLIPSE(X,C,ALPHA,COLOR) draws the elliptic iso-probabi-
% lity contour of a Gaussian distributed bivariate random vector X
% at the significance level ALPHA. The ellipse is centered at X =
% [x; y] where C is the associated 2x2 co... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | chi2invtable.m | .m | SLAM-Algorithms-Octave-master/2_Unscented_Transform/octave/tools/chi2invtable.m | 231,909 | utf_8 | d16aef6be089f46039e76c200f7577d8 | %CHI2INVTABLE Lookup table of the inverse of the chi-square cdf.
% X = CHI2INVTABLE(P,V) returns the inverse of the chi-square cumu-
% lative distribution function (cdf) with V degrees of freedom at
% the value P. The chi-square cdf with V degrees of freedom, is
% the gamma cdf with parameters V/2 and 2.
%
... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawellipse.m | .m | SLAM-Algorithms-Octave-master/2_Unscented_Transform/octave/tools/drawellipse.m | 994 | utf_8 | c0100a4cf263e6e87026b3214221e84d | %DRAWELLIPSE Draw ellipse.
% DRAWELLIPSE(X,A,B,COLOR) draws an ellipse at X = [x y theta]
% with half axes A and B. Theta is the inclination angle of A,
% regardless if A is smaller or greater than B. COLOR is a
% [r g b]-vector or a color string such as 'r' or 'g'.
%
% H = DRAWELLIPSE(...) returns the graphi... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | t2v.m | .m | SLAM-Algorithms-Octave-master/4_Gridmapping/octave/tools/t2v.m | 133 | utf_8 | 6606805d2b95b1d27de95e33aa633889 | #computes the pose vector v from an homogeneous transform A
function v=t2v(A)
v(1:2, 1)=A(1:2,3);
v(3,1)=atan2(A(2,1),A(1,1));
end
|
github | kiran-mohan/SLAM-Algorithms-Octave-master | read_robotlaser.m | .m | SLAM-Algorithms-Octave-master/4_Gridmapping/octave/tools/read_robotlaser.m | 1,375 | utf_8 | 7b26523688f4d9499097947920eeef74 | % read a file containing ROBOTLASER1 in CARMEN logfile format
function laser=read_robotlaser(filename)
fid = fopen(filename, 'r');
laser = cell();
while true
ln = fgetl(fid);
if (ln == -1)
break
endif
tokens = strsplit(ln, ' ', true);
if (strcmp(tokens(1), "ROBOTLASER1") == 0)
continue;
endif
... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | v2t.m | .m | SLAM-Algorithms-Octave-master/4_Gridmapping/octave/tools/v2t.m | 165 | utf_8 | bd190805c2c8033bb7843a4c3559f866 | #computes the homogeneous transform matrix A of the pose vector v
function A=v2t(v)
c=cos(v(3));
s=sin(v(3));
A=[c, -s, v(1);
s, c, v(2);
0 0 1 ];
end
|
github | kiran-mohan/SLAM-Algorithms-Octave-master | bresenham2.m | .m | SLAM-Algorithms-Octave-master/4_Gridmapping/octave/tools/bresenham2.m | 1,360 | utf_8 | 1b7080c156294d251d16ed01efc8b262 | function [X,Y] = bresenham2(mycoords)
% BRESENHAM: Generate a line profile of a 2d image
% using Bresenham's algorithm
% [myline,mycoords] = bresenham(mymat,mycoords,dispFlag)
%
% - For a demo purpose, try >> bresenham();
%
% - mymat is an input image matrix.
%
% - mycoords is coordinate of the f... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | bresenham.m | .m | SLAM-Algorithms-Octave-master/4_Gridmapping/octave/tools/bresenham.m | 2,239 | utf_8 | f4cd4f72898fa1db9fae7a2a15803750 | function [myline,mycoords,outmat,X,Y] = bresenham(mymat,mycoordinates,dispFlag)
% BRESENHAM: Generate a line profile of a 2d image
% using Bresenham's algorithm
% [myline,mycoords] = bresenham(mymat,mycoordinates,dispFlag)
%
% - For a demo purpose, try >> bresenham();
%
% - mymat is an input image ... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | linearize_pose_landmark_constraint.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/linearize_pose_landmark_constraint.m | 813 | utf_8 | 1300260835c78bbccb865318fc680700 | % Compute the error of a pose-landmark constraint
% x 3x1 vector (x,y,theta) of the robot pose
% l 2x1 vector (x,y) of the landmark
% z 2x1 vector (x,y) of the measurement, the position of the landmark in
% the coordinate frame of the robot given by the vector x
%
% Output
% e 2x1 error of the constraint
% A 2x3 Jaco... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | linearize_pose_pose_constraint.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/linearize_pose_pose_constraint.m | 1,480 | utf_8 | fd728f9074d7fe0767c6fa86f6174e95 | % Compute the error of a pose-pose constraint
% x1 3x1 vector (x,y,theta) of the first robot pose
% x2 3x1 vector (x,y,theta) of the second robot pose
% z 3x1 vector (x,y,theta) of the measurement
%
% You may use the functions v2t() and t2v() to compute
% a Homogeneous matrix out of a (x, y, theta) vector
% for computi... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | linearize_and_solve.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/linearize_and_solve.m | 3,126 | utf_8 | f6ccbf5a2b9534939217dc0fb24f7b8f | % performs one iteration of the Gauss-Newton algorithm
% each constraint is linearized and added to the Hessian
function dx = linearize_and_solve(g)
