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values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | jacksky64/imageProcessing-master | im_hist_equalize.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_hist_equalize.m | 888 | utf_8 | f3f11d9287d170ff8356974bbc4a06aa | %IM_HIST_EQUALIZE Histogram equalization of images stored in a dataset
% (DIP_Image)
%
% B = IM_HIST_EQUALIZE(A)
% B = A*IM_HIST_EQUALIZE
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
%
% OUTPUT
% B Dataset with filtered images
%
% SEE ALSO
% DATASETS, DATAFIL... |
github | jacksky64/imageProcessing-master | isvaldset.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/isvaldset.m | 1,837 | utf_8 | 150731368016a57335890b7883d2442c | %ISVALDSET Test whether the argument is a valid dataset
%
% N = ISVALDSET(A);
% N = ISVALDSET(A,M);
% N = ISVALDSET(A,M,C);
%
% INPUT
% A Input argument, to be tested on dataset
% M Minimum number of objects per class in A
% C Minimum number of classes in A
%
% OUTPUT
% N 1/0 if A is... |
github | jacksky64/imageProcessing-master | im_label.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_label.m | 1,355 | utf_8 | 7dbcb468c8d8516bcb86e204f0003626 | %IM_LABEL Labeling of binary images stored in a dataset (DIP_Image)
%
% B = IM_LABEL(A,CONNECTIVITY,MIN_SIZE,MAX_SIZE)
% B = A*IM_LABEL([],CONNECTIVITY,MIN_SIZE,MAX_SIZE)
%
% INPUT
% A Dataset with binary object images dataset (possibly multi-band)
% N Number of iterations (default 1)
% CONNECTIVITY... |
github | jacksky64/imageProcessing-master | im_gaussf.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_gaussf.m | 1,311 | utf_8 | 4cc0d1ee779b18cb90b369721be95fa6 | %IM_GAUSSF Gaussian filter of images stored in a dataset (DIPImage)
%
% B = IM_GAUSSF(A,S)
% B = A*IM_GAUSSF([],S)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% S Desired standard deviation for filter, default S = 1
%
% OUTPUT
% B Dataset with Gaussian filtered imag... |
github | jacksky64/imageProcessing-master | featself.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featself.m | 2,272 | utf_8 | 9ae66ecdd6ab7bfbe60e1d20c6df8412 | %FEATSELF Forward feature selection for classification
%
% [W,R] = FEATSELF(A,CRIT,K,T,FID)
% [W,R] = FEATSELF(A,CRIT,K,N,FID)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or untrained mapping
% (default: 'NN', i.e. the 1-Nearest Neighbor error)
% K Number of features to select (def... |
github | jacksky64/imageProcessing-master | featseli.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featseli.m | 2,740 | utf_8 | fe8af121437cd0badb3e0fdde5f714d8 | %FEATSELI Individual feature selection for classification
%
% [W,R] = FEATSELI(A,CRIT,K,T)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or untrained mapping
% (default: 'NN', i.e. the 1-Nearest Neighbor error)
% K Number of features to select (default: sort all features)
% T Tu... |
github | jacksky64/imageProcessing-master | typp.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/typp.m | 1,043 | utf_8 | 8d294b9b1229f66b0f727b0811f2242d | %TYPP list M-File of PRTools
% TYPE foo.bar lists the ascii file called 'foo.bar'.
%
% TYPE foo lists the ascii file called 'foo.m'.
%
% If files called foo and foo.m both exist, then
% TYPE foo lists the file 'foo', and
% TYPE foo.m list the file 'foo.m'.
%
% TYPE FILENAME lists the contents... |
github | jacksky64/imageProcessing-master | meanc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/meanc.m | 1,652 | utf_8 | 484801a53d01ab23a3259fb7ea43a1d8 | %MEANC Mean combining classifier
%
% W = MEANC(V)
% W = V*MEANC
%
% INPUT
% V Set of classifiers (optional)
%
% OUTPUT
% W Mean combiner
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the same
% classes and W is the mean combiner: it selects the class with the mean of
% the ... |
github | jacksky64/imageProcessing-master | adaboostc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/adaboostc.m | 3,756 | utf_8 | 622ed5621261168b9796ffdf4e57b129 | %ADABOOSTC
%
% [W,V,ALF] = ADABOOSTC(A,CLASSF,N,RULE,VERBOSE);
%
% INPUT
% A Dataset
% CLASSF Untrained weak classifier
% N Number of classifiers to be trained
% RULE Combining rule (default: weighted voting)
% VERBOSE Suppress progress report if 0 (default)
%
% OUTPUT
% W Combined tr... |
github | jacksky64/imageProcessing-master | cmapm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/cmapm.m | 5,624 | utf_8 | 7d0c1c3f05b31ca8840e7529ff33c3c6 | %CMAPM Compute some special maps
%
% INPUT
% Various
%
% OUTPUT
% W Mapping
%
% DESCRIPTION
% CMAPM computes some special data-independent maps for scaling, selecting or
% rotating K-dimensional feature spaces.
