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value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | jacksky64/imageProcessing-master | interpretColor.m | .m | imageProcessing-master/Matlab Slicer/imStacks/+uiextras/interpretColor.m | 3,396 | utf_8 | ec1f7605145817838d2c9b712af4287d | function col = interpretColor(str)
%interpretColor Interpret a color as an RGB triple
%
% rgb = uiextras.interpretColor(col) interprets the input color COL and
% returns the equivalent RGB triple. COL can be one of:
% * RGB triple of floating point numbers in the range 0 to 1
% * RGB triple of UINT8 numb... |
github | jacksky64/imageProcessing-master | Container.m | .m | imageProcessing-master/Matlab Slicer/imStacks/+uiextras/Container.m | 22,989 | utf_8 | c9212141cf493e285730ee2a6af4d5c6 | classdef Container < hgsetget
%Container Container base class
%
% c = uiextras.Container() creates a new container object. Container
% is an abstract class and can only be constructed as the first
% actual of a descendent class.
%
% c = uiextras.Container(param,value,...) cre... |
github | jacksky64/imageProcessing-master | loadLayoutIcon.m | .m | imageProcessing-master/Matlab Slicer/imStacks/+uiextras/loadLayoutIcon.m | 3,144 | utf_8 | 978b9b2fbeb6c98ed1c9a5dd59865fca | function cdata = loadLayoutIcon(imagefilename,bgcol)
%loadLayoutIcon Load an icon and set the transparent color
%
% cdata = uiextras.loadLayoutIcon(filename) tries to load the icon specified by
% filename. If the icon is a PNG file with transparency then transparent
% pixels are set to NaN. If not, then any... |
github | jacksky64/imageProcessing-master | slicer.m | .m | imageProcessing-master/Matlab Slicer/imStacks/oldSlicer/slicer.m | 64,794 | utf_8 | 43e52862e3ac94765c312862049ce1bb | function varargout = slicer(varargin)
%SLICER Interactive visualization of 3D images
%
% SLICER is an graphical interface to explore slices of a 3D image.
% Index of the current slice is given under the slider, mouse position as
% well as cursor value are indicated when mouse is moved over image, and
% sc... |
github | jacksky64/imageProcessing-master | find_features.m | .m | imageProcessing-master/MatlabSIFT/find_features.m | 6,216 | utf_8 | 094478485c587da35b2ec95a2e4059a7 |
%/////////////////////////////////////////////////////////////////////////////////////////////
%
% find_features - scale space feature detector based upon difference of gaussian filters.
% selects features based upon their maximum response in scale space
%
% Usage: maxima = find_features(pyr, im... |
github | jacksky64/imageProcessing-master | plot_matched.m | .m | imageProcessing-master/MatlabSIFT/plot_matched.m | 718 | utf_8 | 8f4f810fdb7b9dffc21e850731443e63 | %
% Author:
% Scott Ettinger
% scott.m.ettinger@intel.com
%
% May 2002
%/////////////////////////////////////////////////////////////////////////////////////////////
function [] = plot_matched(p,w,img,num_flag)
if ~exist('num_flag')
num_flag = 0;
end
figure(gcf);
imagesc(img)
hold on
colormap g... |
github | jacksky64/imageProcessing-master | build_pyramid.m | .m | imageProcessing-master/MatlabSIFT/build_pyramid.m | 2,047 | utf_8 | a5763817edf3ff11db24c6b7f8c8124f | %/////////////////////////////////////////////////////////////////////////////////////////////
%
% build_pyramid - build scaled image pyramid and difference of gaussians pyramid
%
% Usage: [pyr,imp] = build_pyramid(img,levels,scl);
%
% Parameters:
%
% img : original image
% ... |
github | jacksky64/imageProcessing-master | construct_key.m | .m | imageProcessing-master/MatlabSIFT/construct_key.m | 817 | utf_8 | 4f74405436aafff224bcd89245dd5d5b |
function key = construct_key(px, py, img, sz)
pct = .75;
[h,w] = size(img);
[yoff,xoff] = meshgrid(-1:1,-1:1);
yoff = yoff(:)*pct;
xoff = xoff(:)*pct;
for i = 1:size(yoff,1)
ctrx = px + xoff(i)*sz*2; %method using interpolated values
ctry = py + yof... |
github | jacksky64/imageProcessing-master | motion_corr2.m | .m | imageProcessing-master/MatlabSIFT/motion_corr2.m | 4,570 | utf_8 | f87063db26fa6fcececf205f713cac11 | % MOTION_CORR - Computes a set of interest point correspondences
% between two successive frames in an image
% sequence. First, a Harris corner detector is used
% to choose interest points. Then, CORR is used to
% obtain a matching, using both geometric constrai... |
github | jacksky64/imageProcessing-master | getpts.m | .m | imageProcessing-master/MatlabSIFT/getpts.m | 6,081 | utf_8 | e4c6a997168907b5b857ddf6c8d0fbac |
%display features with sub-pixel and sub-scale accuracy
%Scott Ettinger
function [features] = getpts(img, pyr, scl,imp,pts,hood_size,radius,min_separation,edgeratio)
mcolor = [ 0 1 0; %color array for display of features at different scales
0 1 0;
1 0 0;
.2 .5 0;
... |
github | jacksky64/imageProcessing-master | resample_bilinear.m | .m | imageProcessing-master/MatlabSIFT/resample_bilinear.m | 1,248 | utf_8 | 2b3be967a23972ebbdbcdb98673578f1 | %/////////////////////////////////////////////////////////////////////////////////////////////
% Author : Scott Ettinger
%
% resample_bilinear(img, ratio)
%
% resamples a 2d matrix by the ratio given by the ratio parameter using bilinear interpolation
% the 1,1 entry of the matrix is always duplicated.