% number of non-zero elements in graph
nnz = nnz_of_graph(g);
% allocate the sparse H and the vector b
H = spalloc(length(g.x), length(g.x), nnz);
b = zeros(length(g.x)... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | compute_global_error.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/compute_global_error.m | 1,631 | utf_8 | 4a1ef1eb3329879cd7ec352742ca10d4 | % Computes the total error of the graph
function Fx = compute_global_error(g)
Fx = 0;
% Loop over all edges
for eid = 1:length(g.edges)
edge = g.edges(eid);
% pose-pose constraint
if (strcmp(edge.type, 'P') != 0)
x1 = v2t(g.x(edge.fromIdx:edge.fromIdx+2)); % the first robot pose
x2 = v2t(g.x(edge.toI... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | t2v.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/tools/t2v.m | 122 | utf_8 | 4fe2d6a6a2d9713d1811c566c00df3a4 | % computes the pose vector v from a homogeneous transform A
function v=t2v(A)
v = [A(1:2,3); atan2(A(2,1),A(1,1))];
end
|
github | kiran-mohan/SLAM-Algorithms-Octave-master | get_block_for_id.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/tools/get_block_for_id.m | 242 | utf_8 | 45c79bac533c38cfb5bab150d76a2cfe | % returns the block of the state vector which corresponds to the given id
function block = get_block_for_id(g, id)
blockInfo = getfield(g.idLookup, num2str(id));
block = g.x(1+blockInfo.offset : blockInfo.offset + blockInfo.dimension);
end
|
github | kiran-mohan/SLAM-Algorithms-Octave-master | nnz_of_graph.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/tools/nnz_of_graph.m | 468 | utf_8 | 7eb6fe50658d285bbb992af21794cd89 | % calculates the number of non-zeros of a graph
% Actually, it is an upper bound, as duplicate edges might be counted several times
function nnz = nnz_of_graph(g)
nnz = 0;
% elements along the diagonal
for [value, key] = g.idLookup
nnz += value.dimension^2;
end
% off-diagonal elements
for eid = 1:length(g.edges)
... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | invt.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/tools/invt.m | 136 | utf_8 | 9af4f2e99d37fa3d3d966dadec4d881e | % inverts a homogenous transform
function A = invt(m)
A = [m(1:2, 1:2)' [0 0]'; [0 0 1]];
A(1:2, 3) = -A(1:2, 1:2) * m(1:2, 3);
end
|
github | kiran-mohan/SLAM-Algorithms-Octave-master | build_structure.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/tools/build_structure.m | 766 | utf_8 | 8ad7922f6ba64b1062ea3edfdc853840 | % calculates the non-zero pattern of the Hessian matrix of a given graph
function idx = build_structure(g)
idx = [];
% elements along the diagonal
for [value, key] = g.idLookup
dim = value.dimension;
offset = value.offset;
[r,c] = meshgrid(offset+1 : offset+dim, offset+1 : offset+dim);
idx = [idx; [vec(r) ve... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | get_poses_landmarks.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/tools/get_poses_landmarks.m | 333 | utf_8 | 13eea96e29c0c9b7010f898ae4d72d87 | % extract the offset of the poses and the landmarks
function [poses, landmarks] = get_poses_landmarks(g)
poses = [];
landmarks = [];
for [value, key] = g.idLookup
dim = value.dimension;
offset = value.offset;
if (dim == 3)
poses = [poses; offset];
elseif (dim == 2)
landmarks = [landmarks; offset];
... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | v2t.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/tools/v2t.m | 166 | utf_8 | 43f0d024b79314db5a2b162943010b6c | % computes the homogeneous transform matrix A of the pose vector v
function A=v2t(v)
c=cos(v(3));
s=sin(v(3));
A=[c, -s, v(1);
s, c, v(2);
0 0 1 ];
end
|
github | kiran-mohan/SLAM-Algorithms-Octave-master | plot_graph.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/tools/plot_graph.m | 1,395 | utf_8 | a66a1001e404cd4bf33d0d5abc87d73b | % plot a 2D SLAM graph
function plot_graph(g, iteration = -1)
clf;
graphics_toolkit gnuplot
hold on;
[p, l] = get_poses_landmarks(g);
if (length(l) > 0)
landmarkIdxX = l+1;
landmarkIdxY = l+2;
plot(g.x(landmarkIdxX), g.x(landmarkIdxY), '.or', 'markersize', 4);
end
if (length(p) > 0)
pIdxX = p+1;
pIdxY = ... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | read_graph.m | .m | SLAM-Algorithms-Octave-master/8_GraphSLAM/octave/tools/read_graph.m | 2,293 | utf_8 | 0630181c14990966fed786509ae5a85c | % read a g2o data file describing a 2D SLAM instance
function graph = read_graph(filename)
fid = fopen(filename, 'r');
graph = struct (
'x', [],
'edges', [],
'idLookup', struct
);
disp('Parsing File');
while true
ln = fgetl(fid);
if (ln == -1)
break;
end
tokens = strsplit(ln, ' ', true);
double_t... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawprobellipse.m | .m | SLAM-Algorithms-Octave-master/1_EKF_SLAM/octave/tools/drawprobellipse.m | 1,803 | utf_8 | 90c41a3bebf740e86100f47974753eb3 | %DRAWPROBELLIPSE Draw elliptic probability region of a Gaussian in 2D.
