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github | petercorke/machinevision-toolbox-matlab-master | localmax.m | .m | machinevision-toolbox-matlab-master/localmax.m | 1,141 | utf_8 | 7e88532471f844494f48db571e3c3333 |
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Li... |
github | petercorke/machinevision-toolbox-matlab-master | cmfxyz.m | .m | machinevision-toolbox-matlab-master/cmfxyz.m | 13,285 | utf_8 | 7f51aebb2023d46dad551f7a99748733 | %CMFXYZ Color matching function
%
% The color matching function is the XYZ tristimulus required to match a
% particular wavelength excitation.
%
% XYZ = CMFXYZ(LAMBDA) is the CIE XYZ color matching function (Nx3) for illumination
% at wavelength LAMBDA (Nx1) [m]. If LAMBDA is a vector then each row of XYZ
% is the co... |
github | petercorke/machinevision-toolbox-matlab-master | ransac.m | .m | machinevision-toolbox-matlab-master/ransac.m | 9,894 | utf_8 | 70402432a3321489a5677573a469f5be | %RANSAC Random sample and consensus
%
% M = RANSAC(FUNC, X, T, OPTIONS) is the RANSAC algorithm that robustly fits
% data X to the model represented by the function FUNC. RANSAC classifies
% Points that support the model as inliers and those that do not as outliers.
%
% X typically contains corresponding point data, ... |
github | petercorke/machinevision-toolbox-matlab-master | pnmfilt.m | .m | machinevision-toolbox-matlab-master/pnmfilt.m | 2,341 | utf_8 | 44ff352fdef933a1e5ca94fdd172c243 | %PNMFILT Pipe image through PNM utility
%
% OUT = PNMFILT(CMD) runs the external program given by the string CMD
% and the output (assumed to be PNM format) is returned as OUT.
%
% OUT = PNMFILT(CMD, IM) pipes the image IM through the external program
% given by the string CMD and the output is returned as OUT. The ex... |
github | petercorke/machinevision-toolbox-matlab-master | houghpeaks.m | .m | machinevision-toolbox-matlab-master/houghpeaks.m | 4,059 | utf_8 | 8dd7bb64b66c93e511a80dc37ae2b16b | %HOUGHPEAKS Find Hough accumulator peaks.
%
% p = houghpeaks(H, N, hp)
%
% Returns the coordinates of N peaks from the Hough
% accumulator. The highest peak is found, refined to subpixel precision,
% then hp.radius radius around that point is zeroed so as to eliminate
% multiple close minima. The process is rep... |
github | petercorke/machinevision-toolbox-matlab-master | savepcd.m | .m | machinevision-toolbox-matlab-master/savepcd.m | 4,547 | utf_8 | b6fc9de72f9c31f773ed98eb76072db1 | %SAVEPCD Write a point cloud to file in PCD format
%
% SAVEPCD(FNAME, P) writes the point cloud P to the file FNAME as an
% as a PCD format file.
%
% SAVEPCD(FNAME, P, 'binary') as above but save in binary format. Default
% is ascii format.
%
% If P is a 2-dimensional matrix (MxN) then the columns of P represent the
%... |
github | petercorke/machinevision-toolbox-matlab-master | testpattern.m | .m | machinevision-toolbox-matlab-master/testpattern.m | 5,030 | utf_8 | 44514006d7a1b3dda8d0720cab2a46cb | %TESTPATTERN Create test images
%
% IM = TESTPATTERN(TYPE, D, ARGS) creates a test pattern image. If D is a
% scalar the image is DxD else D=[W H] the image is WxH. The image is specified by the
% string TYPE and one or two (type specific) arguments:
%
% 'rampx' intensity ramp from 0 to 1 in the x-direction. ARGS... |
github | petercorke/machinevision-toolbox-matlab-master | colorize.m | .m | machinevision-toolbox-matlab-master/colorize.m | 1,930 | utf_8 | faa045fa05532d6c6e3eab14031ae163 | %COLORIZE Colorize a greyscale image
%
% OUT = COLORIZE(IM, MASK, COLOR) is a color image where each pixel in OUT
% is set to the corresponding element of the greyscale image IM or a specified
% COLOR according to whether the corresponding value of MASK is true or false
% respectively. The color is specified as a 3-v... |
github | petercorke/machinevision-toolbox-matlab-master | mkline.m | .m | machinevision-toolbox-matlab-master/mkline.m | 1,637 | utf_8 | b917735cf0f2e260e081d5661eb9714f | %MKLINE Draw a line in a matrix
%
% m = MKLINE(n, theta, c)
% m = MKLINE(n, theta, c, val)
%
% m = MKLINE(im, theta, c)
% m = MKLINE(im, theta, c, val)
%
% First form creates an NxN matrix of zeros and draws a line
% with vertical intercept C and angle THETA. With the Xaxis to the left
% and Yaxis downw... |
github | petercorke/machinevision-toolbox-matlab-master | ilabel.m | .m | machinevision-toolbox-matlab-master/ilabel.m | 9,854 | utf_8 | f9c007085b3d69b9637c2b9595212909 | %ILABEL Label an image
%
% L = ILABEL(IM) is a label image that indicates connected components within
% the image IM (HxW). Each pixel in L (HxW) is an integer label that indicates
% which connected region the corresponding pixel in IM belongs to. Region
% labels are in the range 1 to M.
%
% [L,M] = ILABEL(IM) as abo... |
github | petercorke/machinevision-toolbox-matlab-master | vgg_rq.m | .m | machinevision-toolbox-matlab-master/vgg_rq.m | 1,178 | utf_8 | 915f5d6742debb42f5553acf1975a6bd | % [R,Q] = vgg_rq(S) Just like qr but the other way around.
%
% If [R,Q] = vgg_rq(X), then R is upper-triangular, Q is orthogonal, and X==R*Q.
% Moreover, if S is a real matrix, then det(Q)>0.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% ... |
github | petercorke/machinevision-toolbox-matlab-master | loadspectrum.m | .m | machinevision-toolbox-matlab-master/loadspectrum.m | 1,940 | utf_8 | e196f17c9b5ff49214a3a2cee18ef9ad | %LOADSPECTRUM Load spectrum data
%
% S = LOADSPECTRUM(LAMBDA, FILENAME) is spectral data (NxD) from file FILENAME
% interpolated to wavelengths [metres] specified in LAMBDA (Nx1). The
% spectral data can be scalar (D=1) or vector (D>1) valued.