%/////... |
github | jacksky64/imageProcessing-master | filter_laplacian.m | .m | imageProcessing-master/MatlabSIFT/filter_laplacian.m | 1,803 | utf_8 | 8cca88c2df0ef869c1bc3dbd496262e3 | %/////////////////////////////////////////////////////////////////////////////////////////////
% Author : Scott Ettinger
%
% filter_gaussian(img, order, sig)
%
% The image is first padded with the outer image data enough times to allow for the size of the
% filter used.
function image_out = filter_gaussian... |
github | jacksky64/imageProcessing-master | match_dv_odometry.m | .m | imageProcessing-master/MatlabSIFT/match_dv_odometry.m | 392 | utf_8 | e49b9238207c097440046bded7eddc6f |
function od_out = match_dv_odometry(od_in,dv)
c = 1;
i = 1;
while i<size(dv,1) & c<size(od_in,1)
while od_in(c,1)<dv(i) & c<size(od_in,1) %find matching odometry measurement
c = c+1;
end
od_out(i,:) = od_in(c,:);
i=i+1;
end
... |
github | jacksky64/imageProcessing-master | detect_features.m | .m | imageProcessing-master/MatlabSIFT/detect_features.m | 3,146 | utf_8 | daa7ae4ed1d013fbbe23f8bd824affde |
%/////////////////////////////////////////////////////////////////////////////////////////////
%
% detect_features - scale space feature detector based upon difference of gaussian filters.
% selects features based upon their maximum response in scale space
%
% Usage: [features,pyr,imp,keys] = de... |
github | jacksky64/imageProcessing-master | find_extrema.m | .m | imageProcessing-master/MatlabSIFT/find_extrema.m | 2,761 | utf_8 | 63c3ac08500a3e375157d034a61ccc11 |
%/////////////////////////////////////////////////////////////////////////////////////////////
%
% find_extrema - finds local maxima within a grayscale image. Each point is
% checked against all of the pixels within a given radius to be a local max/min.
% The magnitude of pixel val... |
github | jacksky64/imageProcessing-master | filter_gaussian.m | .m | imageProcessing-master/MatlabSIFT/filter_gaussian.m | 1,539 | utf_8 | 8c018c4d76363cdb193b6ee5e49ca6a8 | %/////////////////////////////////////////////////////////////////////////////////////////////
% Author : Scott Ettinger
%
% filter_gaussian(img, order, sig)
%
% The image is first padded with the outer image data enough times to allow for the size of the
% filter used.
function image_out = filter_gaussian... |
github | jacksky64/imageProcessing-master | gauss2dx.m | .m | imageProcessing-master/MatlabSIFT/gauss2dx.m | 573 | utf_8 | 852c8ed3ce8569434a4da1e70ad4ee40 | %Author : Scott Ettinger
%Details:
%
%gauss2d(order, sig)
%
%Generates a normalized 2d matrix to use as a gaussian convolution filter
% order - size of filter matrix. Returns an order X order matrix
% sig - sigma value in gaussian equation
function f = gauss2dx(order,sig)
f=0;
i=0;
j=0;
%generate... |
github | jacksky64/imageProcessing-master | refine_features.m | .m | imageProcessing-master/MatlabSIFT/refine_features.m | 8,711 | utf_8 | bcf05884bf144765d706ddf4d1c707b2 | %/////////////////////////////////////////////////////////////////////////////////////////////
%
% refine_features - scale space feature detector based upon difference of gaussian filters.
% selects features based upon their maximum response in scale space
%
% Usage: features = refine_features(img... |
github | jacksky64/imageProcessing-master | plotpoints.m | .m | imageProcessing-master/MatlabSIFT/plotpoints.m | 1,035 | utf_8 | d4872923af1d87538633d8cac8642041 | %/////////////////////////////////////////////////////////////////////////////////////////////
%
% plotpoints - visualize features generated by detect_features
% Usage: plotpoints(p,img,num_flag)
%
% Parameters:
%
% img : original image
% p: vector of points
% ... |
github | jacksky64/imageProcessing-master | showfeatures.m | .m | imageProcessing-master/MatlabSIFT/showfeatures.m | 1,487 | utf_8 | b04b890bdf153576182207d6307c2af1 | %/////////////////////////////////////////////////////////////////////////////////////////////
%
% showfeatures - visualize features generated by detect_features
% Usage: showfeatures(features,img)
%
% Parameters:
%
% img : original image
% features: matrix generated b... |
github | jacksky64/imageProcessing-master | make_cost.m | .m | imageProcessing-master/MatlabSIFT/make_cost.m | 227 | utf_8 | d6ebc15f2e3ae829983736bf8d34646b |
function c = make_cost(k1, k2)
for i=1:size(k1,1)
for k=1:size(k2,1)
c(i,k) = sum((k1(i,:) - k2(k,:)).^2);
end
end
|
github | jacksky64/imageProcessing-master | motion_corr.m | .m | imageProcessing-master/MatlabSIFT/motion_corr.m | 6,466 | utf_8 | 2e27a037d9c354cc545b0c88be7a7648 | % MOTION_CORR - Computes a set of interest point correspondences
% between two successive frames in an image
% sequence. First, a Harris corner detector is used
% to choose interest points. Then, CORR is used to
% obtain a matching, using both geometric constrai... |
github | jacksky64/imageProcessing-master | skeleton.m | .m | imageProcessing-master/FastMarching_version3b/skeleton.m | 6,068 | utf_8 | bc89aea0d0615547c269a6f02eb57787 | function S=skeleton(I,verbose)
% This function Skeleton will calculate an accurate skeleton (centerlines)
% of an object represented by an binary image / volume using the fastmarching
% distance transform.