%
% W = CMAPM(K,N) Selects the features listed in the vector N
% W = CMAPM(K,P) ... |
github | jacksky64/imageProcessing-master | knnm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/knnm.m | 2,438 | utf_8 | e61eb7d473cd279fa7c748f9ad089245 | %KNNM K-Nearest Neighbour based density estimate
%
% W = KNNM(A,KNN)
%
% D = B*W
%
% INPUT
% A Dataset
% KNN Number of nearest neighbours
%
% OUTPUT
% W Density estimate
%
% DESCRIPTION
% A density estimator is constructed based on the k-Nearest Neighbour rule
% using the labeled objects in A. A... |
github | jacksky64/imageProcessing-master | polyc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/polyc.m | 2,421 | utf_8 | 044939bf2b4794fc73056fb724c2ac1f | %POLYC Polynomial Classification
%
% W = polyc(A,CLASSF,N,S)
%
% INPUT
% A Dataset
% CLASSF Untrained classifier (optional; default: FISHERC)
% N Degree of polynomial (optional; default: 1)
% S 1/0, 1 indicates that 2nd order combination terms should be used
% (optional; default... |
github | jacksky64/imageProcessing-master | confmat.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/confmat.m | 7,695 | utf_8 | 0d64b6fb4a10caf99417d1eda2878c7a | %CONFMAT Construct confusion matrix
%
% [C,NE,LABLIST] = CONFMAT(LAB1,LAB2,METHOD,FID)
%
% INPUT
% LAB1 Set of labels
% LAB2 Set of labels
% METHOD 'count' (default) to count number of co-occurences in
% LAB1 and LAB2, 'disagreement' to count relative
% non-co-occurrence... |
github | jacksky64/imageProcessing-master | dps.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/dps.m | 3,849 | utf_8 | 940a25b59ab5d19f49a72df1b888cd6a | % DPS Correntropy based, hierarchical density preserving data split
%
% R = DPS(A,LEVELS,CLASSWISE)
% [R H] = DPS(A,LEVELS,CLASSWISE)
%
% INPUT
% A Input dataset
% LEVELS Number of split levels, default: 3
% CLASSWISE Use (1, default) or ignore (0) label information
%
% OUTPUT
% R... |
github | jacksky64/imageProcessing-master | reorderclasses.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/reorderclasses.m | 3,660 | utf_8 | 0f7203be264e07c19eb91ce888d8c812 | %REORDERCLASSES Reorder the lablist
%
% X = REORDERCLASSES(X,LABLIST)
% X = REORDERCLASSES(X,I)
%
% INPUT
% X (labeled) dataset
% LABLIST correctly ordered lablist
% I permutation vector
%
% OUTPUT
% X dataset with reordered classes
%
% DESCRIPTION
% Change the order of... |
github | jacksky64/imageProcessing-master | bayesc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/bayesc.m | 2,709 | utf_8 | 64ea328a92377d3fd78dcef88ed92b12 | %BAYESC Bayes classifier
%
% W = BAYESC(WA,WB, ... ,P,LABLIST)
%
% INPUT
% WA, WB, ... Trained mappings for supplying class density estimates
% P Vector with class prior probabilities
% Default: equal priors
% LABLIST List of class names (labels)
%
% OUTPUT
% W ... |
github | jacksky64/imageProcessing-master | traincc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/traincc.m | 1,575 | utf_8 | ce1e8f1e40fd17a4139ac4d705643c2d | %TRAINCC Train combining classifier if needed
%
% W = TRAINCC(A,W,CCLASSF)
%
% INPUT
% A Training dataset
% W A set of classifiers to be combined
% CCLASSF Combining classifier
%
% OUTPUT
% B Combined classifier mapping
%
% DESCRIPTION
% The combining classifier CCLASSF is trained ... |
github | jacksky64/imageProcessing-master | plotr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/plotr.m | 1,058 | utf_8 | e922a00d48d26055bff39c4243bfad90 | %PLOTR Plot regression
%
% PLOTR(W)
% PLOTR(W,CLR)
%
% Plot the regression function W, optionally using plot string CLR.
% This plot string can be anything that is defined in plot.m.
% For the best results (concerning the definition of the axis for
% instance) it is wise to first scatter the regression data using... |
github | jacksky64/imageProcessing-master | matchcost.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/matchcost.m | 1,233 | utf_8 | 7f0b9c4e4003b50b936ad26092ce9e5c | % Matchcost
% Copyright: D.M.J. Tax, duin@ph.tn.tudelft.nl
% Faculty of Applied Sciences, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands
% $Id: matchcost.m,v 1.2 2006/03/08 22:06:58 duin Exp $
function [cost,lablist] = matchcost(orglablist,cost,lablist)
prtrace(mfilename,2);
k ... |
github | jacksky64/imageProcessing-master | txtread.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/txtread.m | 696 | utf_8 | 6ebf9789d35e41ef629a89899cc6de7a | %TXTREAD Read text file
%
% A = TXTREAD(FILENAME,N,START)
%
% INPUT
% FILENAME Name of delimited ASCII file
% N Number of elements to be read (default all)
% START First element to be read (default 1)
%
% OUTPUT
% A String
%
% DESCRIPTION
% Reads the total file as text string into A
... |
github | jacksky64/imageProcessing-master | featsel.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featsel.m | 1,825 | utf_8 | a8189a42fced472d3e1bb2f9f56f3b4e | %FEATSEL Selection of known features
%
% W = FEATSEL(K,J)
%
% INPUT
% K Input dimensionality
% J Index vector of features to be selected
%
% OUTPUT
% W Mapping performing the feature selection
%
% DESCRIPTION
% This is a simple support routine that writes feature selection
% in terms of a ... |
github | jacksky64/imageProcessing-master | gaussm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gaussm.m | 2,736 | utf_8 | a025a61e12fe6c77f953ebdd347f1cd5 | %GAUSSM Mixture of Gaussians density estimate
%
% W = GAUSSM(A,K,R,S,M)
% W = A*GAUSSM([],K,R,S,M);
%
% INPUT
% A Dataset
% K Number of Gaussians to use (default: 1)
% R,S,M Regularization parameters, 0 <= R,S <= 1, see QDC
%
% OUTPUT
% W Mixture of Gaussians density estimate
%
% DESCRIPTION
% Est... |
github | jacksky64/imageProcessing-master | prwaitbarinit.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prwaitbarinit.m | 1,294 | utf_8 | 15265af6113db7d793f5a56b190e02a0 | %PRWAITBARINIT Low level routine to simplify PRWAITBAR init
%
% [N,S,COUNT] = PRWAITBARINIT(STRING,N)
%
% INPUT
% STRING - String with text to be written in every waitbar,
% e.g. 'Processing %i items: '. This will be parsed
% by S = SPRINTF(STRING,N);
% N - Total number of ite... |
github | jacksky64/imageProcessing-master | prpinv.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prpinv.m | 315 | utf_8 | e3f9437aff6d34596fe94d4b990b2562 | %PRPINV Call to PINV() including PRWAITBAR
%
% B = PRPINV(A)
%
% This calls B = PINV(A) and includes a message to PRWAITBAR
% in case of a large A
function B = prpinv(A)
[m,n] = size(A);
if min([m,n]) > 250
prwaitbaronce('Inverting %i x %i matrix ...',[m,n]);
B = pinv(A);
prwaitbar(0);
else
B = pinv(A);
end |
github | jacksky64/imageProcessing-master | maxc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/maxc.m | 1,900 | utf_8 | 27971a25a4f95b74f121b8713b358fb2 | %MAXC Maximum combining classifier
%
% W = MAXC(V)
% W = V*MAXC
%
% INPUT
% V Stacked set of classifiers
%
% OUTPUT
% W Combined classifier using max-rule
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the same
% classes, then W is the maximum combiner: it selects the class tha... |
github | jacksky64/imageProcessing-master | kernelc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/kernelc.m | 2,552 | utf_8 | 88c72b7bff0916c5ead2b42ec0be0f1b | %KERNELC Arbitrary kernel/dissimilarity based classifier
%
% W = KERNELC(A,KERNEL,CLASSF)
% W = A*KERNELC([],KERNEL,CLASSF)
%
% INPUT
% A Dateset used for training
% KERNEL - untrained mapping to compute kernel by A*(A*KERNEL) for
% training CLASSF or B*(A*KERNEL) for testing with dat... |
github | jacksky64/imageProcessing-master | ismapping.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/ismapping.m | 493 | utf_8 | 9616ca5d38d4cc8ea4813f5dcdc0ecac | %ISMAPPING Test whether the argument is a mapping
%
% N = ISMAPPING(W);
%
% INPUT
% W Input argument
%
% OUTPUT
% N 1/0 if W is/isn't a mapping object
%
% DESCRIPTION
% True (1) if W is a mapping object and false (0), otherwise.
