plateform stringclasses 1
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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 | loso.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/loso.m | 2,874 | utf_8 | ed1393dabddac9c12947c855895067f7 | %LOSO Leave_One_Set_Out crossvalidation
%
% [E,C,D] = LOSO(A,CLASSF,LABLISTNAME)
% [E,C,D] = LOSO(A,CLASSF,SET_LABELS)
% [E,C,D] = LOSO(A,CLASSF,SET_LABELS,SET_LABLIST)
%
% INPUT
% A Dataset
% CLASSF Untrained classifier
% LABLISTNAME Name of label list in case of multiple labeling
... |
github | jacksky64/imageProcessing-master | quadrc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/quadrc.m | 3,698 | utf_8 | 598d8de6ec7f0611bdec468bf41f2a22 | %QUADRC Quadratic Discriminant Classifier
%
% W = QUADRC(A,R,S)
%
% INPUT
% A Dataset
% R,S 0 <= R,S <= 1, regularization parameters (default: R = 0, S = 0)
%
% OUTPUT
% W Quadratic Discriminant Classifier mapping
%
% DESCRIPTION
% Computation of the quadratic classifier between the classes of the dat... |
github | jacksky64/imageProcessing-master | linearr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/linearr.m | 1,247 | utf_8 | e2721f6121b81980f2a0812e62679997 | %LINEARR Linear regression
%
% Y = LINEARR(X,LAMBDA,N)
%
% INPUT
% X Dataset
% LAMBDA Regularization parameter (default: no regularization)
% N Order of polynomial (optional)
%
% OUTPUT
% Y Linear (or higher order) regression
%
% DESCRIPTION
% Perform a linear regression on dataset X, wit... |
github | jacksky64/imageProcessing-master | svc_nu.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/svc_nu.m | 4,293 | utf_8 | 082db3155588a642885599a02947fefe | %SVC_NU Support Vector Classifier: NU algorithm
%
% This routine is outdated, use NUSVC instead
%
% [W,J,C] = SVC(A,TYPE,PAR,NU,MC,PD)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (optional; default: 'p')
% PAR Kernel parameter (optional; default: 1)
% NU Regularization parameter (0 < NU < 1): e... |
github | jacksky64/imageProcessing-master | fontsize.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/fontsize.m | 721 | utf_8 | 3e26a7fb31b61b0d6d71d9b18153a75c | %FONT_SIZE Set large graphic font
%
% font_size(size)
%
% Set font size for current figure
function font_size(size)
V = axis;
H = get(gcf,'Children');
c1 = [];
for h = H'
if strcmp(get(h,'type'),'axes')
set(get(h,'XLabel'), 'FontSize', size);
set(get(h,'YLabel'), 'FontSize', size);
set(get(h,'Ti... |
github | jacksky64/imageProcessing-master | featsetcc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featsetcc.m | 288 | utf_8 | a053906fa41052ac6d97103075e07f84 | %FEATSETCC Feature set combining 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 [dset,id] = featsetcc(dobj,combc)
error('featsetcc has been replaced by bagcc') |
github | jacksky64/imageProcessing-master | mclassc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/mclassc.m | 3,670 | utf_8 | a230d0335c0818ab2435633ba7d33997 | %MCLASSC Computation of multi-class classifier from 2-class discriminants
%
% W = MCLASSC(A,CLASSF,MODE)
%
% INPUT
% A Dataset
% CLASSF Untrained classifier
% MODE Type of handling multi-class problems (optional; default: 'single')
%
% OUTPUT
% W Combined classifier
%
% DESCRIPTION
% For defaul... |
github | jacksky64/imageProcessing-master | disperror.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/disperror.m | 2,483 | utf_8 | aa9422a1321ed41f48c803488f043e98 | %DISPERROR Display error matrix with information on classifiers and datasets
%
% DISPERROR(DATA,CLASSF,ERROR,STD,FID)
%
% INPUT
% DATA Cell array of M datasets or dataset names (strings)
% CLASSF Cell array of N mappings or mapping names (strings)
% ERROR M*N matrix of (average) error estimates
% STD ... |
github | jacksky64/imageProcessing-master | parzendc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/parzendc.m | 3,112 | utf_8 | 5499152fa3da34991b55b84c764f1a56 | %PARZENDC Parzen density based classifier
%
% [W,H] = PARZENDC(A)
% W = PARZENDC(A,H)
%
% INPUT
% A Dataset
% H Smoothing parameters (optional; default: estimated from A for each class)
%
% OUTPUT
% W Trained Parzen classifier
% H Smoothing parameters, estimated from the data
%
% DESCRIPTION
% For e... |
github | jacksky64/imageProcessing-master | ksmoothr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/ksmoothr.m | 1,034 | utf_8 | 1bb121254911d38d3aed55d5c30bd04d | %KSMOOTHR Kernel smoother
%
% W = KSMOOTHR(X,H)
%
% INPUT
% X Regression dataset
% H Width parameter (default H=1)
%
% OUTPUT
% W Kernel smoother mapping
%
% DESCRIPTION
% Train a kernel smoothing W on data X, with width parameter H.