% DRAWPROBELLIPSE(X,C,ALPHA,COLOR) draws the elliptic iso-probabi-
% lity contour of a Gaussian distributed bivariate random vector X
% at the significance level ALPHA. The ellipse is centered at X =
% [x; y] where C is the associated 2x2 co... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawrobot.m | .m | SLAM-Algorithms-Octave-master/1_EKF_SLAM/octave/tools/drawrobot.m | 5,225 | utf_8 | 3dfed55ac85a746f0f7c2407e1880069 | %DRAWROBOT Draw robot.
% DRAWROBOT(X,COLOR) draws a robot at pose X = [x y theta] such
% that the robot reference frame is attached to the center of
% the wheelbase with the x-axis looking forward. COLOR is a
% [r g b]-vector or a color string such as 'r' or 'g'.
%
% DRAWROBOT(X,COLOR,TYPE) draws a robot of t... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | chi2invtable.m | .m | SLAM-Algorithms-Octave-master/1_EKF_SLAM/octave/tools/chi2invtable.m | 231,909 | utf_8 | d16aef6be089f46039e76c200f7577d8 | %CHI2INVTABLE Lookup table of the inverse of the chi-square cdf.
% X = CHI2INVTABLE(P,V) returns the inverse of the chi-square cumu-
% lative distribution function (cdf) with V degrees of freedom at
% the value P. The chi-square cdf with V degrees of freedom, is
% the gamma cdf with parameters V/2 and 2.
%
... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawellipse.m | .m | SLAM-Algorithms-Octave-master/1_EKF_SLAM/octave/tools/drawellipse.m | 994 | utf_8 | c0100a4cf263e6e87026b3214221e84d | %DRAWELLIPSE Draw ellipse.
% DRAWELLIPSE(X,A,B,COLOR) draws an ellipse at X = [x y theta]
% with half axes A and B. Theta is the inclination angle of A,
% regardless if A is smaller or greater than B. COLOR is a
% [r g b]-vector or a color string such as 'r' or 'g'.
%
% H = DRAWELLIPSE(...) returns the graphi... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | add_landmark_to_map.m | .m | SLAM-Algorithms-Octave-master/3_UKF_SLAM/octave/tools/add_landmark_to_map.m | 2,007 | utf_8 | 618ae778ad57b5aff7d749d25ba196d4 | % Add a landmark to the UKF.
% We have to compute the uncertainty of the landmark given the current state
% (and its uncertainty) of the newly observed landmark. To this end, we also
% employ the unscented transform to propagate Q (sensor noise) through the
% current state
function [mu, sigma, map] = add_landmark_to_m... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawprobellipse.m | .m | SLAM-Algorithms-Octave-master/3_UKF_SLAM/octave/tools/drawprobellipse.m | 1,803 | utf_8 | 90c41a3bebf740e86100f47974753eb3 | %DRAWPROBELLIPSE Draw elliptic probability region of a Gaussian in 2D.
% DRAWPROBELLIPSE(X,C,ALPHA,COLOR) draws the elliptic iso-probabi-
% lity contour of a Gaussian distributed bivariate random vector X
% at the significance level ALPHA. The ellipse is centered at X =
% [x; y] where C is the associated 2x2 co... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawrobot.m | .m | SLAM-Algorithms-Octave-master/3_UKF_SLAM/octave/tools/drawrobot.m | 5,225 | utf_8 | 3dfed55ac85a746f0f7c2407e1880069 | %DRAWROBOT Draw robot.
% DRAWROBOT(X,COLOR) draws a robot at pose X = [x y theta] such
% that the robot reference frame is attached to the center of
% the wheelbase with the x-axis looking forward. COLOR is a
% [r g b]-vector or a color string such as 'r' or 'g'.
%
% DRAWROBOT(X,COLOR,TYPE) draws a robot of t... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | chi2invtable.m | .m | SLAM-Algorithms-Octave-master/3_UKF_SLAM/octave/tools/chi2invtable.m | 231,909 | utf_8 | d16aef6be089f46039e76c200f7577d8 | %CHI2INVTABLE Lookup table of the inverse of the chi-square cdf.
% X = CHI2INVTABLE(P,V) returns the inverse of the chi-square cumu-
% lative distribution function (cdf) with V degrees of freedom at
% the value P. The chi-square cdf with V degrees of freedom, is
% the gamma cdf with parameters V/2 and 2.
%
... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawellipse.m | .m | SLAM-Algorithms-Octave-master/3_UKF_SLAM/octave/tools/drawellipse.m | 994 | utf_8 | c0100a4cf263e6e87026b3214221e84d | %DRAWELLIPSE Draw ellipse.
% DRAWELLIPSE(X,A,B,COLOR) draws an ellipse at X = [x y theta]
% with half axes A and B. Theta is the inclination angle of A,
% regardless if A is smaller or greater than B. COLOR is a
% [r g b]-vector or a color string such as 'r' or 'g'.