%
% [S,LAMBDA] = LOADSPECTRUM(LAMBDA, FILENAME) as above but also returns ... |
github | petercorke/machinevision-toolbox-matlab-master | cie_primaries.m | .m | machinevision-toolbox-matlab-master/cie_primaries.m | 982 | utf_8 | 0e77c64d0e1c45a3254891c942c65412 | %CIE_PRIMARIES Define CIE primary colors
%
% P = CIE_PRIMARIES() is a 3-vector with the wavelengths [m] of the
% CIE 1976 red, green and blue primaries respectively.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can ... |
github | petercorke/machinevision-toolbox-matlab-master | icorner.m | .m | machinevision-toolbox-matlab-master/icorner.m | 10,340 | utf_8 | 6bf4d9e61780e394cd402156bf0bb41c | %ICORNER Corner detector
%
% F = ICORNER(IM, OPTIONS) is a vector of PointFeature objects describing
% corner features detected in the image IM. This is a non-scale space detector
% and by default the Harris method is used but Shi-Tomasi and Noble are also
% supported.
%
% If IM is an image sequence a cell array of P... |
github | petercorke/machinevision-toolbox-matlab-master | iprofile.m | .m | machinevision-toolbox-matlab-master/iprofile.m | 1,869 | utf_8 | 67dbcdd6bd4ddcd307fd8779865508ae | %IPROFILE Extract pixels along a line
%
% V = IPROFILE(IM, P1, P2) is a vector of pixel values extracted from the
% image IM (HxWxP) between the points P1 (2x1) and P2 (2x1). V (NxP) has
% one row for each point along the line and the row is the pixel value
% which will be a vector for a multi-plane image.
%
% [P,UV... |
github | petercorke/machinevision-toolbox-matlab-master | tristim2cc.m | .m | machinevision-toolbox-matlab-master/tristim2cc.m | 2,007 | utf_8 | 914e73559b933a7871a12c0b1853335e | %TRISTIM Tristimulus to chromaticity coordinates
%
% CC = TRISTIM2CC(TRI) is the chromaticity coordinate (1x2) corresponding to the
% tristimulus TRI (1x3). If TRI is RGB then CC is rg, if TRI is XYZ then
% CC is xy. Multiple tristimulus values can be given as rows of TRI (Nx3)
% in which case the chromaticity coord... |
github | petercorke/machinevision-toolbox-matlab-master | loadpgm.m | .m | machinevision-toolbox-matlab-master/loadpgm.m | 3,301 | utf_8 | 5d09cad685d1d4a2ac7a7e26c1d435e6 | %LOADPGM Load a PGM image
%
% I = loadpgm(filename)
%
% Returns a matrix containing the image loaded from the PGM format
% file filename. Handles ASCII (P2) and binary (P5) PGM file formats.
%
% If the filename has no extension, and open fails, a '.pgm' will
% be appended. If the file cannot be opened it returns [].
... |
github | petercorke/machinevision-toolbox-matlab-master | max2d.m | .m | machinevision-toolbox-matlab-master/max2d.m | 1,438 | utf_8 | c95f9dcd88935c333a59626ce08eda2d | %MAX2d Maximum of image
%
% [r,c] = max2d(image)
%
% Return the interpolated coordinates (r,c) of the greatest peak in image.
%
% SEE ALSO: ihough xyhough
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribu... |
github | petercorke/machinevision-toolbox-matlab-master | csubtract.m | .m | machinevision-toolbox-matlab-master/csubtract.m | 1,106 | utf_8 | 7c3877d4c119c34a13a20378cde34508 | %CSUBTRACT Subtract two angles on a circle
%
% d = csubtract(th1, th2)
%
% Subtract two angles and return a result that is always in the range
% [-pi pi).
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistrib... |
github | petercorke/machinevision-toolbox-matlab-master | camcald.m | .m | machinevision-toolbox-matlab-master/camcald.m | 2,287 | utf_8 | 8848e0d80809bbb7599ab8594f7080e5 | % CAMCALD Camera calibration from data points
%
% C = CAMCALD(D) is the camera matrix (3x4) determined by least squares
% from corresponding world and image-plane points. D is a table
% of points with rows of the form [X Y Z U V] where (X,Y,Z) is the
% coordinate of a world point and [U,V] is the corresponding imag... |
github | petercorke/machinevision-toolbox-matlab-master | showpixels.m | .m | machinevision-toolbox-matlab-master/showpixels.m | 5,106 | utf_8 | 4f188c2b132fd73e05eaaa969a71b482 | %SHOWPIXELS Show low resolution image
%
% Displays a low resolution image in detail as a grid with colored lines
% between pixels and numeric display of pixel values at each pixel. Useful
% for illustrating principles in teaching.
%
% Options::
% 'fmt',F Format string (defaults to %d or %.2f depending on imag... |
github | petercorke/machinevision-toolbox-matlab-master | anaglyph.m | .m | machinevision-toolbox-matlab-master/anaglyph.m | 3,425 | utf_8 | 5d663f0afb213da20789f4d7932a99e4 | %ANAGLYPH Convert stereo images to an anaglyph image
%
% A = ANAGLYPH(LEFT, RIGHT) is an anaglyph image where the two images of
% a stereo pair are combined into a single image by coding them in two
% different colors. By default the left image is red, and the right
% image is cyan.
%
% ANAGLYPH(LEFT, RIGHT) as abov... |
github | petercorke/machinevision-toolbox-matlab-master | ihist.m | .m | machinevision-toolbox-matlab-master/ihist.m | 4,261 | utf_8 | c008de90bffdc2a415346135b8b7b482 | %IHIST Image histogram
%
% IHIST(IM, OPTIONS) displays the image histogram. For an image with multiple
% planes the histogram of each plane is given in a separate subplot.
%
% H = IHIST(IM, OPTIONS) is the image histogram as a column vector. For
% an image with multiple planes H is a matrix with one column per image... |
github | petercorke/machinevision-toolbox-matlab-master | im2col.m | .m | machinevision-toolbox-matlab-master/im2col.m | 1,761 | utf_8 | b89db329756334cd9b86e71fadcba493 | %IM2COL Convert an image to pixel per row format
%
% OUT = IM2COL(IM) is a matrix (NxP) where each row represents a single
% of the image IM (HxWxP). The pixels are in image column order (ie. column
% 1, column 2 etc) and there are N=WxH rows.
%
% OUT = IM2COL(IM, MASK) as above but only includes pixels if:
% - the co... |
github | petercorke/machinevision-toolbox-matlab-master | rluminos.m | .m | machinevision-toolbox-matlab-master/rluminos.m | 1,446 | utf_8 | a1888fbb27996829f1c1975d5d4488e3 | %RLUMINOS Relative photopic luminosity function
%
% P = RLUMINOS(LAMBDA) is the relative photopic luminosity function for the
% wavelengths in LAMBDA [m]. If LAMBDA is a vector (Nx1), then P (Nx1) is a
% vector whose elements are the luminosity at the corresponding elements
% of LAMBDA.