%
% S=skeleton(I,verbose)
%
% inputs,
% I : A 2D or 3D binary image
% verbose : Boolean, set to true (d... |
github | jacksky64/imageProcessing-master | msfm.m | .m | imageProcessing-master/FastMarching_version3b/msfm.m | 5,104 | utf_8 | 8166322eef83fa858c709f64c52df7ba | function [T,Y]=msfm(F, SourcePoints, UseSecond, UseCross)
% This function MSFM calculates the shortest distance from a list of
% points to all other pixels in an image volume, using the
% Multistencil Fast Marching Method (MSFM). This method gives more accurate
% distances by using second order derivatives and c... |
github | jacksky64/imageProcessing-master | msfm2d.m | .m | imageProcessing-master/FastMarching_version3b/functions/msfm2d.m | 11,010 | utf_8 | f96cf4a042008f8a5e6c2c2f847e3a67 | function [T,Y]=msfm2d(F, SourcePoints, usesecond, usecross)
% This function MSFM2D calculates the shortest distance from a list of
% points to all other pixels in an image, using the
% Multistencil Fast Marching Method (MSFM). This method gives more accurate
% distances by using second order derivatives and cros... |
github | jacksky64/imageProcessing-master | region_measurement.m | .m | imageProcessing-master/3dViewer/region_measurement.m | 10,315 | utf_8 | 3281727018491aae4fe7dcd5a14fe172 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright:
% Jun Tan
% University of Texas Southwestern Medical Center
% Department of Radiation Oncology
% Last edited: 08/19/2014
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function varargout = region_measurement(varargi... |
github | jacksky64/imageProcessing-master | vi_isoline.m | .m | imageProcessing-master/3dViewer/vi_isoline.m | 7,864 | utf_8 | 57f608cd389b82381976ee8981ad3aee | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright:
% Jun Tan
% University of Texas Southwestern Medical Center
% Department of Radiation Oncology
% Last edited: 08/19/2014
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function varargout = vi_isoline(varargin)
% VI_... |
github | jacksky64/imageProcessing-master | vi.m | .m | imageProcessing-master/3dViewer/vi.m | 78,447 | utf_8 | d16920fc5fbdbfde1720a0708614b1fb | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright:
% Jun Tan
% University of Texas Southwestern Medical Center
% Department of Radiation Oncology
% Last edited: 08/19/2014
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function varargout = vi(varargin)
% VI MATLAB c... |
github | jacksky64/imageProcessing-master | line_measurement.m | .m | imageProcessing-master/3dViewer/line_measurement.m | 15,958 | utf_8 | 9c31465f555fe9cfba04eb2e0626fca9 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright:
% Jun Tan
% University of Texas Southwestern Medical Center
% Department of Radiation Oncology
% Last edited: 08/19/2014
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function varargout = line_measurement(varargin)... |
github | jacksky64/imageProcessing-master | image_stats.m | .m | imageProcessing-master/3dViewer/image_stats.m | 5,961 | utf_8 | 5e350d3d77071a8cfdaf1904d9583a79 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright:
% Jun Tan
% University of Texas Southwestern Medical Center
% Department of Radiation Oncology
% Last edited: 08/19/2014
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function varargout = image_stats(varargin)
% IM... |
github | jacksky64/imageProcessing-master | knnc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/knnc.m | 3,535 | utf_8 | 20362e51c361d7899c025ded631e1d9b | %KNNC K-Nearest Neighbor Classifier
%
% [W,K,E] = KNNC(A,K)
% [W,K,E] = KNNC(A)
%
% INPUT
% A Dataset
% K Number of the nearest neighbors (optional; default: K is
% optimized with respect to the leave-one-out error on A)
%
% OUTPUT
% W k-NN classifier
% K Number of the nearest neighbors used
% ... |
github | jacksky64/imageProcessing-master | im_skel_meas.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_skel_meas.m | 1,669 | utf_8 | dcdcd014bc93aaef5301141e3c64512a | %IM_SKEL_MEASURE Computation by DIP_Image of skeleton-based features
%
% F = IM_SKEL_MEASURE(A,FEATURES)
%
% INPUT
% A Dataset with binary object images dataset
% FEATURES Features to be computed
%
% OUTPUT
% F Dataset with computed features
%
% DESCRIPTION
% The following features may be compute... |
github | jacksky64/imageProcessing-master | im_fft.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_fft.m | 859 | utf_8 | 9c39c2a03e24449fb0baa8cf48e786b2 | %IM_FFT 2D FFT of all images in dataset
%
% F = IM_FFT(A)
%
% INPUT
% A Dataset with object images (possibly multi-band)
%
% OUTPUT
% F Dataset with FFT images
%
% SEE ALSO
% DATASETS, DATAFILES, FFT2
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O... |
github | jacksky64/imageProcessing-master | parzenm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/parzenm.m | 2,629 | utf_8 | fc2c033dbde1f0e376cebdb6f43eb220 | %PARZENM Estimate Parzen densities
%
% W = PARZENM(A,H)
% W = A*PARZENM([],H)
%
% D = B*W
%
% INPUT
% A Input dataset
% H Smoothing parameters (scalar, vector)
%
% OUTPUT
% W output mapping
%
% DESCRIPTION
% A Parzen distribution is estimated for the labeled objects in A. Unlabeled
% objects are neglecte... |
github | jacksky64/imageProcessing-master | col2gray.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/col2gray.m | 1,596 | utf_8 | 8f52ea4434366e7be840bf8ffebaf7dd | %COL2GRAY Mapping for converting multi-band images into single band images
%
% B = COL2GRAY(A,V)
% B = A*COL2GRAY([],V)
%
% INPUT
% A Multiband image or dataset with multi-band images as objects
% V Weight vector, one weight per band. Default: equal weights.
%
% OUTPUT
% B Output image or dataset.
%
% ... |
github | jacksky64/imageProcessing-master | nulibsvc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nulibsvc.m | 5,160 | utf_8 | d2e65d89f90066e16b5bd949f48decea | %NULIBSVC Support Vector Classifier by libsvm, nu version
%
% [W,J,NU] = NULIBSVC(A,KERNEL,NU)
%
% INPUT
% A Dataset
% KERNEL Mapping to compute kernel by A*MAP(A,KERNEL)
% or string to compute kernel by FEVAL(KERNEL,A,A)
% or cell array with strings and parameters to compute ke... |
github | jacksky64/imageProcessing-master | cleval.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/cleval.m | 8,372 | utf_8 | db8f67007c6c315e32e927542219cbac | %CLEVAL Classifier evaluation (learning curve)
%
% E = CLEVAL(A,CLASSF,TRAINSIZES,NREPS,T,TESTFUN)
%
% INPUT
% A Training dataset
% CLASSF Classifier to evaluate
% TRAINSIZE Vector of training set sizes, used to generate subsets of A
% (default [2,3,5,7,10,15,20,30,50,70,100]). TRAINS... |
github | jacksky64/imageProcessing-master | classc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/classc.m | 3,622 | utf_8 | 095c4dd1edad8a43a38da8b9d9263191 | %CLASSC Convert classifier to normalized classifier (yielding confidences)
%
% V = CLASSC(W)
% V = W*CLASSC
% D = CLASSC(A*W)
% D = A*W*CLASSC
% D = CLASSC(A,W)
%
% INPUT
% W Trained or untrained classifier
% A Dataset
%
% OUTPUT
% V Normalized classifier producing confidences instead of
% densities or... |
github | jacksky64/imageProcessing-master | featselb.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featselb.m | 2,850 | utf_8 | 242e3f5afed7c113de4956acdbf3b569 | %FEATSELB Backward feature selection for classification
%
% [W,R] = FEATSELB(A,CRIT,K,T,FID)
% [W,R] = FEATSELB(A,CRIT,K,N,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping
% (optional; default: 'NN', i.e. 1-Nearest Neighbor error)
% K Number of features to ... |
github | jacksky64/imageProcessing-master | issym.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/issym.m | 768 | utf_8 | d029ef5dca799d320df7ee0deb0d19fa | %ISSYM Checks whether a matrix is symmetric
%
% OK = ISSYM(A,DELTA)
%
% INPUT
% A Dataset
% DELTA Parameter for the precision check (optional; default: 1e-12)
%
% OUTPUT
% OK 1 if the matrix A is symmetric and 0, otherwise.