%
% SEE ALSO
% ISDATASET, ISFEATIM, ISDATAIM
% $Id: ismapping.m,v 1.2 2006/03/08 2... |
github | jacksky64/imageProcessing-master | pls_train.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/pls_train.m | 9,379 | utf_8 | 2dc7ca5aaf1d9bcc741e7182373df635 | %pls_train Partial Least Squares (training)
%
% [B,XRes,YRes,Options] = pls_train(X,Y)
% [B,XRes,YRes,Options] = pls_train(X,Y,Options)
%
% INPUT
% X [N -by- d_X] the training (input) data matrix, N samples, d_X variables
% Y [N -by- d_Y] the training (output) data matrix, N samples, d_Y variables
%
% Optio... |
github | jacksky64/imageProcessing-master | featselo.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featselo.m | 4,290 | utf_8 | 4603fe51a9b83f13a1277d846ea96f4d | %FEATSELO Branch and bound feature selection
%
% W = featselo(A,CRIT,K,T,FID)
%
% INPUT
% A input dataset
% CRIT string name of the criterion or untrained mapping
% (optional, def= 'NN' 1-Nearest Neighbor error)
% K numner of features to select (optional, def: K=2)
% T validation set ... |
github | jacksky64/imageProcessing-master | prwaitbar.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prwaitbar.m | 11,571 | utf_8 | 37266483f77f00ad145637eb2148b919 | %PRWAITBAR Report PRTools progress by single waitbar
%
% H = PRWAITBAR(N,M,TEXT)
% H = PRWAITBAR(N,TEXT,FLAG)
% S = PRWAITBAR
%
% INPUT
% N Integer, total number of steps in loop
% M Integer, progress in number of steps in loop
% TEXT Text to be displayed in waitbar
% FLAG Flag (0/... |
github | jacksky64/imageProcessing-master | neurc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/neurc.m | 4,133 | utf_8 | b5c828f2c19833cfaf7febcd6f54c3bb | %NEURC Automatic neural network classifier
%
% W = NEURC (A,UNITS)
%
% INPUT
% A Dataset
% UNITS Number of units
% Default: 0.2 x size smallest class in A.
%
% OUTPUT
% W Trained feed-forward neural network mapping
%
% DESCRIPTION
% Automatically trained feed-forward neural network classifie... |
github | jacksky64/imageProcessing-master | scatterd.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/scatterd.m | 10,094 | utf_8 | b2c8340e223bd55cc3a165e54701df03 | %SCATTERD Display scatterplot
%
% H = SCATTERD(A)
% H = SCATTERD(A,DIM,S,CMAP,FONTSIZE,'label','both','legend','gridded')
%
% INPUT
% A Dataset or matrix
% DIM Number of dimensions: 1,2 or 3 (optional; default: 2)
% S String specifying the colors and markers (optional)
% CMAP Matrix with a color... |
github | jacksky64/imageProcessing-master | vpc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/vpc.m | 5,242 | utf_8 | 3bc522bdc5d792c6303aad94091f0788 | function B = vpc(A, W)
%VPC Voted perceptron classifier
%
% W = VPC(A)
% W = VPC(A, N)
%
% INPUT
% A Dataset
% N Number of sweeps
%
% OUTPUT
% W Voted perceptron classifier
%
% DESCRIPTION
% The classifier trains an ensemble of perceptrons on dataset A. The
% training procedure performs N full sweeps... |
github | jacksky64/imageProcessing-master | featselp.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featselp.m | 5,680 | utf_8 | 95c5a90a0e6120fc254389b69a95cdf7 | %FEATSELP Pudil's floating feature selection (forward)
%
% [W,R] = FEATSELP(A,CRIT,K,T,FID)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or untrained mapping
% (default: 'NN', 1-Nearest Neighbor error)
% K Number of features to select (default: K = 0, select optimal set)
% T T... |
github | jacksky64/imageProcessing-master | labelim.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/labelim.m | 1,675 | utf_8 | 54421f3d90d172ac5de0915a11706f21 | %LABELIM Construct image of object (pixel) labels
%
% IM = LABELIM(A)
% IM = A*LABELIM
%
% INPUT
% A Dataset containing images stored as features
%
% OUTPUT
% IM Image containing the labels of the objects
%
% DESCRIPTION
% For a dataset A containing images stored as features, where each pixel
% corresponds to a... |
github | jacksky64/imageProcessing-master | parzen_map.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/parzen_map.m | 3,716 | utf_8 | d8c30b56a555094142a25709bf69bcce | %PARZEN_MAP Map a dataset on a Parzen densities based classifier
%
% F = PARZEN_MAP(A,W)
%
% INPUT
% A Dataset
% W Trained Parzen classifier mapping (default: PARZENC(A))
%
% OUTPUT
% F Mapped dataset
%
% DESCRIPTION
% Maps the dataset A by the Parzen density based classfier W. F*sigm are the
% posterior... |
github | jacksky64/imageProcessing-master | shiftop.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/shiftop.m | 1,526 | utf_8 | b750bb7f4cdb20bd552bc18639636e71 | %SHIFTOP Shift operating point of classifier
%
% S = SHIFTOP(D,E,C)
% S = SHIFTOP([],E,C);
%
% INPUT
% D Dataset, classification matrix (two classes only)
% E Desired error class N for D*TESTC
% C Index of desired class (default: C = 1)
%
% OUTPUT
% S Mapping, such that E = TES... |
github | jacksky64/imageProcessing-master | fisherm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/fisherm.m | 3,331 | utf_8 | f729ad07dd78730a8680ff448b9c31b4 | %FISHERM Optimal discrimination linear mapping (Fisher mapping, LDA)
%
% W = FISHERM(A,N,ALF)
%
% INPUT
% A Dataset
% N Number of dimensions to map to, N < C, where C is the number of classes
% (default: min(C,K)-1, where K is the number of features in A)
% ALF Preserved variance in the pre-whitening ste... |
github | jacksky64/imageProcessing-master | im_box.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_box.m | 2,487 | utf_8 | 0744833225729dd9a4c7960608872873 | %IM_BOX Find rectangular image in datafile enclosing a blob (0/1 image)
%
% B = IM_BOX(A)
% B = A*IM_BOX
%
% If A is a 0/1 image then B is the same image with all empty (0) border
% columns and rows removed.