%
% SEE ALSO
% KNNR, TESTR, PLOTR
% Copyright: D.M.J. Tax, D.M.J... |
github | jacksky64/imageProcessing-master | isparallel.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/isparallel.m | 727 | utf_8 | c236c6aaf876afb62259dc6dea58e2a5 | %ISPARALLEL Test on parallel mapping
%
% N = ISPARALLEL(W)
% ISPARALLEL(W)
%
% INPUT
% W input mapping
%
% OUTPUT
% N logical value
%
% DESCRIPTION
% Returns true for parallel mappings. If no output is required,
% false outputs are turned into errors. This may be used for
% assertion.
%
% SEE ALSO
% ISMAP... |
github | jacksky64/imageProcessing-master | gencirc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gencirc.m | 1,003 | utf_8 | b95f991f81ebe9c78ff8cf68f51694dd | %GENCIRC Generation of a one-class circular dataset
%
% A = GENCIRC(N,S)
%
% INPUT
% N Size of dataset (optional; default: 50)
% S Standard deviation (optional; default: 0.1)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% Generation of a uniformly distributed one-class 2D circular
% dataset with radius 1 ... |
github | jacksky64/imageProcessing-master | averagec.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/averagec.m | 1,493 | utf_8 | 30ebbb9c4bcf1c7042173d6357e742ef | %AVERAGEC Combining of linear classifiers by averaging coefficients
%
% W = AVERAGEC(V)
% W = V*AVERAGEC
%
% INPUT
% V A set of affine base classifiers.
%
% OUTPUT
% W Combined classifier.
%
% DESCRIPTION
% Let V = [V1,V2,V3, ... ] is a set of affine classifiers trained on the same
% classes, then W is the aver... |
github | jacksky64/imageProcessing-master | perlc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/perlc.m | 3,940 | utf_8 | ab6cd9ecba1cb940276cbd81aea4b8b0 | % PERLC - Train a linear perceptron classifier
%
% W = PERLC(A)
% W = PERLC(A,MAXITER,ETA,W_INI,TYPE)
%
% INPUT
% A Training dataset
% MAXITER Maximum number of iterations (default 100)
% ETA Learning rate (default 0.1)
% W_INI Initial weights, as affine mapping, e.g W_INI = NMC(A)
% ... |
github | jacksky64/imageProcessing-master | gpr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gpr.m | 1,399 | utf_8 | ad5febfcbb81308a317595040c2500c5 | %GPR Gaussian Process regression
%
% W = GPR(A,KERNEL,S_noise)
%
%INPUT
% A Dataset
% KERNEL Untrained mapping to compute kernel by A*(A*KERNEL)
% during training, or B*(A*KERNEL) during evaluation with
% dataset B
% S_noise Standard deviation of the noise
%
%OUTPUT
% W Map... |
github | jacksky64/imageProcessing-master | rejectm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/rejectm.m | 1,951 | utf_8 | fa3364ba0a378a5cb9db889c924a2b4c | %REJECTM Rejection mapping
%
% W = REJECTM(A,FRAC)
%
% DESCRIPTION
% Train the threshold of a rejection mapping W such that a fraction FRAC
% of the training data A is rejected. Dataset A is usually the output of
% a classifier. The mapping REJECTM will add one extra reject class.