%
% DESCRIPTION
% A is considered as a symmetric matrix, when it is square and
... |
github | jacksky64/imageProcessing-master | misval.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/misval.m | 2,478 | utf_8 | c3add71d533e6203113d88dfc8a0fd1c | %MISVAL Fix the missing values in a dataset
%
% B = MISVAL(A,VAL)
% B = A*MISVAL([],VAL)
%
% INPUT
% A Dataset, containing NaNs (missing values)
% VAL String with substitution option
% or value used for substitution
%
% B Dataset with NaNs substituted
%
% DESCRIPTION
%
% The following valu... |
github | jacksky64/imageProcessing-master | isdataset.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/isdataset.m | 501 | utf_8 | 0b61fa069741029a5c4bf06e4ba660c4 | %ISDATASET Test whether the argument is a dataset
%
% N = ISDATASET(A);
%
% INPUT
% A Input argument
%
% OUTPUT
% N 1/0 if A is/isn't a dataset
%
% DESCRIPTION
% The function ISDATASET test if A is a dataset object.
%
% SEE ALSO
% ISMAPPING, ISDATAIM, ISFEATIM
% $Id: isdataset.m,v 1.3 2007/03/22 08:54:59 duin Ex... |
github | jacksky64/imageProcessing-master | stumpc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/stumpc.m | 14,270 | utf_8 | eafef64246953de2e60096c0899fd40e | %STUMPC Decision stump classifier
%
% W = STUMPC(A,CRIT,N)
%
% Computation of a decision tree classifier out of a dataset A using
% a binary splitting criterion CRIT:
% INFCRIT - information gain
% MAXCRIT - purity (default)
% FISHCRIT - Fisher criterion
% Just N (default N=1) nodes are computed.
%
% s... |
github | jacksky64/imageProcessing-master | plote.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/plote.m | 8,258 | utf_8 | c431f890485b05d5b57ec1142bf67d65 | %PLOTE Plot error curves
%
% H = PLOTE(E,LINEWIDTH,S,FONTSIZE,OPTIONS)
%
% INPUT
% E Structure containing error curves (see e.g. CLEVAL)
% LINEWIDTH Line width, < 5 (default 2)
% S Plot strings
% FONTSIZE Font size, >= 5 (default 16)
% OPTIONS Character strings:
% 'noleg... |
github | jacksky64/imageProcessing-master | data2im.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/data2im.m | 3,061 | utf_8 | e249f7bb34cbd866b2b0767974b132cc | %DATA2IM Convert PRTools dataset or datafile to image
%
% IM = DATA2IM(A,J)
% IM = DATA2IM(A(J,:))
%
% INPUT
% A Dataset or datafile containing images
% J Desired images
%
% OUTPUT
% IM If A is dataset, IM is a X*Y*N*K matrix with K images.
% K is the number of images (length(J))
% ... |
github | jacksky64/imageProcessing-master | lkc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/lkc.m | 3,252 | utf_8 | 5e29d726e68a367e8bc996a32c8cc158 | %LKC Linear kernel classifier
%
% W = LKC(A,KERNEL)
%
% INPUT
% A Dataset
% KERNEL Mapping to compute kernel by A*MAP(A,KERNEL)
% or string to compute kernel by FEVAL(KERNEL,A,A)
% or cell array with strings and parameters to compute kernel by
% FEVAL(KERNEL{1},A,A,KE... |
github | jacksky64/imageProcessing-master | feateval.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/feateval.m | 5,360 | utf_8 | 574c3145ddb5fb970c464b329f8d8a60 | %FEATEVAL Evaluation of feature set for classification
%
% J = FEATEVAL(A,CRIT,T)
% J = FEATEVAL(A,CRIT,N)
%
% INPUT
% A input dataset
% CRIT string name of a method or untrained mapping
% T validation dataset (optional)
% N number of cross-validations (optional)
%
% OUTPUT
... |
github | jacksky64/imageProcessing-master | dcsc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/dcsc.m | 7,568 | utf_8 | e21c176dfd20be277f7484f749f2319b | % DCSC Dynamic Classifier Selection Combiner
%
% V = DCSC(A,W,K,TYPE)
% V = A*(W*DCSC([],K,TYPE))
% D = B*V
%
% INPUT
% A Dataset used for training base classifiers as well as combiner
% B Dataset used for testing (executing) the combiner
% W Set of trained or untrained base classifier... |
github | jacksky64/imageProcessing-master | gendatm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatm.m | 1,444 | utf_8 | 052286de99e508d15b878b8079f7d6c7 | %GENDATM Generation of multi-class 2-D data
%
% A = GENDATM(N)
%
% INPUT
% N Vector of class sizes (default: 20)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% Generation of N samples in 8 classes of 2 dimensionally distributed data
% vectors. Classes have equal prior probabilities. If N is a vector of
% sizes, ex... |
github | jacksky64/imageProcessing-master | crossval.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/crossval.m | 8,860 | utf_8 | 0fd850193167e8858f4201c769c67060 | %CROSSVAL Error/performance estimation by cross validation (rotation)
%
% [ERR,CERR,NLAB_OUT] = CROSSVAL(A,CLASSF,NFOLDS,1,TESTFUN)
% [ERR,STDS] = CROSSVAL(A,CLASSF,NFOLDS,NREP,TESTFUN)
% [ERR,CERR,NLAB_OUT] = CROSSVAL(A,CLASSF,NFOLDS,'DPS',TESTFUN)
% R = CROSSVAL(A,[],NFOLDS,0)
%
% ... |
github | jacksky64/imageProcessing-master | featsetc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featsetc.m | 323 | utf_8 | af3d4cb5cf54ec2cf01ef80386e579cc | %FEATSETC Set classifier
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
function [out1,out2] = featsetc(a,objclassf,fsetindex,fsetcombc,fsetclassf,fsetlab)
error('featsetc has been replaced by bagc')
... |
github | jacksky64/imageProcessing-master | baggingc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/baggingc.m | 2,478 | utf_8 | e1527f453891053e0d7cc405f8973486 | %BAGGINGC Bootstrapping and aggregation of classifiers
%
% W = BAGGINGC (A,CLASSF,N,ACLASSF,T)
%
% INPUT
% A Training dataset.