%
% B = IM_BOX(A,N)
%
% If A is a 0/1 image then B is the same image, but having in each direction
% N emp... |
github | jacksky64/imageProcessing-master | im_patch.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_patch.m | 5,368 | utf_8 | cee874a6d4aa0b4e1e79057508a0eeb8 | %IM_PATCH Generate patches from images
%
% B = IM_PATCH(A,PSIZE,PNUM,TYPE)
% B = IM_PATCH(A,PSIZE,COORD,'user')
% W = IM_PATCH([],PSIZE,PNUM,TYPE)
% B = A*W
%
% INPUT
% A Dataset or datafile with (multi-band) object images dataset
% PSIZE 2-dimensional patch size. If PSIZE is 1-dimensional square
... |
github | jacksky64/imageProcessing-master | pca.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/pca.m | 1,888 | utf_8 | 4f05b0416c32e4724340e266115a23e8 | %PCA Principal component analysis (PCA or MCA on overall covariance matrix)
%
% [W,FRAC] = PCA(A,N)
% [W,N] = PCA(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions (>= 1) or fraction of variance (< 1)
% to retain; if > 0, perform PCA; otherwise MCA. Default: N = inf.
... |
github | jacksky64/imageProcessing-master | im_threshold.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_threshold.m | 2,565 | utf_8 | c0695bb34cf81afd570191ccd7a06f38 | %IM_THRESHOLD Threshold images stored in a dataset (DIP_Image)
%
% B = IM_THRESHOLD(A,TYPE,PAR,INV)
% B = A*IM_THRESHOLD([],TYPE,PAR,INV)
%
% INPUT
% A Dataset with object images (possibly multi-band)
% TYPE Type of procedure, see below
% PAR Related parameter
% INV If INV = 1, result inver... |
github | jacksky64/imageProcessing-master | nmc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nmc.m | 2,128 | utf_8 | d6c35ba10f1a2d77641d5948b832dd6f | %NMC Nearest Mean Classifier
%
% W = NMC(A)
% W = A*NMC
%
% INPUT
% A Dataset
%
% OUTPUT
% W Nearest Mean Classifier
%
% DESCRIPTION
% Computation of the nearest mean classifier between the classes in the
% dataset A. The use of soft labels is supported. Prior probabilities are
% not used.
%
% The di... |
github | jacksky64/imageProcessing-master | gendats.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendats.m | 2,033 | utf_8 | 5ec6c34e8c8a19bcc26bfd04944c5b6d | %GENDATS Generation of a simple classification problem of 2 Gaussian classes
%
% A = GENDATS (N,K,D,LABTYPE)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K Dimensionality of the dataset to be generated (default: 2).
% D Distance between class means in t... |
github | jacksky64/imageProcessing-master | ffnc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/ffnc.m | 9,651 | utf_8 | 810924cee56981dbe41a86706eeed34c | %FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
% for Levenberg-Marquardt
% A Training dataset
% UNITS Array indicating number of units in each hidden layer (defau... |
github | jacksky64/imageProcessing-master | im_resize.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_resize.m | 3,822 | utf_8 | 9b9aa3cc9eb40cb875da06de9efcddb7 | %IM_RESIZE Mapping for resizing object images in datasets and datafiles
%
% B = IM_RESIZE(A,SIZE,METHOD)
% B = A*IM_RESIZE([],SIZE,METHOD)
%
% INPUT
% A Dataset or datafile
% SIZE Desired size
% METHOD Method, see IMRESIZE
%
% OUTPUT
% B Dataset or datafile
%
% DESCRIPTION
% The obje... |
github | jacksky64/imageProcessing-master | remoutl.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/remoutl.m | 1,593 | utf_8 | 30de9687b8f002b3fc42aa90be9ee9dd | %REMOUTL Remove outliers from a dataset
%
% B = REMOUTL(A,T,P)
% B = A*REMOUTL([],T,P)
%
% INPUT
% A Dataset
% T Threshold for outlier detection (default 3)
% P Fraction of distances passing T (default 0.10)
%
% OUTPUT
% B Dataset
%
% DESCRIPTION
% Outliers in A are removed, other objects are copied to B. Cla... |
github | jacksky64/imageProcessing-master | histm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/histm.m | 4,184 | utf_8 | aa606d4c44c644bd3466cbf920b12c6a | %HISTM Histogramming: mapping of dataset (datafile) to histogram
%
% W = HISTM(A,N)
% W = A*HISTM([],N)
% C = B*W
%
% C = HISTM(B,X)
% C = B*HISTM([],X)
%
%
%
% INPUT
% A Dataset or datafile for defining histogram bins (training)
% N Scalar defining number of histogram bins (defau... |
github | jacksky64/imageProcessing-master | costm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/costm.m | 3,037 | utf_8 | 8d9480389c20643eb572a49a15806a5c | %COSTM Cost mapping, classification using costs
%
% Y = COSTM(X,C,LABLIST)
% W = COSTM([],C,LABLIST)
%
% DESCRIPTION
% Maps the classifier output X (assumed to be posterior probability
% estimates) to the cost-outputs, defined by the cost-matrix C:
%
% C(i,j) = cost of misclassifying an object from class i as cl... |
github | jacksky64/imageProcessing-master | remclass.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/remclass.m | 715 | utf_8 | 015f458b4cb61086e8c3c94574db9072 | %REMCLASS Remove small classes
%
% B = REMCLASS(A,N)
%
% INPUT
% A Dataset
% N Integer, maximum class size to be removed (optional; default 0)
%
% OUTPUT
% B Dataset
%
% DESCRIPTION
% Classes having N objects or less are removed. The corresponding objects
% are made unlabeled. Use SELDAT to r... |
github | jacksky64/imageProcessing-master | clevals.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/clevals.m | 7,795 | utf_8 | 78938416bcf29c02dc21b55343c11a1c | %CLEVALS Classifier evaluation (feature size/learning curve), bootstrap possible
%
% E = CLEVALS(A,CLASSF,FEATSIZE,TRAINSIZES,NREPS,T)
%
% INPUT
% A Training dataset
% CLASSF Classifier to evaluate
% FEATSIZE Vector of feature sizes
% (default: 1:K, where K is the number of feature... |
github | jacksky64/imageProcessing-master | nlfisherm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nlfisherm.m | 3,145 | utf_8 | 72b443c040e6a245d4a94d5ca1cde5d6 | %NLFISHERM Non-linear Fisher Mapping according to Marco Loog
%
% W = NLFISHERM(A,N)
%
% INPUT
% A Dataset
% N Number of dimensions (optional; default: MIN(K,C)-1, where
% K is the dimensionality of A and C is the number of classes)
%
% OUTPUT
% W Non-linear Fisher mapping
%
% DESCRIPTION
% Finds... |
github | jacksky64/imageProcessing-master | mdsc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/mdsc.m | 3,283 | utf_8 | 5aeefec94fce41fbe831891e643a2f93 | %MDSC Manhatten Dissimilarity Space Classification
%
% W = MDSC(A,R,CLASSF)
% W = A*FDSC([],R,CLASSF)
% D = X*W
%
% INPUT
% A Dateset used for training
% R Dataset used for representation
% or a fraction of A to be used for this.