%
% H = DRAWELLIPSE(...) returns the graphi... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | apply_odometry_correction.m | .m | SLAM-Algorithms-Octave-master/7_Odom_Calib_LeastSquares/octave/apply_odometry_correction.m | 418 | utf_8 | a210c7952b7d9183c51353841c6fe8b3 | % computes a calibrated vector of odometry measurements
% by applying the bias term to each line of the measurements
% X: 3x3 matrix obtained by the calibration process
% U: Nx3 matrix containing the odometry measurements
% C: Nx3 matrix containing the corrected odometry measurements
function C = apply_odometry_cor... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | compute_trajectory.m | .m | SLAM-Algorithms-Octave-master/7_Odom_Calib_LeastSquares/octave/compute_trajectory.m | 802 | utf_8 | 26eb49b61536e09b12d1ed166d79123a | % computes the trajectory of the robot by chaining up
% the incremental movements of the odometry vector
% U: a Nx3 matrix, each row contains the odoemtry ux, uy utheta
% T: a (N+1)x3 matrix, each row contains the robot position (starting from 0,0,0)
function T = compute_trajectory(U)
% initialize the trajectory matr... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | ls_calibrate_odometry.m | .m | SLAM-Algorithms-Octave-master/7_Odom_Calib_LeastSquares/octave/ls_calibrate_odometry.m | 1,774 | utf_8 | f44f15274cb082108a6b07ae374162e3 | % this function solves the odometry calibration problem
% given a measurement matrix Z.
% We assume that the information matrix is the identity
% for each of the measurements
% Every row of the matrix contains
% z_i = [u'x, u'y, u'theta, ux, uy, ytheta]
% Z: The measurement matrix
% X: the calibration matrix
% returns ... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | t2v.m | .m | SLAM-Algorithms-Octave-master/7_Odom_Calib_LeastSquares/octave/tools/t2v.m | 122 | utf_8 | 869378bf4d6409006dc9681e45aecdbb | #computes the pose vector v from an homogeneous transform A
function v=t2v(A)
v = [A(1:2,3); atan2(A(2,1),A(1,1))];
end
|
github | kiran-mohan/SLAM-Algorithms-Octave-master | v2t.m | .m | SLAM-Algorithms-Octave-master/7_Odom_Calib_LeastSquares/octave/tools/v2t.m | 165 | utf_8 | bd190805c2c8033bb7843a4c3559f866 | #computes the homogeneous transform matrix A of the pose vector v
function A=v2t(v)
c=cos(v(3));
s=sin(v(3));
A=[c, -s, v(1);
s, c, v(2);
0 0 1 ];
end
|
github | kiran-mohan/SLAM-Algorithms-Octave-master | resample.m | .m | SLAM-Algorithms-Octave-master/5_Particle_Filters/octave/resample.m | 1,111 | utf_8 | cf799595decd8c4c7b28ee1f97d51400 | % resample the set of particles.
% A particle has a probability proportional to its weight to get
% selected. A good option for such a resampling method is the so-called low
% variance sampling, Probabilistic Robotics pg. 109
function newParticles = resample(particles)
numParticles = length(particles);
w = [particles... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | resample.m | .m | SLAM-Algorithms-Octave-master/6_FastSLAM/octave/tools/resample.m | 1,264 | utf_8 | d5f805465ccb86ff9b4315695ffaa07c | % resample the set of particles.
% A particle has a probability proportional to its weight to get
% selected. A good option for such a resampling method is the so-called low
% variance sampling, Probabilistic Robotics pg. 109
function newParticles = resample(particles)
numParticles = length(particles);
w = [particles... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawprobellipse.m | .m | SLAM-Algorithms-Octave-master/6_FastSLAM/octave/tools/drawprobellipse.m | 1,803 | utf_8 | 90c41a3bebf740e86100f47974753eb3 | %DRAWPROBELLIPSE Draw elliptic probability region of a Gaussian in 2D.
% DRAWPROBELLIPSE(X,C,ALPHA,COLOR) draws the elliptic iso-probabi-
% lity contour of a Gaussian distributed bivariate random vector X
% at the significance level ALPHA. The ellipse is centered at X =
% [x; y] where C is the associated 2x2 co... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawrobot.m | .m | SLAM-Algorithms-Octave-master/6_FastSLAM/octave/tools/drawrobot.m | 5,225 | utf_8 | 3dfed55ac85a746f0f7c2407e1880069 | %DRAWROBOT Draw robot.
% DRAWROBOT(X,COLOR) draws a robot at pose X = [x y theta] such
% that the robot reference frame is attached to the center of
% the wheelbase with the x-axis looking forward. COLOR is a
% [r g b]-vector or a color string such as 'r' or 'g'.
%
% DRAWROBOT(X,COLOR,TYPE) draws a robot of t... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | measurement_model.m | .m | SLAM-Algorithms-Octave-master/6_FastSLAM/octave/tools/measurement_model.m | 1,025 | utf_8 | 4a0ad5fabced752df762d7390cdab378 | % compute the expected measurement for a landmark
% and the Jacobian with respect to the landmark
function [h, H] = measurement_model(particle, z)
% extract the id of the landmark
landmarkId = z.id;
% two 2D vector for the position (x,y) of the observed landmark
landmarkPos = particle.landmarks(landmarkId).mu;
% TODO... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | chi2invtable.m | .m | SLAM-Algorithms-Octave-master/6_FastSLAM/octave/tools/chi2invtable.m | 231,909 | utf_8 | d16aef6be089f46039e76c200f7577d8 | %CHI2INVTABLE Lookup table of the inverse of the chi-square cdf.