%
% W = REJECTM(A,FRAC,REJNAME)
... |
github | jacksky64/imageProcessing-master | testp.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/testp.m | 2,691 | utf_8 | b23721ebac675abf1169567eb38380a6 | %TESTP Error estimation of Parzen classifier
%
% E = TESTP(A,H,T)
% E = TESTP(A,H)
%
% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
% parzenc)
% T test dataset (optional)
%
% OUTPUT
% E estimated error rate
%
% DESCRIPTION
% Tests a d... |
github | jacksky64/imageProcessing-master | prtver.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prtver.m | 994 | utf_8 | 1ca6fb7544215befd398757ac4f2ba7d | %PRTVER Get PRTools version
%
%This routine is intended for internal use in PRTools only
function prtversion = prtver
persistent PRTVERSION
if ~isempty (PRTVERSION)
prtversion = PRTVERSION;
return
end
verstring = version;
if strcmp(computer,'MAC2') | verstring(1) == '5';
% name = fileparts(which('fis... |
github | jacksky64/imageProcessing-master | pcaklm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/pcaklm.m | 5,776 | utf_8 | 152497f52bca71043ca54be95472ebff | %PCAKLM Principal Component Analysis/Karhunen-Loeve Mapping
% (PCA or MCA of overall/mean covariance matrix)
%
% [W,FRAC] = PCAKLM(TYPE,A,N)
% [W,N] = PCAKLM(TYPE,A,FRAC)
%
% INPUT
% A Dataset
% TYPE Type of mapping: 'pca' or 'klm'. Default: 'pca'.
% N or FRAC Number of dimensions (>= ... |
github | jacksky64/imageProcessing-master | loglc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/loglc.m | 3,458 | utf_8 | 4c1da4f17c22125827b02b458af22294 | %LOGLC Logistic Linear Classifier
%
% W = LOGLC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Logistic linear classifier
%
% DESCRIPTION
% Computation of the linear classifier for the dataset A by maximizing the
% likelihood criterion using the logistic (sigmoid) function.
% This routine becomes very slow for ... |
github | jacksky64/imageProcessing-master | modeseek.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/modeseek.m | 1,991 | utf_8 | 8fe1d02f08dc5537527b58387dd22cae | %MODESEEK Clustering by mode-seeking
%
% [LAB,J] = MODESEEK(D,K)
%
% INPUT
% D Distance matrix or distance dataset (square)
% K Number of neighbours to search for local mode (default: 10)
%
% OUTPUT
% LAB Cluster assignments, 1..K
% J Indices of modal samples
%
% DESCRIPTION
% A K-NN mo... |
github | jacksky64/imageProcessing-master | plsm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/plsm.m | 2,563 | utf_8 | 3c22241ed5520e5da9e20863af1ae6cb | % PLSM Partial Least Squares Feature Extraction
%
% W = PLSM
% W = PLSM([],MAXLV,METHOD)
%
% [W, INFORM] = PLSM(A,MAXLV,METHOD)
%
% INPUT
% A training dataset
% MAXLV maximal number of latent variables (will be corrected
% if > rank(A));
% MAXLV=inf means MAX... |
github | jacksky64/imageProcessing-master | pls_apply.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/pls_apply.m | 1,626 | utf_8 | 961a8eadfab5964c53573af62c6b64f2 | %pls_apply Partial Least Squares (applying)
%
% Y = pls_apply(X,B)
% Y = pls_apply(X,B,Options)
%
% INPUT
% X [N -by- d_X] the input data matrix, N samples, d_X variables
% B [d_X -by- d_Y] regression matrix: Y_new = X_new*B
% (X_new here after preprocessing, Y_new before
%... |
github | jacksky64/imageProcessing-master | parallel.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/parallel.m | 6,283 | utf_8 | b199f4616786be7d72eee766b4fd7620 | %PARALLEL Combining classifiers in different feature spaces
%
% WC = PARALLEL(W1,W2,W3, ....) or WC = [W1;W2;W3; ...]