% Default: R = A.
% CLASSF Classi... |
github | jacksky64/imageProcessing-master | isuntrained.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/isuntrained.m | 746 | utf_8 | f722c42ff9acdb259cab4bf5896afa5a | %ISUNTRAINED Test on untrained mapping
%
% I = ISUNTRAINED(W)
% ISUNTRAINED(W)
%
% True if the mapping type of W is 'untrained' (see HELP MAPPINGS).
% If called without an output argument ISUNTRAINED generates
% an error if the mapping type of W is not 'untrained'.
% $Id: isuntrained.m,v 1.1 2009/03/18 16:12:41 ... |
github | jacksky64/imageProcessing-master | rnnc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/rnnc.m | 2,817 | utf_8 | 916bb7ca9fd194f6c83f71f944206b17 | %RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer
% S Standard deviation of weights in an input layer (default: 1)
%
% OUTPUT
% W Trained Random Neural Net classifier
%
% DESCRIPTION
% W is a feed-forward neural net with one hidde... |
github | jacksky64/imageProcessing-master | fdsc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/fdsc.m | 3,173 | utf_8 | cd9903c57175b348ec71705429713f3a | %FDSC Feature based Dissimilarity Space Classification
%
% W = FDSC(A,R,FEATMAP,TYPE,P,CLASSF)
% W = A*FDSC([],R,FEATMAP,TYPE,P,CLASSF)
% D = X*W
%
% INPUT
% A Dateset used for training
% R Dataset used for representation
% or a fraction of A to be used for this.
% D... |
github | jacksky64/imageProcessing-master | spatm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/spatm.m | 2,054 | utf_8 | e46a002860322effbf2030ce585f464d | %SPATM Augment image dataset with spatial label information
%
% E = SPATM(D,S)
% E = D*SPATM([],S)
%
% INPUT
% D image dataset classified by a classifier
% S smoothing parameter (optional, default: sigma = 1.0)
%
% OUTPUT
% E augmented dataset with additional spatial information
%
% ... |
github | jacksky64/imageProcessing-master | matchlab.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/matchlab.m | 2,443 | utf_8 | 4ce3510698452c48d4c4a857a3131926 | %MATCHLAB Compare two labellings and rotate the labels for an optimal match
%
% LABELS = MATCHLAB(LAB1,LAB2)
%
% INPUT
% LAB1,LAB2 Label lists of the same objects
%
% OUTPUT
% LABELS A rotated version of LAB2, optimally matched with LAB1
%
% DESCRIPTION
% LAB1 and LAB2 are label lists for the same objects.... |
github | jacksky64/imageProcessing-master | sequential.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/sequential.m | 3,611 | utf_8 | c0548a91d3772beb03a2c590f1a43189 | %SEQUENTIAL Sequential mapping
%
% V = SEQUENTIAL(W1,W2)
% B = SEQUENTIAL(A,W)
%
% INPUT
% W,W1,W2 Mappings
% A Dataset
%
% OUTPUT
% V Sequentially combined mapping
% B Dataset
%
% DESCRIPTION
% The two mappings W1 and W2 are combined into a single mapping V. Note
% that SEQUENTIAL(W... |
github | jacksky64/imageProcessing-master | gendatd.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatd.m | 2,549 | utf_8 | 2490ae4a0fa5edd6119f172424195e52 | %GENDATD Generation of 'difficult' normally distributed classes
%
% A = GENDATD(N,K,D1,D2,LABTYPE)
%
% INPUT
% N Number of objects in each of the classes (default: [50 50])
% K Dimensionality of the dataset (default: 2)
% D1 Difference in mean in feature 1 (default: 3)
% D2 Differen... |
github | jacksky64/imageProcessing-master | showfigs.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/showfigs.m | 700 | utf_8 | cd1ab9d2bc6692b6dccc8b2cd94faea6 | %SHOWFIGS Show all figures on the screen
%
% SHOWFIGS(K)
%
% Use K figures on a row
function showfigs(k)
h = sort(get(0,'children')); % handles for all figures
n = length(h); % number of figure
if nargin == 0
k = ceil(sqrt(n)); % figures to be shown
end
s = 0.95/k; % screen s... |
github | jacksky64/imageProcessing-master | parsc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/parsc.m | 822 | utf_8 | c12237507f36a7b2589880ab583a1e25 | %PARSC Parse classifier
%
% PARSC(W)
%
% Displays the type and, for combining classifiers, the structure of the
% mapping W.