% CLASSF The base classifier (default: nmc)
% N Number of base classifiers to train (default: 100)
% ACLASSF Aggregating classifier (default: meanc), [] for no... |
github | jacksky64/imageProcessing-master | svo_nu.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/svo_nu.m | 3,962 | utf_8 | 396fa04268e3a40213f79e02eb83ee67 | %SVO_NU Support Vector Optimizer: NU algorithm
%
% [V,J,C] = SVO(K,NLAB,NU,PD)
%
% INPUT
% K Similarity matrix
% NLAB Label list consisting of -1/+1
% NU Regularization parameter (0 < NU < 1): expected fraction of SV (optional; default: 0.25)
%
% PD Do or do not the check of the positive definitene... |
github | jacksky64/imageProcessing-master | im_dbr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_dbr.m | 4,020 | utf_8 | af0771a52e215a85ee7b958c4661a1ce | %IM_DBR Image Database Retrieval GUI
%
% [RANK,TARG,OUTL] = IM_DBR(DBASE,FSETS,CLASSF,COMB)
%
% INPUT
% DBASE - Dataset or datafile with N object images
% FSETS - Cell array with maximum 4 feature sets
% CLASSF - Cell array with untrained classifiers (Default: KNNC([],1))
% COMB - Combining c... |
github | jacksky64/imageProcessing-master | testr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/testr.m | 1,212 | utf_8 | 3fb7df399bfa4a82711fe3d24a4e08d7 | %TESTR MSE for regression
%
% E = TESTR(X,W,TYPE)
% E = TESTR(X*W,TYPE)
% E = X*W*TESTR([],TYPE)
%
% INPUT
% X Regression dataset
% W Regression mapping
% TYPE Type of error measure, default: mean squared error
%
% OUTPUT
% E Mean squared error
%
% DESCRIPTION
% Compute the error of regr... |
github | jacksky64/imageProcessing-master | stacked.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/stacked.m | 4,901 | utf_8 | 79804a8343df74d2068f63cfa3b6d6be | %STACKED Combining classifiers in the same feature space
%
% WC = STACKED(W1,W2,W3, ....) or WC = [W1,W2,W3, ...]
% WC = STACKED({W1,W2,W3, ...}) or WC = [{W1,W2,W3, ...}]
% WC = STACKED(WC,W1,W2, ....) or WC = [WC,W2,W3, ...]
%
% INPUT
% W1,W2,W3 Set of classifiers
%
% OUTPUT
% WC Combined classifi... |
github | jacksky64/imageProcessing-master | bandsel.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/bandsel.m | 4,320 | utf_8 | b397f93a16fc65c469e370035595021d | %BANDSEL Selection of bands from object images
%
% B = BANDSEL(A,J)
% W = BANDSEL([],J)
% B = A*BANDSEL([],J)
%
% INPUT
% A Dataset or datafile with multi-band object images
% J Indices of bands to be selected
%
% OUTPUT
% W Mapping performing the band selection
% B Dataset with se... |
github | jacksky64/imageProcessing-master | datfilt.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/datfilt.m | 1,208 | utf_8 | 31f36e31d9f2ba195de6f48b03120f3c | %DATFILT Filtering of dataset images
%
% B = DATFILT(A,F)
%
% INPUT
% A Dataset with image data
% F Matrix with the convolution mask
%
% OUTPUT
% B Dataset containing all the images after filtering
%
% DESCRIPTION
% All images stored in the dataset A are horizontally and vertically
% convoluted by the 1-dime... |
github | jacksky64/imageProcessing-master | linewidth.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/linewidth.m | 605 | utf_8 | 38780117b4f229c290a054070793bd28 | %LINEWIDTH Set linewidth in plot
%
% linewidth(width)
%Set linewidth for current figure
function linewidth(width)
if strcmp(get(gca,'type'),'line')
set(gca,'linewidth',width);
end
children = get(gca,'children');
set_linewidth_children(children,width)
return
function set_linewidth_children(children,width)
if isempty(... |
github | jacksky64/imageProcessing-master | medianc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/medianc.m | 1,428 | utf_8 | b359e761a6b0660209c316be6d90edfb | %MEDIANC Median combining classifier
%
% W = MEDIANC(V)
% W = V*MEDIANC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Median combining classifier on V
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the same
% classes, then W is the median combiner: it selects the clas... |
github | jacksky64/imageProcessing-master | im_rotate.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_rotate.m | 1,230 | utf_8 | adfb824fe371037f44f120c02c27249f | %IM_ROTATE Rotate all images in dataset
%
% B = IM_ROTATE(A,ALF)
%
% INPUT
% A Dataset with object images (possibly multi-band)
% ALF Rotation angle (in radians),
% default: rotation to main axis
%
% OUTPUT
% B Dataset with rotated object images
%
% SEE ALSO
% DATASETS, DATAFILES,... |
github | jacksky64/imageProcessing-master | gensubsets.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gensubsets.m | 2,064 | utf_8 | b46581d4b9ea139026fe9db863da530c | %GENSUBSETS Generate sequence of embedded training sets
%
% [L,R] = GENSUBSETS(NLAB,S)
% [L,R] = GENSUBSETS(A,S)
%
% INPUT
% NLAB Column vector of numeric labels of some dataset A.
% NLAB = GETNLAB(A)
% A Dataset for which subsets are to be created
% S Array of growing subset sizes.