% WC = PARALLEL({W1;W2;W3; ...}) or WC = [{W1;W2;W3; ...}]
% WC = PARALLEL(WC,W1,W2, ....) or WC = [WC;W2;W3; ...]
% WC = PARALELL(C);
% WC = PARALLEL(WC,N);
%
% INPUT
% W1,W2,... Ba... |
github | jacksky64/imageProcessing-master | im_fill_norm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_fill_norm.m | 1,152 | utf_8 | fcd880b1620e4cf27165ee515172ac83 | %IM_FILL_NORM Fill and normalize image for display puproses
%
% B = IM_FILL_NORM(A,N,BACKGROUND)
%
%Low level routine for the DATAFILE/SHOW command to display non-square
%images of the datafile A, inside square of NxN pixels. Empty areas are
%filled with gray.
%Empty parts of images are given the value BACKGRO... |
github | jacksky64/imageProcessing-master | isfeatim.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/isfeatim.m | 621 | utf_8 | 9e19b7be2892fcb9bd5d884c251c94c5 | %ISFEATIM
%
% N = ISFEATIM(A);
%
% INPUT
% A Input dataset
%
% OUTPUT
% N 1/0 if dataset A does/doesn't contain images
%
% DESCRIPTION
% True if dataset contains features that are images.
%
% SEE ALSO
% ISDATASET, ISMAPPING, ISDATAIM
% $Id: isfeatim.m,v 1.2 2006/03/08 22:06:58 duin Exp $
function n = isfeati... |
github | jacksky64/imageProcessing-master | pls_prepro.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/pls_prepro.m | 1,715 | utf_8 | d87b6dd6fe49928ae30c51efbc000dbc | % [X,centering,scaling] = pls_prepro(X,centering,scaling, flag)
function [X,centering,scaling] = pls_prepro(X,centering,scaling, flag)
% Copyright: S.Verzakov, serguei@ph.tn.tudelft.nl
% Faculty of Applied Sciences, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands
if nargin<4
flag = 1;... |
github | jacksky64/imageProcessing-master | clevalf.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/clevalf.m | 4,409 | utf_8 | cf5e535696e36f4d01d8c9746d141c1a | %CLEVALF Classifier evaluation (feature size curve)
%
% E = CLEVALF(A,CLASSF,FEATSIZES,LEARNSIZE,NREPS,T,TESTFUN)
%
% INPUT
% A Training dataset.
% CLASSF The untrained classifier to be tested.
% FEATSIZES Vector of feature sizes (default: all sizes)
% LEARNSIZE Number of objects/fraction of ... |
github | jacksky64/imageProcessing-master | distm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/distm.m | 2,392 | utf_8 | 86750607d43a524f999ca5c5de2345f8 | %DISTM Compute square Euclidean distance matrix
%
% D = DISTM(A,B)
% D = DISTM(A);
% D = A*DISTM
%
% INPUT
% A,B Datasets or matrices; B is optional, default B = A
%
% OUTPUT
% D Square Euclidean distance dataset or matrix
%
% DESCRIPTION
% Computation of the square Euclidean distance matrix D betw... |
github | jacksky64/imageProcessing-master | svo.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/svo.m | 5,695 | utf_8 | a9b5f6ada2a4fc27d55d2577eb06d58f | %SVO Support Vector Optimizer
%
% [V,J,C,NU] = SVO(K,NLAB,C,OPTIONS)
%
% INPUT
% K Similarity matrix
% NLAB Label list consisting of -1/+1
% C Scalar for weighting the errors (optional; default: 1)
% OPTIONS
% .PD_CHECK force positive definiteness of the kernel by adding a small constant ... |
github | jacksky64/imageProcessing-master | prcursor.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prcursor.m | 1,012 | utf_8 | f0ffc26e1d4b1085e240482c53ee31cb | %PRCURSOR Show object ident.