%
% See also MAPPINGS
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands
% $Id: par... |
github | jacksky64/imageProcessing-master | nusvo.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nusvo.m | 13,099 | utf_8 | 77ea7fcfc5be9ceba0683f334fd54943 | %NUSVO Support Vector Optimizer: NU algorithm
%
% [V,J,NU,C] = NUSVO(K,NLAB,NU,OPTIONS)
%
% INPUT
% K Similarity matrix
% NLAB Label list consisting of -1/+1
% NU Regularization parameter (0 < NU < 1): expected fraction of SV (optional; default: 0.01)
% OPTIONS
% .PD_CHECK force positi... |
github | jacksky64/imageProcessing-master | bagc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/bagc.m | 5,838 | utf_8 | 34c5773a5b24755a649f551adf94c547 | %BAGC Bag classifier for classifying sets of objects
%
% [WBAG,WOBJ] = BAGC(A,OBJCLASSF,BAGINDEX,BAGCOMBC,BAGCLASSF,BAGLAB)
% D = B*WBAG
%
% INPUT
% A Training dataset with object labels and bag indices
% B Test Dataset with index list of bags, stored as label list
% OBJCLASSF Train... |
github | jacksky64/imageProcessing-master | isdatafile.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/isdatafile.m | 432 | utf_8 | 3db749ec603b6905a19079febdbe4ad0 | %ISDATAFILE Test whether the argument is a datafile
%
% N = ISDATAFILE(A);
%
% INPUT
% A Input argument
%
% OUTPUT
% N 1/0 if A is/isn't a datafile
%
% DESCRIPTION
% The function ISDATAFILE test if A is a datafile object.
%
% SEE ALSO
% ISMAPPING, ISDATAIM, ISFEATIM
function n = isdatafile(a)
prtrace(mfilename)... |
github | jacksky64/imageProcessing-master | getlab.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/getlab.m | 1,052 | utf_8 | b98123fcb599f94c58d4cf2fc066238a | %GETLAB Get labels of dataset or mapping
%
% LABELS = GETLAB(A)
% LABELS = GETLAB(W)
%
% INPUT
% A Dataset
% W Mapping
%
% OUTPUT
% LABELS Labels
%
% DESCRIPTION
% Returns the labels of the objects in the dataset A or the feature labels
% assigned by the mapping W.
%
% If A (or W) is neither a dataset no... |
github | jacksky64/imageProcessing-master | nlabeld.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nlabeld.m | 1,491 | utf_8 | 68528c7a976a65375113b0826d3e77c2 | %NLABELD Return numeric labels of classified dataset
%
% NLABELS = NLABELD(Z)
% NLABELS = Z*NLABELD
% NLABELS = NLABELD(A,W)
% NLABELS = A*W*NLABELD
%
% INPUT
% Z Classified dataset, or
% A,W Dataset and classifier mapping
%
% OUTPUT
% NLABELS vector of numeric labels
%
% DESCRIPTION
% Returns the n... |
github | jacksky64/imageProcessing-master | issequential.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/issequential.m | 690 | utf_8 | 423df5d323415d1898676c9dc38ba5de | %ISSEQUENTIAL Test on sequential mapping
%
% N = ISSEQUENTIAL(W)
% ISSEQUENTIAL(W)
%
% INPUT
% W input mapping
%
% OUTPUT
% N logical value
%
% DESCRIPTION
% Returns true for sequential mappings. If no output is required,
% false outputs are turned into errors. This may be used for
% assertion.
%
% SEE AL... |
github | jacksky64/imageProcessing-master | meancov.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/meancov.m | 4,697 | utf_8 | 516e769bd293798e8908454943696bae | %MEANCOV Estimation of the means and covariances from multiclass data
%
% [U,G] = MEANCOV(A,N)
%
% INPUT
% A Dataset
% N Normalization to use for calculating covariances: by M, the number
% of samples in A (N = 1) or by M-1 (default, unbiased, N = 0).
%
% OUTPUT
% U Mean vectors
% G Covarianc... |
github | jacksky64/imageProcessing-master | lmnc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/lmnc.m | 1,729 | utf_8 | d81df5248a7ba509f542ad246d4ee62e | %LMNC Levenberg-Marquardt trained feed-forward neural net classifier
%
% [W,HIST] = LMNC (A,UNITS,ITER,W_INI,T)
%
% INPUT
% A Dataset
% UNITS Vector with numbers of units in each hidden layer (default: [5])
% ITER Number of iterations to train (default: inf)
% W_INI Weight initialization network ... |
github | jacksky64/imageProcessing-master | nbayesc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nbayesc.m | 1,973 | utf_8 | f3a84fd0ca90586f58eeb8c3cb521e93 | %NBAYESC Bayes Classifier for given normal densities
%
% W = NBAYESC(U,G)
%
% INPUT
% U Dataset of means of classes
% G Covariance matrices (optional; default: identity matrices)
%
% OUTPUT
% W Bayes classifier
%
% DESCRIPTION
% Computation of the Bayes normal classifier between a set of classes.
% The m... |
github | jacksky64/imageProcessing-master | im2feat.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im2feat.m | 2,376 | utf_8 | e83cbb9ad8a4892f4b9e358f6649c454 | %IM2FEAT Convert Matlab images or datafile to dataset feature
%
% B = IM2FEAT(IM,A)
%
% INPUT
% IM X*Y image, X*Y*K array of K images, or cell-array of images
% The images may be given as a datafile.
% A Input dataset
%
% OUTPUT
% B Dataset with IM added
%
% DESCRIPTION
% Add standard Matlab ... |
github | jacksky64/imageProcessing-master | datasetconv.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/datasetconv.m | 352 | utf_8 | b95f2e854d2189ab21fc91db2b4dc30c | %DATASETCONV Convert to dataset if needed
%
% A = DATASETCONV(A)
%
% If A is not a dataset it is converted to a dataset.