%
% Relative luminosity lies ... |
github | petercorke/machinevision-toolbox-matlab-master | ilut.m | .m | machinevision-toolbox-matlab-master/ilut.m | 2,758 | utf_8 | 42c875d5dfc6433aef2a0c15fc082552 | %ILUT Apply lookup table to image
%
% OUT = ILUT(IM, LUT) is an image the same size as IM (NxMxP) where each
% pixel value is mapped through the lookup table LUT (Kx1).
%
% OUT = ILUT(IM, LUT) is an image (NxMxP) formed by mapping the image
% of index values IM (NxM) through the lookup table LUT (KxP). An input
% pi... |
github | petercorke/machinevision-toolbox-matlab-master | colordistance.m | .m | machinevision-toolbox-matlab-master/colordistance.m | 1,737 | utf_8 | a7d24bb5f09ca566145fce8c61f2cd30 | %COLORDISTANCE Colorspace distance
%
% D = COLORDISTANCE(IM, RG) is the Euclidean distance on the rg-chromaticity
% plane from coordinate RG=[r,g] to every pixel in the color image IM. D is
% an image with the same dimensions as IM and the value of each pixel is
% the color space distance of the corresponding pixel... |
github | petercorke/machinevision-toolbox-matlab-master | kmeans.m | .m | machinevision-toolbox-matlab-master/kmeans.m | 6,690 | utf_8 | 46be2b989f40450024d3e8301b71b503 | %KMEANS K-means clustering
%
% [L,C] = KMEANS(X, K, OPTIONS) is a K-means clustering of multi-dimensional
% data points X (DxN) where N is the number of points, and D is the dimension.
% The data is organized into K clusters based on Euclidean distance from cluster
% centres C (DxK). L is a vector (Nx1) whose elements... |
github | petercorke/machinevision-toolbox-matlab-master | xy2rgb.m | .m | machinevision-toolbox-matlab-master/xy2rgb.m | 923 | utf_8 | 39f62167aa798c006a34a81609b2dd79 |
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Li... |
github | petercorke/machinevision-toolbox-matlab-master | iscalespace.m | .m | machinevision-toolbox-matlab-master/iscalespace.m | 2,379 | utf_8 | c3c4ec0ad46cd3c03fe4404502f41912 | %ISCALESPACE Scale-space image sequence
%
% [G,L,S] = ISCALESPACE(IM, N, SIGMA) is a scale space image sequence of
% length N derived from IM (HxW). The standard deviation of the smoothing
% Gaussian is SIGMA. At each scale step the variance of the Gaussian increases
% by SIGMA^2. The first step in the sequence i... |
github | petercorke/machinevision-toolbox-matlab-master | iwarp.m | .m | machinevision-toolbox-matlab-master/iwarp.m | 691 | utf_8 | 5bbef1222fc8195201d0c86bf16f3e1e | %IWARP Generalized warp
%
% OUT = IWARP(IM, U, V) is a warped image (WxH) corresponding to
% IM (WxH). The output pixels are taken from IM at the coordinates given
% by U and V (both WxH). That is, the value of OUT(I,J) is given by the
% value of IM(X,Y) where X=U(I,J) and Y=V(I,J). In general X and Y are
% not inte... |
github | petercorke/machinevision-toolbox-matlab-master | cmfrgb.m | .m | machinevision-toolbox-matlab-master/cmfrgb.m | 2,646 | utf_8 | 6b878d154358705698f4c0ba01ce9f02 | %CMFRGB RGB color matching function
%
% The color matching function is the RGB tristimulus required to match a
% particular spectral excitation.
%
% RGB = CMFRGB(LAMBDA) is the CIE color matching function (Nx3) for illumination
% at wavelength LAMBDA (Nx1) [m]. If LAMBDA is a vector then each row of RGB
% is the colo... |
github | petercorke/machinevision-toolbox-matlab-master | rectify3.m | .m | machinevision-toolbox-matlab-master/rectify3.m | 7,302 | utf_8 | ea174df1777db1186dd5b083ac761a33 |
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Li... |
github | petercorke/machinevision-toolbox-matlab-master | support.m | .m | machinevision-toolbox-matlab-master/@SurfPointFeature/support.m | 1,423 | utf_8 | 30127ee0f9bf954e80b0d05a53c241c9 | %SurfPointFeature.support Support region of feature
%
% OUT = F.support(IM, W) is an image of the support region of the
% feature F, extracted from the image IM in which the feature appears.
% The support region is scaled to WxW and rotated so that the feature's
% orientation axis is upward.
%
% OUT = F.support(IMAGES... |
github | petercorke/machinevision-toolbox-matlab-master | linefit.m | .m | machinevision-toolbox-matlab-master/demos/linefit.m | 1,210 | utf_8 | a2a23a520248b9d645797627ba3fb663 | function [out,resid] = linefit(xy)
if isstruct(xy)
out = ransac_driver(xy);
return;
end
% data is passed with points in columns
x = xy(1,:)'; y = xy(2,:)';
out = [x ones(size(x))] \ y;
resid = max(abs(y - [x ones(size(x))]*out));
end
function out = ransac_dri... |
github | petercorke/machinevision-toolbox-matlab-master | morphdemo.m | .m | machinevision-toolbox-matlab-master/demos/morphdemo.m | 3,805 | utf_8 | 06203ae6a89580e9054ea47b772d6ce8 | %MORPHDEMO Demonstrate morphology using animation
%
% MORPHDEMO(IM, SE, OPTIONS) displays an animation to show the principles
% of the mathematical morphology operations dilation or erosion. Two
% windows are displayed side by side, input binary image on the left and
% output image on the right. The structuring eleme... |
github | petercorke/machinevision-toolbox-matlab-master | checkbuttonpress.m | .m | machinevision-toolbox-matlab-master/demos/checkbuttonpress.m | 342 | utf_8 | 4360d4e536530cf05c0443c957b13d81 | classdef checkbuttonpress < handle
properties
press = false;
end
methods
function cb = checkbuttonpress
set(gcf, 'WindowButtonDownFcn', @(src, event) button_callback(src, event, cb));
end
end
end
function button_callback(src, event, cb)
... |
github | petercorke/machinevision-toolbox-matlab-master | lensanim.m | .m | machinevision-toolbox-matlab-master/demos/lensanim.m | 2,716 | utf_8 | fa85ed29a56241839614f123d5c648ee | function lensanim
% draw(4)
% return
res = 100;
anim = Animate('lensanim');
for zo=[9:-0.05:2]
draw(zo);
set(gcf,'PaperPositionMode','auto')
% oldscreenunits = get(gcf,'Units');
% oldpaperunits = get(gcf,'PaperUnits');
% oldpaperpo... |
github | petercorke/machinevision-toolbox-matlab-master | convdemo.m | .m | machinevision-toolbox-matlab-master/demos/convdemo.m | 2,940 | utf_8 | 3c057d18dfb462983fd19b0c0193a6f1 | %CONVDEMO Demonstrate correlation using animation
%
% CONVHDEMO(IM, K, OPTIONS) displays an animation to show the principles
% of the image correlation. Two windows are displayed side by side, input
% binary image on the left and output image on the right.