% X = CHI2INVTABLE(P,V) returns the inverse of the chi-square cumu-
% lative distribution function (cdf) with V degrees of freedom at
% the value P. The chi-square cdf with V degrees of freedom, is
% the gamma cdf with parameters V/2 and 2.
%
... |
github | kiran-mohan/SLAM-Algorithms-Octave-master | drawellipse.m | .m | SLAM-Algorithms-Octave-master/6_FastSLAM/octave/tools/drawellipse.m | 994 | utf_8 | c0100a4cf263e6e87026b3214221e84d | %DRAWELLIPSE Draw ellipse.
% DRAWELLIPSE(X,A,B,COLOR) draws an ellipse at X = [x y theta]
% with half axes A and B. Theta is the inclination angle of A,
% regardless if A is smaller or greater than B. COLOR is a
% [r g b]-vector or a color string such as 'r' or 'g'.
%
% H = DRAWELLIPSE(...) returns the graphi... |
github | ryan-mcginnis/IMU-orientation-master | get_orientation_optim.m | .m | IMU-orientation-master/get_orientation_optim.m | 3,690 | utf_8 | fdeee78811855640f9dded127e46e880 | function Rinf = get_orientation_optim(time, a, w, ind)
%Function to determine orientation of IMU. Does so in two steps: 1) Define
%initial orientation of device based on direction of gravity and 2) Define
%orientation thereafter by fusing acceleration and angular velocity
%estimates via optimization. Method assumes ... |
github | ryan-mcginnis/IMU-orientation-master | get_orientation_optim_quaternion.m | .m | IMU-orientation-master/get_orientation_optim_quaternion.m | 3,726 | utf_8 | 766f8469bab9e2da83322db24666fb80 | function qinf = get_orientation_optim_quaternion(time, a, w, ind)
%Function to determine orientation of IMU. Does so in two steps: 1) Define
%initial orientation of device based on direction of gravity and 2) Define
%orientation thereafter by fusing acceleration and angular velocity
%estimates via optimization. Meth... |
github | inter0509/NmfClustering-master | NMF.m | .m | NmfClustering-master/NMF.m | 1,091 | utf_8 | 5347ed70d65a10ac4dab7366e53f893b | %######################################################%
%## ##%
%## ##%
%## ##%
%######################################################%
function [W,H] = NMF(V,r,maxiter)
%... |
github | inter0509/NmfClustering-master | kmeans.m | .m | NmfClustering-master/kmeans.m | 870 | utf_8 | 0b088bd7144c0807e8cc49e9a3322f6d | %######################################################%
%## ##%
%## ##%
%## ##%
%######################################################%
function label = kmeans(fea, k)
% K... |
github | davidsonic/face_classification_ccbr2016-master | TestDQT.m | .m | face_classification_ccbr2016-master/lomo+boost/Testing/TestDQT.m | 784 | utf_8 | 492519b93e41b58a50059196956ce146 | function score = TestDQT(tree, x)
% function to test the learned DQT based weak classifier.
n = size(x,1);
score = zeros(n,1,'single');
if isempty(x)
score(:) = repmat(tree.fit, size(x,1), 1);
else
score = TestSubTree(tree, x, 0);
end
end
function score = TestSubTree(tree, x, node)
if isempt... |
github | davidsonic/face_classification_ccbr2016-master | LOMO.m | .m | face_classification_ccbr2016-master/lomo+boost/Testing/LOMO.m | 11,278 | utf_8 | a111bc172e5e35431a08316d99e72aab | function descriptors = LOMO(images, options)
%% function Descriptors = LOMO(images, options)
% Function for the Local Maximal Occurrence (LOMO) feature extraction
%
% Input:
% <images>: a set of n RGB color images. Size: [h, w, 3, n]
% [optioins]: optional parameters. A structure containing any of the
% fo... |
github | davidsonic/face_classification_ccbr2016-master | TestDQT.m | .m | face_classification_ccbr2016-master/lomo+boost/Training/src/TestDQT.m | 784 | utf_8 | 492519b93e41b58a50059196956ce146 | function score = TestDQT(tree, x)
% function to test the learned DQT based weak classifier.
n = size(x,1);
score = zeros(n,1,'single');
if isempty(x)
score(:) = repmat(tree.fit, size(x,1), 1);
else
score = TestSubTree(tree, x, 0);
end
end
function score = TestSubTree(tree, x, node)
if isempt... |
github | davidsonic/face_classification_ccbr2016-master | LOMO.m | .m | face_classification_ccbr2016-master/lomo+boost/Training/src/LOMO.m | 10,822 | utf_8 | 34422414f1e5950111a14be12cd73a0e | function descriptors = LOMO(images, options)
%% function Descriptors = LOMO(images, options)
% Function for the Local Maximal Occurrence (LOMO) feature extraction
%
% Input:
% <images>: a set of n RGB color images. Size: [h, w, 3, n]
% [optioins]: optional parameters. A structure containing any of the
% fo... |
github | davidsonic/face_classification_ccbr2016-master | LearnGAB.m | .m | face_classification_ccbr2016-master/lomo+boost/Training/src/LearnGAB.m | 9,694 | utf_8 | a826d5e3f8372dc2c6ea799b27027700 | function [model, negPassIndex, posFx, negFx] = LearnGAB(posX, negX, model, options)
%% [model, negPassIndex, posFx, negFx] = LearnGAB(posX, negX, model, options)
% Train a soft cascade based Gentle AdaBoost classifier, with deep quadratic
% tree (DQT) based weak classifiers.