%
% SEE ALSO
% DATASETS, DATASET
function a = datasetconv(a)
% This is just programmed like this for speed, as
% a = dataset(a) will do the same but involves more checking
if ~isdat... |
github | jacksky64/imageProcessing-master | normm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/normm.m | 2,861 | utf_8 | cb4e55cf8a9dfcb89939f885a33d9420 | %NORMM Apply Minkowski-P distance normalization map
%
% B = A*NORMM(P)
% B = NORMM(A,P)
%
% INPUT
% A Dataset or matrix
% P Order of the Minkowski distance (optional; default: 1)
%
% OUTPUT
% B Dataset or matrix of normalized Minkowski-P distances
%
% DESCRIPTION
% Normalizes the distances of all object... |
github | jacksky64/imageProcessing-master | isobjim.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/isobjim.m | 772 | utf_8 | c465aa4b99a145aff0001ec8b413bdba | %ISOBJIM test if the dataset contains objects that are images
%
% N = ISOBJIM(A)
% ISOBJIM(A)
%
% INPUT
% A input dataset
%
% OUTPUT
% N logical value
%
% DESCRIPTION
% True if dataset contains objects that are images. If no output is required,
% false outputs are turned into errors. This may be used for asse... |
github | jacksky64/imageProcessing-master | datgauss.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/datgauss.m | 2,418 | utf_8 | ace1a3fbf372299804b0c039c7127923 | %DATGAUSS Apply Gaussian filter on images in a dataset
%
% B = DATGAUSS(A,SIGMA)
%
% INPUT
% A Dataset containing images
% SIGMA Standard deviation of Gaussian filter (default 1)
%
% OUTPUT
% B Dataset with filtered images
%
% DESCRIPTION
% All images stored as objects (rows) or as features (column... |
github | jacksky64/imageProcessing-master | prforum.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prforum.m | 94 | utf_8 | f268817c3db7b653655098ff868b5e86 | %PRFORUM
%
% N = PRFORUM(L)
%
%PRTools test
function prforum(k)
prforum_private(k); |
github | jacksky64/imageProcessing-master | qdc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/qdc.m | 4,296 | utf_8 | 4055290392ab0ff63272a6d4c54a5402 | %QDC Quadratic Bayes Normal Classifier (Bayes-Normal-2)
%
% [W,R,S,M] = QDC(A,R,S,M)
% W = A*QDC([],R,S)
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0 <= R,S <= 1
% (optional; default: no regularization, i.e. R,S = 0)
% M Dimension of subspace structure in covariance matrix (... |
github | jacksky64/imageProcessing-master | roc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/roc.m | 5,483 | utf_8 | 8d0022cbb75dfc2d81a59beca6ddc7b7 | %ROC Receiver-Operator Curve
%
% E = ROC(A,W,C,N)
% E = ROC(B,C,N)
%
% INPUT
% A Dataset
% W Trained classifier, or
% B Classification result, B = A*W*CLASSC
% C Index of desired class (default: C = 1)
% N Number of points on the Receiver-Operator Curve (default: 100)
%
% OUTPUT
% E Structure con... |
github | jacksky64/imageProcessing-master | myfixedmapping.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/myfixedmapping.m | 3,358 | utf_8 | d32084aeb276bb7537cce545039c805b | %MYFIXEDMAPPING Skeleton for a user supplied mapping
%
% W = MYFIXEDMAPPING([],PAR]
% W = MYFIXEDMAPPING
% B = A*MYFIXEDMAPPING
% B = A*MYFIXEDMAPPING([],PAR]
% B = MYFIXEDMAPPING(A,PAR)
%
% INPUT
% A Dataset
% PAR Parameter
%
% OUTPUT
% W Mapping definition
% B Dataset A mapped b... |
github | jacksky64/imageProcessing-master | prwaitbaronce.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prwaitbaronce.m | 903 | utf_8 | a3e428983a44e7ed0cab38cf4eac163c | %PRWAITBARONCE Generate single prwaitbar message
%
% PRWAITBARONCE(STRING,PAR)
%
% INPUT
% STRING - String with text to be written in the waitbar,
% e.g. '%i x %i eigenvalue decomposition: '.
% This will be parsed by S = SPRINTF(STRING,PAR{:});
% PAR - scalar or cell array with... |
github | jacksky64/imageProcessing-master | stamp_map.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/stamp_map.m | 4,244 | utf_8 | 63bbd553d399602bb1ab73c0929db82d | %STAMP_MAP Stamping and storing of mappings for fast reusage
%
% C = STAMP_MAP(A,W) or C = A*W
% U = STAMP_MAP(V,W) or U = V*W
%
% This routine is equivalent to MAP except that it stores previous
% results (C or U) and retrieves them when the same inputs (A and W
% or V and W) are given. This is especially go... |
github | jacksky64/imageProcessing-master | featselv.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featselv.m | 1,072 | utf_8 | c4a82524f1333db4a149ebaf8e45203b | %FEATSELV Varying feature selection
%
% W = FEATSELV(A)
% W = A*FEATSELV
%
% Selects all features with a non-zero variance.
% Classifiers can be trained like A*(FEATSELV*LDC([],1E-3)) to make
% use of this feature selection
%
% SEE ALSO
% MAPPINGS, DATASETS, FEATEVAL, FEATSELO, FEATSELB, FEATSELF,
% FEATSE... |
github | jacksky64/imageProcessing-master | isvaldfile.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/isvaldfile.m | 2,064 | utf_8 | add4fdfc5794d47ac7f0165a39792c80 | %ISVALDFILE Test whether the argument is a valid datafile or dataset
%
% N = ISVALDFILE(A);
% N = ISVALDFILE(A,M);
% N = ISVALDFILE(A,M,C);
%
% INPUT
% A Input argument, to be tested on datafile or dataset
% M Minimum number of objects per class in A
% C Minimum number of classes in A
%
... |
github | jacksky64/imageProcessing-master | prrank.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prrank.m | 359 | utf_8 | bd7b267f3361cdd10b064e53104abc0b | %PRRANK Call to RANK() including PRWAITBAR
%
% B = PRRANK(A,tol)
%
% This calls B = RANK(A,tol) and includes a message to PRWAITBAR
% in case of a large A
function B = prrank(varargin)
[m,n] = size(varargin{1});
if min([m,n]) >= 500
prwaitbaronce('Rank of %i x %i matrix ...',[m,n]);
B = rank(varargin{:});
prwaitb... |
github | jacksky64/imageProcessing-master | prprogress.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prprogress.m | 2,167 | utf_8 | 45837f238a625e60b78f5cb0f8f737ca | %PRPROGRESS Report progress of some PRTools iterative routines
%
% PRPROGRESS ON
%
% All progress of all routines will be written to the command window.
%
% PRPROGRESS(FID)
%
% Progress reports will be written to the file with file descriptor FID.
%
% PRPROGRESS(FID,FORMAT,...)
%
% Writes progress messag... |
github | jacksky64/imageProcessing-master | renumlab.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/renumlab.m | 5,523 | utf_8 | ac9d5150e678debdb0b3b1bbdc792d64 | %RENUMLAB Renumber labels
%
% [NLAB,LABLIST] = RENUMLAB(LABELS)
% [NLAB1,NLAB2,LABLIST] = RENUMLAB(LABELS1,LABELS2)
%
% INPUT
% LABELS,LABELS1,LABELS2 Array of labels
%
% OUTPUT
% NLAB,NLAB1,NLAB2 Vector of numeric labels
% LABLIST Unique labels
%
% DESCRIPTION
% If a single a... |
github | jacksky64/imageProcessing-master | rbnc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/rbnc.m | 3,564 | utf_8 | 9664c09d16c78330d2de8a63f79b3fe0 | %RBNC Radial basis function neural network classifier
%
% W = RBNC(A,UNITS)
%
% INPUT
% A Dataset
% UNITS Number of RBF units in hidden layer
%
% OUTPUT
% W Radial basis neural network mapping
%
% DESCRIPTION
% A feed-forward neural network classifier with one hidden layer with
% UNITS radial b... |
github | jacksky64/imageProcessing-master | prdataset.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prdataset.m | 2,177 | utf_8 | 62381781251a635adbb3dbd86682f0e3 | %PRDATASET Load and convert dataset from disk
%
% A = PRDATASET(NAME,M,N)
%
% The dataset given in NAME is loaded from a .mat file and converted
% to the current PRTools definition. Objects and features requested
% by the index vectors M and N are returned.