% The kernel moves over the input image and is colored red.... |
github | petercorke/machinevision-toolbox-matlab-master | plot_camera.m | .m | machinevision-toolbox-matlab-master/@Camera/plot_camera.m | 3,613 | utf_8 | 65f61b161d170887030cf5bfdbf736d6 | %Camera.plot_camera Display camera icon in world view
%
% C.plot_camera(OPTIONS) draw a camera as a simple 3D model in the current
% figure.
%
% Options::
% 'pose',T Camera displayed in pose T (homogeneous transformation 4x4)
% 'scale',S Overall scale factor (default 0.2 x maximum axis dimension)
% 'color',C ... |
github | petercorke/machinevision-toolbox-matlab-master | iblobs.m | .m | machinevision-toolbox-matlab-master/IPT/iblobs.m | 7,639 | utf_8 | 0ca0bd1abb46f01a2c65c1db38caf615 | %IBLOBS Blob features
%
% F = IBLOBS(IM, OPTIONS) is a vector of RegionFeature objects that
% describe each connected region in the image IM.
%
% Options::
% 'aspect',A set pixel aspect ratio, default 1.0
% 'connect',C set connectivity, 4 (default) or 8
% 'greyscale' compute greyscale moments 0 (def... |
github | petercorke/machinevision-toolbox-matlab-master | hitormiss.m | .m | machinevision-toolbox-matlab-master/IPT/hitormiss.m | 1,327 | utf_8 | acae348a717f2e4446687ca93cb40c4d | %HITORMISS Hit or miss transform
%
% H = HITORMISS(IM, SE) is the hit-or-miss transform of the binary image IM with
% the structuring element SE. Unlike standard morphological operations S has
% three possible values: 0, 1 and don't care (represented by NaN).
%
% References::
% - Robotics, Vision & Control, Section 1... |
github | petercorke/machinevision-toolbox-matlab-master | ilabeltest.m | .m | machinevision-toolbox-matlab-master/examples/ilabeltest.m | 981 | utf_8 | 43dea1022e496ffe77d1e9901b730c45 |
% Copyright (C) 1995-2009, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Lic... |
github | petercorke/machinevision-toolbox-matlab-master | sphere_rotate.m | .m | machinevision-toolbox-matlab-master/examples/sphere_rotate.m | 2,244 | utf_8 | 6ea790327382e3454032aaeaa4577807 |
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Li... |
github | petercorke/machinevision-toolbox-matlab-master | sphere_paint.m | .m | machinevision-toolbox-matlab-master/examples/sphere_paint.m | 1,337 | utf_8 | 534502f4aca690960549aa224ef812d3 |
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Li... |
github | petercorke/machinevision-toolbox-matlab-master | ilabelTest.m | .m | machinevision-toolbox-matlab-master/unit_test/ilabelTest.m | 8,732 | utf_8 | a5838206c086b43ee31de988dee1288b | function tests = iLabelTest
tests = functiontests(localfunctions);
clc
end
function pattern1_test(tc)
p = [
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 1 1 1 0 0 0 0
0 0 0 1 1 1 0 0 0 0
0 0 0 1 1 1 0 0 0 0
0 0 0 0 0 0 0... |
github | petercorke/machinevision-toolbox-matlab-master | morphTest.m | .m | machinevision-toolbox-matlab-master/unit_test/morphTest.m | 7,718 | utf_8 | cceebb399f6a6a3a76a47e9ac046e14b | function tests = MorphTest(testCase)
tests = functiontests(localfunctions);
end
function imorph1_test(testCase)
in = [1 2; 3 4];
se = 1;
verifyEqual(testCase, imorph(in, se, 'min'), in);
verifyEqual(testCase, imorph(in, se, 'max'), in);
verifyEqual(testCase, imorph(in, se, 'min', 'replicate'... |
github | petercorke/machinevision-toolbox-matlab-master | LineFeatureTest.m | .m | machinevision-toolbox-matlab-master/unit_test/LineFeatureTest.m | 3,420 | utf_8 | 8017d5045ee8b6fefa669cdf407aeb0c | function tests = LineFeatureTest
tests = functiontests(localfunctions)
clc
end
function setupOnce(tc)
im = testpattern('squares', 256, 256, 128);
im = irotate(im, -0.3);
edges = icanny(im);
tc.TestData.edges = edges;
end
function teardownOnce(tc)
close all
end
function constructor_te... |
github | petercorke/machinevision-toolbox-matlab-master | colorTest.m | .m | machinevision-toolbox-matlab-master/unit_test/colorTest.m | 1,649 | utf_8 | 9cd2e04fec8beb8bd857f3bdd7bee395 | function tests = ColorTest(testCase)
tests = functiontests(localfunctions);
clc
end
function teardownOnce(testCase)
close all
end
function colorname_test(testCase)
rgb = colorname('skyblue');
verifyEqual(testCase, rgb, [0 0.541176470588235 1], 'AbsTol', 1e-6);
xy = colorname('skyblue', 'x... |
github | petercorke/machinevision-toolbox-matlab-master | VisualServoTest.m | .m | machinevision-toolbox-matlab-master/unit_test/VisualServoTest.m | 1,195 | utf_8 | 301fd03a8af1a405d52cc16b35d67b5c | function VisualServoTest(testCase)
tests = functiontests(localfunctions);
end
function pbvs_test(testCase)
cam = CentralCamera('default');
Tc0 = transl(1,1,-3)*trotz(0.6);
TcStar_t = transl(0, 0, 1);
pbvs = PBVS(cam, 'T0', Tc0, 'Tf', TcStar_t);
pbvs
pbvs.run
pbvs.plot_p
pbvs.... |
github | petercorke/machinevision-toolbox-matlab-master | ioTest.m | .m | machinevision-toolbox-matlab-master/unit_test/ioTest.m | 2,240 | utf_8 | d785014777e54aa8adef915da16e5998 | function tests = ioTest(tc)
tests = functiontests(localfunctions);
end
function teardownOnce(tc)
close all
end
function fileio_test(tc)
% iread
z = iread('lena.png');
tc.verifyTrue(iscolor(z));
tc.verifyTrue( isa(z, 'uint8'));
z = iread('lena.pgm', 'double');
tc.verifyFalse(iscolor(z)... |
github | petercorke/machinevision-toolbox-matlab-master | cameraTest.m | .m | machinevision-toolbox-matlab-master/unit_test/cameraTest.m | 4,490 | utf_8 | d91bf4262743cadb22ad92d743ad9f1d | function VisualServoTest(testCase)
tests = functiontests(localfunctions);
end
function testFunctions
%BundleAdjust.testFunctions Test generated functions
%
% BundleAdjust.testFunctions will run a batch of unit tests on the
% functions generated by BundleAdj... |