... |
github | jacksky64/imageProcessing-master | ploto.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/ploto.m | 1,981 | utf_8 | a555d41642039708da0330e072b05329 | %PLOTO Plot objects as 1-D functions of the feature number
%
% [HH HO HC] = PLOTO(A,N)
%
% INPUT
% A Dataset
% N Integer
%
% OUTPUT
% HH Lines handles
% HO Object identifier handles
% HC Class number handles
%
% DESCRIPTION
% Produces 1-D function plots for all the objects in dataset A. The plots
%... |
github | jacksky64/imageProcessing-master | iscomdset.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/iscomdset.m | 1,536 | utf_8 | e4f1285eb09e4258dd2fb8a71fa2e676 | %ISCOMDSET Test whether datasets are compatible
%
% N = ISCOMDSET(A,B,CLAS);
%
% INPUT
% A Input argument, to be tested on dataset
% B Input argument, to be tested on compatibility with A
% CLAS 1/0, test on equal classes (1) or don't test (0)
% (optional; default 1)
%
% OUTPUT
% N 1... |
github | jacksky64/imageProcessing-master | prdata.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prdata.m | 1,605 | utf_8 | 5945233e9aca3aff5de534a83caf9910 | %PRDATA Read data files
%
% A = PRDATA(FILENAME,FLAG)
%
% INPUT
% FILENAME Name of delimited ASCII file containing rows of data
% FLAG If not 0, first column is assumed to contain labels (default 1)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% Reads data into the dataset A. The first word of each ... |
github | jacksky64/imageProcessing-master | affine.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/affine.m | 6,661 | utf_8 | 6cff6eade53ed2451811ada3fd31a10b | %AFFINE Construct affine (linear) mapping from parameters
%
% W = AFFINE(R,OFFSET,LABLIST_IN,LABLIST_OUT,SIZE_IN,SIZE_OUT)
% W = AFFINE(R,OFFSET,A)
% W = AFFINE(W1,W2)
%
% INPUT
% R Matrix of a linear mapping from a K- to an L-dimensional space
% OFFSET Shift applied after R; a row vector of... |
github | jacksky64/imageProcessing-master | show.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/show.m | 1,832 | utf_8 | 747bec8ec7718217efb36afb78ddd30a | %SHOW PRTools general show
%
% H = SHOW(A,N,B)
%
% INPUT
% A Image
% N Number of images on a row
% B Intensity value of background (default 0.5);
%
% OUTPUT
% H Graphics handle
%
% DESCRIPTION
% PRTools offers a SHOW command for variables of the data classes DATASET
% and DA... |
github | jacksky64/imageProcessing-master | gauss.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gauss.m | 4,857 | utf_8 | f871f6aac7a86da963a8c0f48b508082 | %GAUSS Generation of a multivariate Gaussian dataset
%
% A = GAUSS(N,U,G,LABTYPE)
%
% INPUT (in case of generation a 1-class dataset in K dimensions)
% N Number of objects to be generated (default 50).
% U Desired mean (vector of length K).
% G K x K covariance matrix. Default eye(K).
% LABTY... |
github | jacksky64/imageProcessing-master | nlabcmp.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nlabcmp.m | 975 | utf_8 | 91014c1ad5b78b2874f44cf02e296fcb | %NLABCMP Compare two label lists and count the differences
%
% [N,C] = NLABCMP(LAB1,LAB2)
%
% INPUT
% LAB1,
% LAB2 Label lists
%
% OUTPUT
% C A 0/1 vector pointing to different/equal labels
% N Number of differences in LAB1 and LAB2
%
% DESCRIPTION
% Compares two label lists and counts the disa... |
github | jacksky64/imageProcessing-master | featsellr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featsellr.m | 9,276 | utf_8 | 899156db13a08ae7791a4a549dd9d1c7 | %FEATSELLR Plus-L-takeaway-R feature selection for classification
%
% [W,RES] = FEATSELLR(A,CRIT,K,L,R,T,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping
% (optional; default: 'NN', i.e. 1-Nearest Neighbor error)
% K Number of features to select
% (o... |
github | jacksky64/imageProcessing-master | prdatasets.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prdatasets.m | 3,170 | utf_8 | a203d844b15ee8e794afdc358bd12a83 | %PRDATASETS Checks availability of a PRTOOLS dataset
%
% PRDATASETS
%
% Checks the availability of the PRDATASETS directory, downloads the
% Contents file and m-files if necessary and adds it to the search path.
% Lists Contents file.
%
% PRDATASETS(DSET)
%
% Checks the availability of the particular data... |
github | jacksky64/imageProcessing-master | gentrunk.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gentrunk.m | 1,849 | utf_8 | 3a239e0c7a1bf3700f24ce43dc0e7187 | %GENTRUNK Generation of Trunk's classification problem of 2 Gaussian classes
%
% A = GENTRUNK(N,K)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K Dimensionality of the dataset to be generated (default: 2).
%
% OUTPUT
% A Dataset.