%
% See PRDATA for loading arbitrary data into a PRTools data... |
github | jacksky64/imageProcessing-master | distmaha.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/distmaha.m | 2,996 | utf_8 | dea417eab0552e74f75db3c579415a17 | %DISTMAHA Mahalanobis distance
%
% D = DISTMAHA (A,U,G)
%
% INPUT
% A Dataset
% U Mean(s) (optional; default: estimate on classes in A)
% G Covariance(s) (optional; default: estimate on classes in A)
%
% OUTPUT
% D Mahalanobis distance matrix
%
% DESCRIPTION
% Computes the M*N Mahanalobis distance matrix of ... |
github | jacksky64/imageProcessing-master | selectim.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/selectim.m | 1,152 | utf_8 | 3422b0b2161ddca7f72bcf27039e7cb3 | %SELECTIM Select one or more images in multiband image or dataset
%
% B = SELECTIM(A,N)
% B = A*SELECTIM([],N)
%
% INPUT
% A Multiband image or dataset containing multiband images
% N Vector or scalar pointing to desired images
%
% OUTPUT
% B New, reduced, multiband image or d... |
github | jacksky64/imageProcessing-master | doublem.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/doublem.m | 452 | utf_8 | af0d345d5316cb22136b39db9aed4eb6 | %DOUBLEM Datafile mapping for conversion to double
%
% B = DOUBLEM(A)
% B = A*DOUBLEM
%
% For datasets B = A, in all other cases A is converted to double.
function a = doublem(a)
prtrace(mfilename);
if nargin < 1 | isempty(a)
a = mapping(mfilename,'fixed');
a = setname(a,'double');
elseif isdat... |
github | jacksky64/imageProcessing-master | im_harris.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_harris.m | 13,420 | utf_8 | 39fab5bf9a6fc25ad377d6d50d407aaa | %IM_HARRIS Harris corner detector
%
% X = IM_HARRIS(A,N,SIGMA)
%
% INPUT
% A Datafile or dataset with images
% N Number of desired Harris points per image (default 100)
% SIGMA Smoothing size (default 3)
%
% OUTPUT
% X Dataset with a [N,3] array with for every image
% x, y ... |
github | jacksky64/imageProcessing-master | filtm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/filtm.m | 3,963 | utf_8 | 7a05a77f873c14f7b9776858cb33417a | %FILTM Mapping to filter objects in datasets and datafiles
%
% B = FILTM(A,FILTER_COMMAND,{PAR1,PAR2,....},SIZE)
% B = A*FILTM([],FILTER_COMMAND,{PAR1,PAR2,....},SIZE)
%
% INPUT
% A Dataset or datafile
% FILTER_COMMAND String with function name
% {PAR1, ... } Cell array with optional parameters to FILTER... |
github | jacksky64/imageProcessing-master | perc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/perc.m | 1,819 | utf_8 | 0074226bc6558b7c32d83f440cdb313d | %PERC Percentile combining classifier
%
% W = PERC(V,P)
% W = V*PERC([],P)
%
% INPUT
% V Set of classifiers
% P Percentile, 0 <= P <= 100
%
% OUTPUT
% W Percentile combining classifier on V
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the... |
github | jacksky64/imageProcessing-master | mds_cs.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/mds_cs.m | 3,083 | utf_8 | 3146fafdcca70fc84952a465cab553ce | % MDS_CS Classical scaling
%
% W = MDS_CS(D,N)
%
% INPUT
% D Square dissimilarity matrix of the size M x M
% N Desired output dimensionality (optional; default: 2)
%
% OUTPUT
% W Classical scaling mapping
%
% DESCRIPTION
% A linear mapping of objects given by a symmetric distance matrix D with
% a zero diago... |
github | jacksky64/imageProcessing-master | rsquared.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/rsquared.m | 727 | utf_8 | c4f5d33e644e6c2cdbbdc3edefb6c7e4 | %RSQUARED R^2 statistic
%
% E = RSQUARED(X,W)
% E = RSQUARED(X*W)
% E = X*W*RSQUARED
%
% INPUT
% X Regression dataset
% W Regression mapping
%
% OUTPUT
% E The R^2-statistic
%
% DESCRIPTION
% Compute the R^2 statistic of regression W on dataset X.
%
% SEE ALSO
% TESTR
% Copyright: D.M... |
github | jacksky64/imageProcessing-master | im_skel.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_skel.m | 816 | utf_8 | 8b6fef0e8819522166a1b00bf7a945ca | %IM_SKEL Skeleton of binary images stored in a dataset (DIP_Image)
%
% B = IM_SKEL(A)
% B = A*IM_SKEL
%
% INPUT
% A Dataset with binary object images dataset
%
% OUTPUT
% B Dataset with skeleton images
%
% SEE ALSO
% DATASETS, DATAFILES, DIP_IMAGE, BSKELETON
% Copyright: R.P.W. Duin, r.p.w.duin@prto... |
github | jacksky64/imageProcessing-master | klm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/klm.m | 1,967 | utf_8 | d875ae891450009ebf5dfea7d03bb079 | %KLM Karhunen-Loeve Mapping (PCA or MCA of mean covariance matrix)
%
% [W,FRAC] = KLM(A,N)
% [W,N] = KLM(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions (>= 1) or fraction of variance (< 1)
% to retain; if > 0, perform PCA; otherwise MCA.
% Default: N = inf.... |
github | jacksky64/imageProcessing-master | im_gray.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_gray.m | 1,529 | utf_8 | f259e1fe56cc90cab8d8e3ac4efe0e7f | %IM_GRAY Conversion of multi-band images into gray images
%
% B = IM_GRAY(A,V)
% B = A*IM_GRAY([],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.
%
% DESCRIPTION
% The m... |
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