github | petercorke/machinevision-toolbox-matlab-master | RegionFeatureTest.m | .m | machinevision-toolbox-matlab-master/unit_test/RegionFeatureTest.m | 5,519 | utf_8 | bd3bec8df60e3d075d4c02022a96f688 | function tests = RegionFeatureTest
tests = functiontests(localfunctions);
clc
end
function teardownOnce(tc)
close all
end
function thresh_test(tc)
castle = iread('castle.png', 'double', 'grey');
ithresh(castle);
t = otsu(castle);
t = niblack(castle, -0.2, 35);
tc.verifyEqual(size(t), ... |
github | petercorke/machinevision-toolbox-matlab-master | PointFeatureTest.m | .m | machinevision-toolbox-matlab-master/unit_test/PointFeatureTest.m | 2,058 | utf_8 | b97a10a61d612ac3ae5fc95311165232 | function PointFeatureTest(testCase)
tests = functiontests(localfunctions);
end
function Corner_test(testCase)
b1 = iread('building2-1.png', 'grey', 'double');
C = icorner(b1, 'nfeat', 200);
verifyEqual(testCase, length(C), 200);
ss = char(C);
C(1:5).u;
C(1:5).v;
C(1:5).strength;
d... |
github | petercorke/machinevision-toolbox-matlab-master | utilityTest.m | .m | machinevision-toolbox-matlab-master/unit_test/utilityTest.m | 17,180 | utf_8 | b38f46dc6de4d820ba3816a923504fff | function tests = utilityTest(tc)
tests = functiontests(localfunctions);
clc
end
function idouble_test(tc)
% test for uint8
tc.verifyEqual( idouble( cast(0, 'uint8')), 0);
tc.verifyEqual( idouble( cast(128, 'uint8')), 128/255);
tc.verifyEqual( idouble( cast(255, 'uint8')), 1);
% test for ui... |
github | petercorke/machinevision-toolbox-matlab-master | support.m | .m | machinevision-toolbox-matlab-master/@SiftPointFeature/support.m | 1,352 | utf_8 | 84c515e52432cf3f7a7a590758268add | %SiftPointFeature.support Support region of feature
%
% OUT = F.support(IM, W) is an image of the support region of the
% feature F, extracted from the image IM in which the feature appears.
% The support region is scaled to WxW and rotated so that the feature's
% orientation axis is upward.
%
% OUT = F.support(IMAGES... |
github | petercorke/machinevision-toolbox-matlab-master | showpose.m | .m | machinevision-toolbox-matlab-master/@SphericalCamera/showpose.m | 1,594 | utf_8 | c9fb2d86e23d1454586f49125125ad83 |
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the Li... |
github | petercorke/machinevision-toolbox-matlab-master | sph2.m | .m | machinevision-toolbox-matlab-master/@SphericalCamera/sph2.m | 8,790 | utf_8 | d6e92bb94db5bc9e81fc84f25f3d8720 | %SPH Implement spherical IBVS for point features
%
% results = sph(T)
% results = sph(T, params)
%
% Simulate IBVS with for a square target comprising 4 points is placed
% in the world XY plane. The camera/robot is initially at pose T and is
% driven to the orgin.
%
% Two windows are shown and animated:
% 1.... |
github | petercorke/machinevision-toolbox-matlab-master | plot_camera.m | .m | machinevision-toolbox-matlab-master/@SphericalCamera/plot_camera.m | 2,479 | utf_8 | c5de02ed09b68538e871fb82af7ea580 | %SphericalCamera.plot_camera Display camera icon in world view
%
% C.plot_camera(T) draws the spherical image plane (unit sphere) at pose given by
% the SE3 object T.
%
% C.plot_camera(T, P) as above but also display world points, given by the
% columns of P (3xN), as small spheres.
%
%
% Reference::
%
% "Spherical ima... |
github | petercorke/machinevision-toolbox-matlab-master | sph.m | .m | machinevision-toolbox-matlab-master/@SphericalCamera/sph.m | 8,677 | utf_8 | d7f91e876104ae16710e0d1740e0adb8 | %SPH Implement spherical IBVS for point features
%
% results = sph(T)
% results = sph(T, params)
%
% Simulate IBVS with for a square target comprising 4 points is placed
% in the world XY plane. The camera/robot is initially at pose T and is
% driven to the orgin.
%
% Two windows are shown and animated:
% 1.... |
github | petercorke/machinevision-toolbox-matlab-master | visjac_p.m | .m | machinevision-toolbox-matlab-master/@SphericalCamera/visjac_p.m | 2,086 | utf_8 | 64e25adb6227e5867d34e2d914f67952 | %SphericalCamera.visjac_p Visual motion Jacobian for point feature
%
% J = C.visjac_p(PT, Z) is the image Jacobian (2Nx6) for the image plane
% points PT (2xN) described by phi (longitude) and theta (colatitude).
% The depth of the points from the camera is given by Z which is a scalar,
% for all points, or a vector (... |
github | petercorke/machinevision-toolbox-matlab-master | find_isolated_matches.m | .m | machinevision-toolbox-matlab-master/matching/find_isolated_matches.m | 1,957 | utf_8 | b6280c693aa5a25c83af852ea8222d94 | %FIND_ISOLATED_MATCHES find isolated matches in disparity image
%
% [I,S] = find_isolated_matches(D,neighbourhood,threshold)
%
% I = map of isolated matches (0 = isolated, 1 not isolated)
% S = difference between disparity and average disparity
% D = disparity map
% neighbourhood = [x y] area to be averaged
% threshold... |
github | petercorke/machinevision-toolbox-matlab-master | visjac_e.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/visjac_e.m | 2,205 | utf_8 | 16ad422f08d897daebbf1fc57372475a | %CentralCamera.visjac_e Visual motion Jacobian for point feature
%
% J = C.visjac_e(E, PL) is the image Jacobian (5x6) for the ellipse
% E (5x1) described by u^2 + E1v^2 - 2E2uv + 2E3u + 2E4v + E5 = 0. The
% ellipse lies in the world plane PL = (a,b,c,d) such that aX + bY + cZ + d = 0.
%
% The Jacobian gives the rate... |
github | petercorke/machinevision-toolbox-matlab-master | flowfield.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/flowfield.m | 1,486 | utf_8 | d67680b0ed03792f6b3be33d63e0a35c | %CentralCamera.flowfield Optical flow
%
% C.flowfield(V) displays the optical flow pattern for a sparse grid
% of points when the camera has a spatial velocity V (6x1).
%
% See also QUIVER.