%
% DESCRIPTION
% Gener... |
github | jacksky64/imageProcessing-master | setdat.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/setdat.m | 1,305 | utf_8 | c0fb96a1040be90efb0e4d9553ba0e5d | %SETDAT Reset data and feature labels of dataset for classification output
%
% A = SETDAT(A,DATA,W)
%
% INPUT
% A Dataset
% DATA Dataset or double
% W Mapping (optional)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% The data in the dataset A is replaced by DATA (dataset or double). The
% n... |
github | jacksky64/imageProcessing-master | testc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/testc.m | 15,179 | utf_8 | 80b4f31f69f8abb91b74a44a0c35913c | %TESTC Test classifier, error / performance estimation
%
% [E,C] = TESTC(A*W,TYPE)
% [E,C] = TESTC(A,W,TYPE)
% E = A*W*TESTC([],TYPE)
%
% [E,F] = TESTC(A*W,TYPE,LABEL)
% [E,F] = TESTC(A,W,TYPE,LABEL)
% E = A*W*TESTC([],TYPE,LABEL)
%
% INPUT
% A Dataset
% W Trained classifier mapping
% ... |
github | jacksky64/imageProcessing-master | labeld.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/labeld.m | 3,452 | utf_8 | a0eb48345f2f78a977286d3b730bf6ee | %LABELD Find labels of classification dataset (perform crisp classification)
%
% LABELS = LABELD(Z)
% LABELS = Z*LABELD
% LABELS = LABELD(A,W)
% LABELS = A*W*LABELD
% LABELS = LABELD(Z,THRESH)
% LABELS = Z*LABELD([],THRESH)
% LABELS = LABELD(A,W,THRESH)
% LABELS = A*W*LABELD([],THRESH)
%
% INPUT
% Z ... |
github | jacksky64/imageProcessing-master | nmsc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nmsc.m | 2,013 | utf_8 | d6f267790fb4836284cfe76cb9641f13 | %NMSC Nearest Mean Scaled Classifier
%
% W = NMSC(A)
% W = A*NMSC
%
% INPUT
% A Trainign dataset
%
% OUTPUT
% W Nearest Mean Scaled Classifier mapping
%
% DESCRIPTION
% Computation of the linear discriminant for the classes in the dataset A
% assuming normal distributions with zero covariances and equal cl... |
github | jacksky64/imageProcessing-master | testauc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/testauc.m | 2,064 | utf_8 | f5ad0b21b349ec323644d52b39eb9be6 | %TESTAUC Multiclass error area under the ROC
%
% E = TESTAUC(A*W)
% E = TESTAUC(A,W)
% E = A*W*TESTAUC
%
% INPUT
% A Dataset to be classified
% W Classifier
%
% OUTPUT
% E Error, Area under the ROC
%
% DESCRIPTION
% The area under the ROC is computed for the datset A w.r.t. the
% classifer... |
github | jacksky64/imageProcessing-master | genclass.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/genclass.m | 1,579 | utf_8 | b363022121705e34497b3a5c067eabd1 | %GENCLASS Generate class frequency distribution
%
% M = GENCLASS(N,P)
%
% INPUT
% N Number (scalar)
% P Prior probabilities
%
% OUTPUT
% M Class frequency distribution
%
% DESCRIPTION
% Generates a class frequency distribution M of N (scalar) samples
% over a set of classes with prior probabilities given b... |
github | jacksky64/imageProcessing-master | prtools_news.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prtools_news.m | 2,342 | utf_8 | b439aced7644c0559d07b4f2e2542d8c | %PRTOOLS_NEWS List PRTools news and download new versions
%
% PRTOOLS_NEWS List PRTools news
% PRTOOLS_NEWS(DIRNAME,UNZIP) Reload PRTools
%
% DIRNAME is the directory to download PRTools. If UNZIP == 1
% (default 0) it is unzipped.
function out = prtools_news(dirname,unzip_li... |
github | jacksky64/imageProcessing-master | gendatw.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatw.m | 834 | utf_8 | c8923ae31b9b5ca941d682f2f264e9ce | %GENDATW Sample dataset by given weigths
%
% B = GENDATW(A,V,N)
%
% INPUT
% A Dataset
% V Vector with weigths for each object in A
% N Number of objects to be generated (default size A);
%
% OUTPUT
% B Dataset
%
% DESCRIPTION
% The dataset A is sampled using the weigths in V as a prio... |
github | jacksky64/imageProcessing-master | kernelm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/kernelm.m | 5,348 | utf_8 | 3e8ef13d0db7c522ec59556fa1e416ba | %KERNELM Kernel mapping, dissimilarity representation
%
% [W,J] = KERNELM(A,KERNEL,SELECT,P1,P2 , ...)
% W = A*KERNELM([],KERNEL,SELECT,P1,P2 , ...)
% K = B*W
%
% INPUT
% A,B Datasets
% KERNEL Untrained kernel / dissimilarity representation,
% a mapping computing proximitie... |
github | jacksky64/imageProcessing-master | rbsvc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/rbsvc.m | 2,053 | utf_8 | 41cca5dbfaed944ed3654474ee369f0b | %RBSVC Automatic radial basis Support Vector Classifier
%
% [W,KERNEL,NU] = RBSVC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Mapping: Radial Basis Support Vector Classifier
% KERNEL Untrained mapping, representing the optimised kernel
% NU Resulting value for NU from NUSVC
%
% DESCR... |
github | jacksky64/imageProcessing-master | gendatp.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatp.m | 2,955 | utf_8 | 5b3389c5a8b91866cde0c9f0155e41ab | %GENDATP Parzen density data generation
%
% B = GENDATP(A,N,S,G)
%
% INPUT
% A Dataset
% N Number(s) of points to be generated (optional; default: 50 per class)
% S Smoothing parameter(s)
% (optional; default: a maximum likelihood estimate based on A)
% G Covariance matrix used for generation of t... |
github | jacksky64/imageProcessing-master | spirals.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/spirals.m | 553 | utf_8 | 9c160bf41b06bee2e4e497139043f7f5 | %SPIRALS 194 objects with 2 features in 2 classes
%
% A = SPIRALS
% A = SPIRALS(M,N)
%
% Load the dataset in A, select the objects and features according to the
% index vectors M and N. This is one of the Spiral dataset implementations.
%
% See also DATASETS, PRDATASETS
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org... |
github | jacksky64/imageProcessing-master | plotdg.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/plotdg.m | 1,889 | utf_8 | a5fda91cafdb6a5f1d4df34153470a49 | %PLOTDG Plot dendrogram
%
% PLOTDG(DENDROGRAM,K)
%
% INPUT
% DENDROGRAM Dendrogram
% K Number of clusters
%
% OUTPUT
%
% DESCRIPTION
% Plots a dendrogram as generated by HCLUST. If the optional K is given the
% dendrogram is compressed first to K clusters. Along the horizontal axis
% the numbers stored... |
github | jacksky64/imageProcessing-master | newline.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/newline.m | 174 | utf_8 | 2a39d991937508030bfbf1e69ec3c1a6 | %NEWLINE The platform dependent newline character
%
% c = newline
% $Id: newline.m,v 1.3 2010/03/18 12:25:21 duin Exp $
function c = newline
c = sprintf('\n');
return
|
github | jacksky64/imageProcessing-master | genlab.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/genlab.m | 3,076 | utf_8 | f6ea44f4198613eeb28e3339d6174aa7 | %GENLAB Generate labels for classes
%
% LABELS = GENLAB(N,LABLIST)
%
% INPUT
% N Number of labels to be generated
% LABLIST Label names (optional; default: numeric labels 1,2,3,...)