%
% Input:
% <posX>: features of th... |
github | davidsonic/face_classification_ccbr2016-master | TrainDetector.m | .m | face_classification_ccbr2016-master/lomo+boost/Training/src/TrainDetector.m | 8,605 | utf_8 | d948156b02d64900a520dc45a25f6ec1 | function model = TrainDetector(faceDBFile, nonfaceDBFile, outFile, options)
%% function model = TrainDetector(faceDBFile, nonfaceDBFile, outFile, options)
% Train a Nomalized Pixel Difference (NPD) based face detector.
%
% Input:
% <faceDBFile>: MAT file for the face images. It contains an array FaceDB
% of s... |
github | davidsonic/face_classification_ccbr2016-master | voc_eval.m | .m | face_classification_ccbr2016-master/Proposal_Extraction_Code/py-faster-2/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | Deafro/FirmwareAoASS-master | ellipsoid_fit.m | .m | FirmwareAoASS-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 | obartra/ssim-master | scale_quality_maps.m | .m | ssim-master/assets/iw-ssim/scale_quality_maps.m | 1,500 | utf_8 | cdb7b7096712493ff2db1e2a2eb73cfa | %contrast-structure similarity map and squared error map for each scale, and luminance similarity map for the coarsest scale
function [cs_map l_map se_map]= scale_quality_maps(pyro,pyrd,pind,Nsc,K,L,win)
if (nargin < 3 | nargin > 7)
cs_map = -Inf;
l_map = -Inf;
se_map = -Inf;
disp... |
github | obartra/ssim-master | info_content_weight_map.m | .m | ssim-master/assets/iw-ssim/info_content_weight_map.m | 4,070 | utf_8 | 3e15430df8e53d3cc0e8744238d5e6d4 | %compute information content weight map for Scale 1 to Nsc-1
function [iw_map]= info_content_weight_map(pyro,pyrd,pind,Nsc,parent,blSzX,blSzY,sigma_nsq)
tol = 1e-15;
if (~exist('Nsc'))
Nsc = size(pind, 1);
end
if (~exist('parent'))
parent = 1; % include parent neighbor
end
if (~exist('blSzX'))
blSz... |
github | samhelmholtz/skinny-dip-master | ANMI_analytical_11.m | .m | skinny-dip-master/experiments/scripts/ANMI_analytical_11.m | 3,864 | utf_8 | 1d59c59f3ecf82d483676d7fb7a87822 | %Program for calculating the Adjusted Mutual Information (AMI) between
%two clusterings, tested on Matlab 7.0 (R14)
%(C) Nguyen Xuan Vinh 2008-2009
%Contact: n.x.vinh@unsw.edu.au
% vthesniper@yahoo.com
%--------------------------------------------------------------------------
%*Input: cluster label of t... |
github | ShadenSmith/splatt-master | make.m | .m | splatt-master/matlab/make.m | 1,277 | utf_8 | ff9b52d8ba937bd87c1fb846633e82b5 | % Adapted from MetisMEX
function make
% octave uses mkoctfile instead of mex
if(exist('OCTAVE_VERSION', 'builtin') ~= 0)
make_octave;
return;
end
c = computer;
switch c
case 'MACI64'
mex splatt_load.c -I../include -L../build/Darwin-x86_64/lib ...
-lsplatt -lgomp -lmwlapack -lmwblas -lm
... |
github | Selmaan/NMF-Source-Extraction-master | lars_regression_noise.m | .m | NMF-Source-Extraction-master/utilities/lars_regression_noise.m | 7,333 | utf_8 | 0dad59080c800f0ee51ade925dc16c93 | function [Ws, lambdas, W_lam, lam, flag] = lars_regression_noise(Y, X, positive, noise)
% run LARS for regression problems with LASSO penalty, with optional positivity constraints
% Author: Eftychios Pnevmatikakis. Adapted code from Ari Pakman
% Input Parameters:
% Y: Y(:,t) is the observed data at time ... |
github | Selmaan/NMF-Source-Extraction-master | kde.m | .m | NMF-Source-Extraction-master/utilities/kde.m | 7,076 | utf_8 | 3a33931f2a89111a29b1562146eb9249 | function [bandwidth,density,xmesh,cdf]=kde(data,n,MIN,MAX)
% Reliable and extremely fast kernel density estimator for one-dimensional data;
% Gaussian kernel is assumed and the bandwidth is chosen automatically;
% Unlike many other implementations, this one is immune to problems
% caused by multimo... |
github | Selmaan/NMF-Source-Extraction-master | plot_components_GUI.m | .m | NMF-Source-Extraction-master/utilities/plot_components_GUI.m | 7,800 | utf_8 | d9fbde3a0051a190ec9c227cac27b3e7 | %%
function plot_components_GUI(Y,A,C,b,f,Cn,options)
memmaped = isobject(Y);
defoptions = CNMFSetParms;
if nargin < 7 || isempty(options); options = []; end
if ~isfield(options,'d1') || isempty(options.d1); d1 = input('What is the total number of rows? \n'); else d1 = options.d1; end % # of rows
if ~isfield(... |
github | Selmaan/NMF-Source-Extraction-master | greedyROI.m | .m | NMF-Source-Extraction-master/utilities/greedyROI.m | 12,618 | utf_8 | d6595302c3a7bf11a4997a2b7b817ad6 | function [Ain, Cin, b_in, f_in, center, res] = greedyROI(Y, K, params, ROI_list)
% component initialization using a greedy algorithm to identify neurons in 2d or 3d calcium imaging movies
%
% Usage: [Ain, Cin, bin, fin, center, res] = greedyROI2d(data, K, params)
%
% Input:
% Y d1 x d2 x (d3 x) T movie, ra... |
github | Selmaan/NMF-Source-Extraction-master | ROI_GUI.m | .m | NMF-Source-Extraction-master/utilities/ROI_GUI.m | 9,275 | utf_8 | d31846c4089f804170a9dc1a2710a2c2 | function varargout = ROI_GUI(varargin)
% ROI_GUI MATLAB code for ROI_GUI.template_fig
% ROI_GUI, by itself, creates a new ROI_GUI or raises the existing
% singleton*.