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is... |
github | petercorke/machinevision-toolbox-matlab-master | estpose.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/estpose.m | 1,331 | utf_8 | e2fefca333c13afa8381f73a4cf3f6d1 | %CentralCamera.estpose Estimate pose from object model and camera view
%
% T = C.estpose(XYZ, UV) is an estimate of the pose of the object defined by
% coordinates XYZ (3xN) in its own coordinate frame. UV (2xN) are the
% corresponding image plane coordinates.
%
% Reference::
% "EPnP: An accurate O(n) solution to the... |
github | petercorke/machinevision-toolbox-matlab-master | visjac_l.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/visjac_l.m | 2,129 | utf_8 | 2ba4c058be758d3f5d917f4e7b77d406 | %CentralCamera.visjac_l Visual motion Jacobian for line feature
%
% J = C.visjac_l(L, PL) is the image Jacobian (2Nx6) for the image plane
% lines L (2xN). Each column of L is a line in theta-rho format, and the
% rows are theta and rho respectively.
%
% The lines all lie in the plane PL = (a,b,c,d) such that aX + b... |
github | petercorke/machinevision-toolbox-matlab-master | visjac_p.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/visjac_p.m | 2,061 | utf_8 | 432514ee83606a77d52db948636a2035 | %CentralCamera.visjac_p Visual motion Jacobian for point feature
%
% J = C.visjac_p(UV, Z) is the image Jacobian (2Nx6) for the image plane
% points UV (2xN). The depth of the points from the camera is given by Z
% which is a scalar for all points, or a vector (Nx1) of depth for each point.
%
% The Jacobian gives the... |
github | petercorke/machinevision-toolbox-matlab-master | visjac_p_polar.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/visjac_p_polar.m | 2,482 | utf_8 | 4efab5dffff8e30197bb10a4bf4970ae | %CentralCamera.visjac_p_polar Visual motion Jacobian for point feature
%
% J = C.visjac_p_polar(RT, Z) is the image Jacobian (2Nx6) for the image plane
% points RT (2xN) described in polar form, radius and theta. The depth of the
% points from the camera is given by Z which is a scalar for all point, or a
% vector ... |
github | petercorke/machinevision-toolbox-matlab-master | showpose.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/..@Camera/showpose.m | 1,745 | utf_8 | ff381da08167ce6200570274e9e4d00c | %SHOWPOSE Display a camera icon in 3D
%
% h = cam.showpose(T)
%
% Create a new camera at pose T, and return the graphics handle.
%
% The camera is depicted as a pyramid with the apex as the camera
% origin and the base plane normal to the optical axis.
% The sides are colored red, green, blue corresponding to the X,... |
github | petercorke/machinevision-toolbox-matlab-master | efficient_pnp_gauss.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/private/efficient_pnp_gauss.m | 8,268 | utf_8 | e7ac790cd5444efb8dcbb92a7412b51a | function [R,T,Xc,best_solution,opt]=efficient_pnp_gauss(x3d_h,x2d_h,A)
% EFFICIENT_PNP_GAUSS Main Function to solve the PnP problem
% as described in:
%
% Francesc Moreno-Noguer, Vincent Lepetit, Pascal Fua.
% Accurate Non-Iterative O(n) Solution to the PnP Problem.
% In Proceedings of... |
github | petercorke/machinevision-toolbox-matlab-master | invhomog.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/private/invhomog.m | 4,024 | utf_8 | 3012932f5882b83a79324dceae80a143 | %INVHOMOG Decompose an homography
%
% S = INVHOMOG(H, OPTIONS) decomposes the homography H (3x3)into the camera motion
% and the normal to the plane.
%
% In practice there are multiple solutions and S is a vector of structures
% with elements:
% T camera motion as a homogeneous transform matrix (4x4), translation n... |
github | petercorke/machinevision-toolbox-matlab-master | efficient_pnp.m | .m | machinevision-toolbox-matlab-master/@CentralCamera/private/efficient_pnp.m | 6,775 | utf_8 | 426ec34e6a739a0bea193903c324a5bf | function [R,T,Xc,best_solution]=efficient_pnp(x3d_h,x2d_h,A)
% EFFICIENT_PNP Main Function to solve the PnP problem
% as described in:
%
% Francesc Moreno-Noguer, Vincent Lepetit, Pascal Fua.
% Accurate Non-Iterative O(n) Solution to the PnP Problem.
% In Proceedings of ICCV, 2007.
%... |
github | williammortl/NHANES-master | simulateDay.m | .m | NHANES-master/simulateDay.m | 2,281 | utf_8 | b2b009b44b61300e7bd62e70d561ade4 | %%% Author: William Michael Mortl
%%% Feel free to use this code for educational purposes, any other use
%%% requires citations to: NHanes study, William Michael Mortl, and
%%% Sriram Sankaranaraynan
function [ returnedRegressorData ] = simulateDay( genderCode, age, BMI )
%%% function: simulateDay
%%% descript... |
github | williammortl/NHANES-master | histogram.m | .m | NHANES-master/histogram.m | 729 | utf_8 | c2b229ab2409db553759c2cc9dcdf9ca | %%% Author: William Michael Mortl
%%% Feel free to use this code for educational purposes, any other use
%%% requires citations to: William Michael Mortl, and
%%% Sriram Sankaranaraynan
function [meanVal, medianVal, modeVal, stdVal] = histogram(vals, numBins, titleStr, h)
%% remove NaN vals
I = find(i... |
github | williammortl/NHANES-master | weightedHistogram.m | .m | NHANES-master/weightedHistogram.m | 966 | utf_8 | 29b77fc2969d426e062701af5a09db62 | %%% Author: William Michael Mortl
%%% Feel free to use this code for educational purposes, any other use
%%% requires citations to: William Michael Mortl, and
%%% Sriram Sankaranaraynan
function [meanVal, medianVal, modeVal, stdVal] = weightedHistogram(valsAndWeights, numBins, titleStr, h)
%% remove NaN v... |
github | williammortl/NHANES-master | adjustForWeighting.m | .m | NHANES-master/adjustForWeighting.m | 1,014 | utf_8 | 45699ba43494ff75765f5e62b55182ea | %%% Author: William Michael Mortl