%
% OUTPUT
% LABELS Labels in a column vector or strinag array
%
% DESCRIPTION
% Generate a set of labels as defined... |
github | jacksky64/imageProcessing-master | im_berosion.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_berosion.m | 1,256 | utf_8 | ea9ba28359fad8fd157dc7a9cb947476 | %IM_BEROSION Binary erosion of images stored in a dataset (DIP_Image)
%
% B = IM_BEROSION(A,N,CONNECTIVITY,EDGE_CONDITION)
% B = A*IM_BEROSION([],N,CONNECTIVITY,EDGE_CONDITION)
%
% INPUT
% A Dataset with binary object images dataset (possibly multi-band)
% N Number of iterations (default 1)
% CONNEC... |
github | jacksky64/imageProcessing-master | im_minf.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_minf.m | 1,134 | utf_8 | fe024d15ba47c051482648153256c205 | %IM_MINF Minimum filter of images stored in a dataset (DIP_Image)
%
% B = IM_MINF(A,SIZE,SHAPE)
% B = A*IM_MINF([],SIZE,SHAPE)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% SIZE Filter width in pixels, default SIZE = 7
% SHAPE String with shape:'rectangular', 'elliptic', '... |
github | jacksky64/imageProcessing-master | setname.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/setname.m | 287 | utf_8 | 038915ac208df9ac19248da017fefef0 | %SETNAME Mapping for easy name setting
%
% A = A*SETNAME([],NAME)
% W = W*SETNAME([],NAME)
%
%Set name of dataset A or mapping W
function a = setname(a,varargin)
if nargin < 1 | isempty(a)
a = mapping(mfilename,'combiner',varargin);
else
a = setname(a,varargin);
end |
github | jacksky64/imageProcessing-master | subsc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/subsc.m | 4,474 | utf_8 | c48deab246d0527ec9f8facef7c0cb91 | %SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per class
%
% OUTPUT
% W Subspace classifier
%
% DESCRIPTION
% Each class in the trainingset A is described by ... |
github | jacksky64/imageProcessing-master | reject.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/reject.m | 3,470 | utf_8 | e28c512648bc1dc90ebb01253058ff9d | %REJECT Compute the error-reject trade-off curve
%
% E = REJECT(D);
% E = REJECT(A,W);
%
% INPUT
% D Classification result, D = A*W
% A Dataset
% W Cell array of trained classifiers
%
% OUTPUT
% E Structure storing the error curve and information needed for plotting
%
% DESCRIPTION
% E = REJECT(D)... |
github | jacksky64/imageProcessing-master | rejectc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/rejectc.m | 2,045 | utf_8 | 5edd5997a9b5130d788a7e1c2c935433 | %REJECTC Construction of a rejecting classifier
%
% WR = REJECTC(A,W,FRAC,TYPE)
%
% INPUT
% A Dataset
% W Trained or untrained classifier
% FRAC Fraction to be rejected. Default: 0.05
% TYPE String with reject type: 'ambiguity' or 'outlier'.
% 'a' and 'o' are supported as well.... |
github | jacksky64/imageProcessing-master | gendatk.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatk.m | 3,686 | utf_8 | 7b5a0540b3986a375806e98dd13c0cf5 | %GENDATK K-Nearest neighbor data generation
%
% B = GENDATK(A,N,K,S)
%
% INPUT
% A Dataset
% N Number of points (optional; default: 50)
% K Number of nearest neighbors (optional; default: 1)
% S Standard deviation (optional; default: 1)
%
% OUTPUT
% B Generated dataset
%
% DESCRIPTION
% Generation of... |
github | jacksky64/imageProcessing-master | nusvc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nusvc.m | 4,575 | utf_8 | efaf4b65ce42bc988eab73dc28fea2c0 | %NUSVC Support Vector Classifier: NU algorithm
%
% [W,J,NU] = NUSVC(A,KERNEL,NU)
% W = A*SVC([],KERNEL,NU)
%
% INPUT
% A Dataset
% KERNEL - Untrained mapping to compute kernel by A*(A*KERNEL) during
% training, or B*(A*KERNEL) during testing with dataset B.
% - String to compute... |
github | jacksky64/imageProcessing-master | prarff.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prarff.m | 3,226 | utf_8 | b9a5520deaa586036751cc082ae2f646 | %PRARFF COnvert ARFF file into PRTools dataset
%
% A = PRARFF(FILE)
%
% INPUT
% FILE ARFF file
%
% OUTPUT
% A Dataset in PRTools format
%
% DESCRIPTION
% ARFF files as used in WEKA are converted into PRTools format. In case
% they don't fit (non-numeric features, varying feature length) an err... |
github | jacksky64/imageProcessing-master | prmemory.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prmemory.m | 1,969 | utf_8 | 4831b62875e22b1bfcfc7f3d412afe7e | %PRMEMORY Set/get size of memory usage
%
% N = PRMEMORY(N)
%
% N : The desired / retrieved maximum size data of matrices (in
% matrix elements)
%
% DESCRIPTION
% This retoutine sets or retrieves a global variable GLOBALPRMEMORY that
% controls the maximum size of data matrices in PRTools. Routines like
% K... |
github | jacksky64/imageProcessing-master | im_scale.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_scale.m | 1,217 | utf_8 | 523e0781344f11a37de848be741dc80c | %IM_SCALE Scale all binary images in a datafile to a giving fraction of pixels 'on'
%
% B = IM_SCALE(A,P)
% B = A*IM_SCALE([],P)
%
% B is a zoomed in / out version of A such that about a fraction
% P of the image pixels is 'on' (1).
%
% SEE ALSO
% DATASETS, DATAFILES, IM_BOX, IM_CENTER
% Copyright: R.P.W. Duin, r.... |
github | jacksky64/imageProcessing-master | kcentres.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/kcentres.m | 5,769 | utf_8 | f5d8e03cc086b9c56daea98db515307e | %KCENTRES Finds K center objects from a distance matrix
%
% [LAB,J,DM] = KCENTRES(D,K,N)
%
% INPUT
% D Distance matrix between, e.g. M objects (may be a dataset)
% K Number of center objects to be found (optional; default: 1)
% N Number of trials starting from a random initialization
% (optiona... |
github | jacksky64/imageProcessing-master | bagcc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/bagcc.m | 3,054 | utf_8 | 82d80a12e773b00901ef371565572def | %BAGCC Combining classifier for classifying bags of objects
%
% DBAG = BAGCC(DOBJ,COMBC)
% DBAG = DOBJ*BAGCC([],COMBC)
%
% INPUT
% DOBJ Dataset, classification matrix, output of some base classifier
% COMBC Combiner, e.g. MAXC (default VOTEC)
%
% OUTPUT
% DBAG Dataset, classification matrix for ... |
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