%
% H = ROI_GUI returns the handle to a new ROI_GUI or the handle to
% the existing singleton*.
%
% ROI_GUI('CALLBACK',hObject,e... |
github | Selmaan/NMF-Source-Extraction-master | greedyROI_corr.m | .m | NMF-Source-Extraction-master/utilities/greedyROI_corr.m | 9,085 | utf_8 | ec8c6add53b2f19276e297fd7a6cfe6f | function [Ain, Cin, bin, fin, center, res] = greedyROI_corr(Y, K, options, sn, debug_on, save_avi)
%% a greedy method for detecting ROIs and initializing CNMF. in each iteration,
% it searches the one with large (peak-median)/noise level and large local
% correlation
%% Input:
% Y: d X T matrx, imaging data
% K: ... |
github | Selmaan/NMF-Source-Extraction-master | subdir.m | .m | NMF-Source-Extraction-master/utilities/subdir.m | 3,733 | utf_8 | 00ecbfe501a10bbea84b9fbaaaaf5f8e | function varargout = subdir(varargin)
%SUBDIR Performs a recursive file search
%
% subdir
% subdir(name)
% files = subdir(...)
%
% This function performs a recursive file search. The input and output
% format is identical to the dir function.
%
% Input variables:
%
% name: pathname or filename for search, can be a... |
github | Selmaan/NMF-Source-Extraction-master | signalExtraction.m | .m | NMF-Source-Extraction-master/utilities/signalExtraction.m | 3,642 | utf_8 | a7fb1981bbffe9d3ffc5168d7f17f7ad | function [ inferred, filtered, raw ] = signalExtraction(Y,A,C,b,f,d1,d2,extractControl)
% this code extract the signal after CNMF is ran
% inputs: Y raw data (d X T matrix, d # number of pixels, T # of timesteps)
% A matrix of spatial components (d x K matrix, K # of components)
% C matrix of temporal... |
github | Selmaan/NMF-Source-Extraction-master | sourceClusterGUI.m | .m | NMF-Source-Extraction-master/SC_util/sourceClusterGUI.m | 6,315 | utf_8 | 62de363c3b7cc7876fd86560b801fe91 | function varargout = sourceClusterGUI(varargin)
% SOURCECLUSTERGUI MATLAB code for sourceClusterGUI.fig
% SOURCECLUSTERGUI, by itself, creates a new SOURCECLUSTERGUI or raises the existing
% singleton*.
%
% H = SOURCECLUSTERGUI returns the handle to a new SOURCECLUSTERGUI or the handle to
% the exis... |
github | Selmaan/NMF-Source-Extraction-master | extractSourcesNMF.m | .m | NMF-Source-Extraction-master/SC_util/extractSourcesNMF.m | 10,572 | utf_8 | 47b00d38cedaca060053d050dcddeb8a | function extractSourcesNMF(acqObj,nSlice,data,initImages)
syncObj = acqObj.syncInfo;
acqBlocks = [1 syncObj.sliceFrames(1,nSlice)];
for blockNum = 2:size(syncObj.sliceFrames,1)
acqBlocks(blockNum,:) = ...
[1+syncObj.sliceFrames(blockNum-1,nSlice), syncObj.sliceFrames(blockNum,nSlice)];
end
memMap = matfil... |
github | Selmaan/NMF-Source-Extraction-master | harvey_constrained_oasisAR1.m | .m | NMF-Source-Extraction-master/SC_util/harvey_constrained_oasisAR1.m | 14,031 | utf_8 | 93f86a531530dbfe38cc2311f64caa36 | function [c, s, b, g, lam, active_set] = harvey_constrained_oasisAR1(y, g, sn, optimize_b,...
optimize_g, decimate, maxIter, tau_range)
% This is a lightly modified version of the constrained-AR1 deconvolution code
% It has been altered to include 'GetSn' and 'oasisAR1' functions, to
% requires an initial decay pa... |
github | Selmaan/NMF-Source-Extraction-master | sc_constrained_oasisAR1.m | .m | NMF-Source-Extraction-master/SC_util/sc_constrained_oasisAR1.m | 13,316 | utf_8 | 0660c4e757415c488be87a5681392275 | function [c, s, b, g, lam, active_set, miter] = sc_constrained_oasisAR1(y, g, sn, optimize_b,...
optimize_g, decimate, maxIter)
%% Infer the most likely discretized spike train underlying an AR(1) fluorescence trace
% Solves the sparse non-negative deconvolution problem
% min 1/2|c-y|^2 + lam |s|_1 subject to s_t ... |
github | Selmaan/NMF-Source-Extraction-master | cvx_version.m | .m | NMF-Source-Extraction-master/cvx/cvx_version.m | 14,459 | utf_8 | 9f358480cc5d66caa274c9d5bd5ed0de | function varargout = cvx_version( varargin )
% CVX_VERSION Returns version and environment information for CVX.