%%% Feel free to use this code for educational purposes, any other use
%%% requires citations to: NHanes study, William Michael Mortl, and
%%% Sriram Sankaranaraynan
function [dataOut] = adjustForWeighting(dataIn, weightCol)
%% init
[numVals, numAttributes] = size(dat... |
github | williammortl/NHANES-master | runMultipleSimulations.m | .m | NHANES-master/runMultipleSimulations.m | 2,575 | utf_8 | 3afadb18fecd8df67d24308b927c22d3 | %%% Author: William Michael Mortl
%%% Feel free to use this code for educational purposes, any other use
%%% requires citations to: NHanes study, William Michael Mortl, and
%%% Sriram Sankaranaraynan
function [matrixOfSimulations] = runMultipleSimulations(numSimulations)
%%% function: simulateDay
%%% descripti... |
github | williammortl/NHANES-master | consolidateFunc.m | .m | NHANES-master/consolidateFunc.m | 3,487 | utf_8 | 990d8e3a908e661e323d0304611df59d | %%% Author: William Michael Mortl
%%% Feel free to use this code for educational purposes, any other use
%%% requires citations to: NHanes study, William Michael Mortl,
%%% Sriram Sankaranaraynan, and Fraser Cameron
function [consData] = consolidateFunc(data, dataBMX, dataDemo, inputColID, inputColCarbs, input... |
github | ddtm/dl-course-master | voc_eval.m | .m | dl-course-master/Seminar6/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 | marshallcoz/turnt-octo-happiness-master | plotCircle3D.m | .m | turnt-octo-happiness-master/LayIBEM3d/plotCircle3D.m | 926 | utf_8 | 3c568e11220ce8061dd8b4dcfeac40b8 | function plotCircle3D(center,normal,radius,tipo)
theta=0:0.01:2*pi;
v=null(normal);
points=real(repmat(center',1,size(theta,2))+radius*(v(:,1)*cos(theta)+v(:,2)*sin(theta)));
if(tipo == 1) %contornos
plot3(points(1,:),points(2,:),points(3,:),'k-');
elseif(tipo == 2) %geometria
h=fill3(points(1,:),points(2,:),po... |
github | marshallcoz/turnt-octo-happiness-master | GijTij_omega.m | .m | turnt-octo-happiness-master/LayIBEM3d/GijTij_omega.m | 1,266 | utf_8 | 5d2d800b4278f6f6925b3fe4ecf208c7 | %% funcion de Green 3d Espacio completo analitica
function [G,T,nil] = GijTij_omega(m,f,p_x,pXi)
nil = zeros(1,f.nmax);
come = f.come;
r = distancia(p_x,pXi);
gamma = (p_x.center - pXi.center)./r;
n = p_x.normal;
k = come/m.beta;
q = come/m.alfa;
ba = m.beta/m.alfa;
ba2 = ba^2;
qr = q * r;
kr = k * r;
kr2 = kr^2;
f1 ... |
github | marshallcoz/turnt-octo-happiness-master | sismogramaA.m | .m | turnt-octo-happiness-master/LayIBEM3d/sismogramaA.m | 568 | utf_8 | e380d2cbe01c350082c08f7d5b814f3d | % La mitad de arriba del sismograma, con la frecuencia
function sismogramaA(iRecep,f_vars,s,comp,dirFza,center)
name = ['G' num2str(comp) num2str(dirFza) '_(' num2str(iRecep) ')'];
figure('Name',name);hold on%; set(gcf,'Visible', 'off');
subplot(2,1,1)
plot((0:f_vars.ntiempo-1).*f_vars.DFREC,real(s(comp,:)),'r');hold o... |
github | alcu/sms-master | hflip.m | .m | sms-master/concen_ring_traj/hflip.m | 1,527 | utf_8 | 4694167b4e1419b79c6ff43977068121 | % newarr = hflip(arr [,cp]);
%
% Flips the array about the horizontal axis.
%
% If cp is given, the array is flipped about the point
% cp, where 2*cp must be an integer.
%
% $Id: hflip.m,v 1.1 2010-10-21 13:54:53 jfnielse Exp $
% =============== CVS Log Messages ==========================
% This file is maintained i... |
github | alcu/sms-master | trapwave.m | .m | sms-master/concen_ring_traj/trapwave.m | 2,812 | utf_8 | ad43357c3b142ddac3588e4c405a332b | %
% function [waveform,t] = trapwave(area,T,gmax,smax)
%
% Function returns the minimum-duration waveform with
% the given area.
%
% INPUT:
% area = area in G*s/cm.
% T = gradient sampling period (s)
% gmax = maximum gradient amplitude (G/cm)
% smax = maximum gradient slew rate (G/cm/s)
%
% OUTPUT:
% waveform = gr... |
github | alcu/sms-master | ftmodsens_create3.m | .m | sms-master/recon/ftmodsens_create3.m | 2,656 | utf_8 | 23a7d79a1cdefdb44c2dc96c09a8ff27 | function ob = ftmodsens_create3(k, mod_kspace, c, fmap, ti, varargin)
% function ob = ftmodsens_create3(k, mod_kspace, c, fmap, ti, varargin)
% Constructs system fatrix2 for SENSE-like simultaneous multislice recon.
%
% Note about mask implementation: See fatrix2.m.
%
% Inputs:
% k Vector of complex k-spa... |
github | alcu/sms-master | mri_r2_fit.m | .m | sms-master/irt/mri/mri_r2_fit.m | 2,320 | utf_8 | 89304fd84ed299f366a4112e9cd4a4e2 | function r2 = mri_r2_fit(images, telist, varargin)
%|function r2 = mri_r2_fit(images, telist, varargin)
%|
%| fit R2=1/T2 maps to images with different echo times
%| in
%| images [(nd) nt] images with nt different echo times
%| telist [nt] echo times
%|
%| options
%| 'how' char 'log-ls' - ordinary LS fit to log dat... |
github | alcu/sms-master | mri_brainweb_params.m | .m | sms-master/irt/mri/mri_brainweb_params.m | 1,746 | utf_8 | aa58ad72c488c4920d87d0ccbbae5c9c | function f = mri_brainweb_params(label)
%|function f = mri_brainweb_params(label)
%|
%| tissue parameters for brainweb brain images, based on
%| http://mouldy.bic.mni.mcgill.ca/brainweb/tissue_mr_parameters.txt
%| units of t* are msec
%|
%| in
%| label {0,1,...,10} or 'fat', etc.
%| out
%| f.t1,t2,t2s,pd,label,name
... |
github | alcu/sms-master | mri_objects.m | .m | sms-master/irt/mri/mri_objects.m | 14,400 | utf_8 | bfc16d7b2b15fbc08baa9283948b5a28 | function st = mri_objects(varargin)
%|function st = mri_objects([options], 'type1', params1, 'type2', params2, ...)
%| Generate strum that describes image-domain objects and Fourier domain spectra
%| of simple structures such as rectangles, disks, superpositions thereof.