%
% When called with no arguments, CVX_VERSION prints out version and
% platform information that is needed when submitting CVX bug reports.
%
% This function is also used internally to return use... |
github | Selmaan/NMF-Source-Extraction-master | cvx_grbgetkey.m | .m | NMF-Source-Extraction-master/cvx/cvx_grbgetkey.m | 19,096 | utf_8 | 080162e4fd27b14ea8387362148db7d1 | function success = cvx_grbgetkey( kcode, overwrite )
% CVX_GRBGETKEY Retrieves and saves a Gurobi/CVX license.
%
% This function is used to install Gurobi license keys for use in CVX. It
% is called with your Gurobi license code as a string argument; e.g.
%
% cvx_grbgetkey xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
% ... |
github | Selmaan/NMF-Source-Extraction-master | HSDNTcorr.m | .m | NMF-Source-Extraction-master/cvx/sdpt3/HSDSolver/HSDNTcorr.m | 1,001 | utf_8 | c42eba1c6bae660b88921b7c8747490e | %%************************************************************************
%% HSDNTcorr: corrector step for the NT direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%************************************************************************
f... |
github | Selmaan/NMF-Source-Extraction-master | HSDHKMdirfun.m | .m | NMF-Source-Extraction-master/cvx/sdpt3/HSDSolver/HSDHKMdirfun.m | 1,551 | utf_8 | 1034e25e48a42d2fa143f93f47961fe9 | %%*******************************************************************
%% HSDHKMdirfun: compute (dX,dZ), given dy, for the HKM direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%****************************************************************... |
github | Selmaan/NMF-Source-Extraction-master | HSDsqlp.m | .m | NMF-Source-Extraction-master/cvx/sdpt3/HSDSolver/HSDsqlp.m | 11,860 | utf_8 | 00b8311a8efbee36662ca9288870a1cd | %%*****************************************************************************
%% HSDsqlp: solve an semidefinite-quadratic-linear program
%% by infeasible path-following method on the homogeneous self-dual model.
%%
%% [obj,X,y,Z,info,runhist] =
%% HSDsqlp(blk,At,C,b,OPTIONS,X0,y0,Z0);
%%
%% Input: blk: a cel... |
github | Selmaan/NMF-Source-Extraction-master | HSDsortA.m | .m | NMF-Source-Extraction-master/cvx/sdpt3/HSDSolver/HSDsortA.m | 2,577 | utf_8 | 0a74ddbb8a0c79bf22592d780d865e06 | %%*********************************************************************
%% sortA: sort columns of At{p} in ascending order according to the
%% number of nonzero elements.
%%
%% [At,C,b,X0,Z0,permA,permZ] = sortA(blk,At,C,b,X0,Z0);
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tut... |
github | Selmaan/NMF-Source-Extraction-master | HSDHKMrhsfun.m | .m | NMF-Source-Extraction-master/cvx/sdpt3/HSDSolver/HSDHKMrhsfun.m | 2,666 | utf_8 | 16409ae4672f80ef54a33c31ef30000f | %%*******************************************************************
%% HSDHKMrhsfun: compute the right-hand side vector of the
%% Schur complement equation for the HKM direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*****... |
github | Selmaan/NMF-Source-Extraction-master | HSDsqlpcheckconvg.m | .m | NMF-Source-Extraction-master/cvx/sdpt3/HSDSolver/HSDsqlpcheckconvg.m | 6,249 | utf_8 | a579e4972fd77d5cc3e11b72bf56d3a9 | %%*****************************************************************************
%% HSDsqlpcheckconvg: check convergence.
%%
%% ZpATynorm, AX, normX, normZ are with respect to the
%% original variables, not the HSD variables.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last ... |
github | Selmaan/NMF-Source-Extraction-master | HSDNTdirfun.m | .m | NMF-Source-Extraction-master/cvx/sdpt3/HSDSolver/HSDNTdirfun.m | 1,459 | utf_8 | a045827a3ca1adcf8806cfd8234ad5e4 | %%*******************************************************************
%% HSDNTdirfun: compute (dX,dZ), given dy, for the NT direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%******************************************************************... |
github | Selmaan/NMF-Source-Extraction-master | HSDNTrhsfun.m | .m | NMF-Source-Extraction-master/cvx/sdpt3/HSDSolver/HSDNTrhsfun.m | 3,424 | utf_8 | 02348c55d691a53b023639b8103757be | %%*******************************************************************
%% HSDNTrhsfun: compute the right-hand side vector of the
%% Schur complement equation for the NT direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*********... |
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