%| These functions are useful for simple "idea... |
github | alcu/sms-master | mri_kspace_spiral.m | .m | sms-master/irt/mri/mri_kspace_spiral.m | 7,460 | utf_8 | 11f16b4459a7d43315adcd0f8e56e5bc | function [kspace, omega] = mri_kspace_spiral(varargin)
%function [kspace, omega] = mri_kspace_spiral(varargin)
% k-space spiral trajectory based on GE 3T scanner constraints
% options (name / value pairs)
% N size of reconstructed image
% Nt # of time points
% fov field of view in cm
% dt time sampling interval
% out
... |
github | alcu/sms-master | mri_grid_linear.m | .m | sms-master/irt/mri/mri_grid_linear.m | 2,276 | utf_8 | 045a0d0c83dbeffc34b1fcccd05937ed | function [xhat, yhat, xg, kg] = mri_grid_linear(kspace, ydata, N, fov)
%|function [xhat, yhat, xg, kg] = mri_grid_linear(kspace, ydata, N, fov)
%| very crude "gridding" based on linear interpolation.
%| not recommended as anything but a straw man or perhaps
%| for initializing iterative methods.
%| in
%| kspace [M 2... |
github | alcu/sms-master | mri_density_comp.m | .m | sms-master/irt/mri/mri_density_comp.m | 7,715 | utf_8 | 0b76cf31dbbf1f41115372538164abcf | function wi = mri_density_comp(kspace, dtype, varargin)
%|function wi = mri_density_comp(kspace, dtype, varargin)
%|
%| fix: THIS NEEDS A LOT OF WORK!
%|
%| Compute density compensation factors for the conjugate phase
%| method for image reconstruction from Fourier samples.
%|
%| in
%| kspace [M 1] kspace sample loca... |
github | alcu/sms-master | mri_sensemap_sim.m | .m | sms-master/irt/mri/mri_sensemap_sim.m | 5,867 | utf_8 | 9d6a1397a7c8defe1100a9f741e52d2f | function smap = mri_sensemap_sim(varargin)
%|function smap = mri_sensemap_sim(varargin)
%|
%| Simulate sensitivity maps for sensitivity-encoded MRI
%| based grivich:00:tmf doi:10.1119/1.19461
%|
%| option
%| nx, ny, dx, dy, ncoil, rcoil, orbit (see below)
%| out
%| smap [nx ny ncoil] simulated sensitivity maps (compl... |
github | alcu/sms-master | mri_b1map_sliceselect.m | .m | sms-master/irt/mri/mri_b1map_sliceselect.m | 67,552 | utf_8 | f7f063983e3bb6b74e2058ecb00c838b | function [zmaps omaps cost] = mri_b1map_sliceselect(yy, alpha, varargin)
% function [zmaps omaps cost] = mri_b1map_sliceselect(yy, alpha, [options])
%
% Estimate "B1+ map" for each of ncoil coils
% from sequence of reconstructed images with ntip different nominal tip angles.
% Model:
% todo
% in
% yy [nx ny nmeasure... |
github | alcu/sms-master | mri_exp_approx.m | .m | sms-master/irt/mri/mri_exp_approx.m | 9,164 | utf_8 | 27c92ffff860b0fc79108efbec53cccb | function [B, C, hk, zk] = mri_exp_approx(ti, zmap, LL, varargin)
%|function [B, C, hk, zk] = mri_exp_approx(ti, zmap, LL, [options])
%|
%| Build approximations to exponentials for iterative MR image reconstruction,
%| generalizing "time segmentation" and "frequency segmentation" methods.
%| This is a key part of the ... |
github | alcu/sms-master | mri_field_map_reg.m | .m | sms-master/irt/mri/mri_field_map_reg.m | 6,901 | utf_8 | a366684b3979d15f8eeae01c8496a3af | function [wmap, wconv] = mri_field_map_reg(yik, etime, varargin)
%|function [wmap, wconv] = mri_field_map_reg(yik, etime, varargin)
%|
%| MRI field map estimation using regularization and optimization transfer,
%| based on a multiple image model:
%| y_ik = mag_i * phase_ik * decay_ik + noise_ik
%| phase_ik = exp(1i *... |
github | alcu/sms-master | mri_r2_fit_normalize.m | .m | sms-master/irt/mri/mri_r2_fit_normalize.m | 1,369 | utf_8 | e2909081f333009675769c7e4868d6eb | function [images kappa_typical] = mri_r2_fit_normalize(images, te, varargin)
%|function [images kappa_typical] = mri_r2_fit_normalize(images, te, varargin)
%|
%| Normalize images for R2=1/T2 mapping so that typical "kappa" is unity.
%|
%| in
%| images [(nd) nt] images with nt different echo times
%| te [nt] echo tim... |
github | alcu/sms-master | mri_trajectory.m | .m | sms-master/irt/mri/mri_trajectory.m | 8,306 | utf_8 | 227c6b670610267ff7947a193b164aba | function [kspace, omega, wi] = mri_trajectory(ktype, arg_traj, N, fov, arg_wi)
%|function [kspace, omega, wi] = mri_trajectory(ktype, arg_traj, N, fov, arg_wi)
%| generate kspace trajectory samples and density compensation functions.
%|
%| in
%| ktype string k-space trajectory type. see choices below.
%| arg_traj c... |
github | alcu/sms-master | ir_mri_sensemap_admm.m | .m | sms-master/irt/mri/ir_mri_sensemap_admm.m | 9,248 | utf_8 | 60d78c56732788b001e123a3e51ef95f | function [smap, sinit] = ir_mri_sensemap_admm(ykj, varargin)
%function [smap, sinit] = ir_mri_sensemap_admm(ykj, [options])
%|
%| Regularized estimation of sensitivity maps for parallel MRI
%| using augmented Lagrangian methods with variable splitting.
%| See M.J. Allison et al., IEEE TMI, 32(3), 556-564, Mar. 2013.
%... |
github | alcu/sms-master | mri_fun_approx.m | .m | sms-master/irt/mri/mri_fun_approx.m | 9,770 | utf_8 | 0f76917bb0e03bd312be5c7c39c00e4b | function [B, C, hk, wk] = mri_fun_approx(ti, uj, vj, f1, f2, varargin)
%|function [B, C, hk, wk] = mri_fun_approx(ti, uj, vj, f1, f2, [options])
%|
%| UNDER DEVELOPMENT
%| For certain iterative MR image reconstruction problems, we have
%| signals of the form f1(uj ti) f2(vj ti), for some functions f1() and f2(),
%| a... |
github | alcu/sms-master | ir_mri_sensemap_init.m | .m | sms-master/irt/mri/ir_mri_sensemap_init.m | 3,647 | utf_8 | d03a9a052defc25427ec89aa366c5b08 | function sinit = ir_mri_sensemap_init(init, ykj, bodycoil, sizeI, thresh, maskObj)
nx = sizeI(1);
ny = sizeI(2);
ncoil = sizeI(3);
if ~isempty(maskObj)
warning('using the maskObj instead of thresholding to determine good pixels');
end
if isempty(init) || ischar(init)
sinit = zeros(nx, ny, ncoil);
for ic... |
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