%
% PRCURSOR(H)
%
% Enable the datacursor in a scatterplot. This can be used to
% investigate the object identifier by clicking on the object.
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
fu... |
github | jacksky64/imageProcessing-master | clevalb.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/clevalb.m | 5,779 | utf_8 | 49bc4933f35f2f156671760c1c794679 | %CLEVALB Classifier evaluation (learning curve), bootstrap version
%
% E = CLEVALB(A,CLASSF,TRAINSIZES,N)
%
% INPUT
% A Training dataset
% CLASSF Classifier to evaluate
% TRAINSIZES Vector of class sizes, used to generate subsets of A
% (default [2,3,5,7,10,15,20,30,50,70,100])
% ... |
github | jacksky64/imageProcessing-master | klms.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/klms.m | 1,499 | utf_8 | c5280fd52bab9dc81ed54a1061e8b099 | %KLMS Karhunen Loeve Mapping, followed by scaling
%
% [W,FRAC] = KLMS(A,N)
% [W,N] = KLMS(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.
%
% OUTPUT
% W ... |
github | jacksky64/imageProcessing-master | knn_map.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/knn_map.m | 3,533 | utf_8 | 92e336b19065af64fd9f274ee288a896 | %KNN_MAP Map a dataset on a K-NN classifier
%
% F = KNN_MAP(A,W)
%
% INPUT
% A Dataset
% W K-NN classifier trained by KNNC
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps the dataset A by the K-NN classifier W on the [0,1] interval for
% each of the classes that W is trained on. The posteri... |
github | jacksky64/imageProcessing-master | im_measure.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_measure.m | 4,799 | utf_8 | adb1ef01ba08251a32b46ad247177e35 | %IM_MEASURE Computation by DIP_Image of feature measurements
%
% F = IM_MEASURE(A,GRAY,FEATURES)
%
% INPUT
% A Dataset with binary object images dataset (possibly multi-band)
% GRAY Gray-valued images (matched with A, optional)
% FEATURES Features to be computed
%
% OUTPUT
% F Dataset with co... |
github | jacksky64/imageProcessing-master | mds_stress.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/mds_stress.m | 1,539 | utf_8 | 9d18dc6dbb2205f7949662a11fb0146e | %MDS_STRESS - Sammon stress between dissimilarity matrices
%
% E = MDS_STRESS(q,Ds,D)
%
% INPUT
% q Indicator of the Sammon stress; q = -2,-1,0,1,2
% Ds Original distance matrix
% D Approximated distance matrix
%
% OUTPUT
% E Sammon stress
%
% DESCRIPTION
% Computes the Sammon stress between the ori... |
github | jacksky64/imageProcessing-master | closemess.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/closemess.m | 414 | utf_8 | 74b3dc0ce1e07c2c0a6f04f464762113 | %CLOSEMESS Close progress message
%
% CLOSEMESS(FID,N)
%
% Closes a progress message of length N on file-id FID
%
% This routine is obsolete now and just preserved to get
% old code running.
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 260... |
github | jacksky64/imageProcessing-master | gendatsin.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatsin.m | 1,008 | utf_8 | 9b2a9a557eb3beb99a42a96d70edff8d | %GENREGSIN Generate sinusoidal regression data
%
% X = GENDATSIN(N,SIGMA)
%
% INPUT
% N Number of objects to generate
% SIGMA Standard deviation of the noise
%
% OUTPUT
% X Regression dataset
%
% DESCRIPTION
% Generate an artificial regression dataset [X,Y] with:
%
% y = sin(4x) + noise.
%
%... |
github | jacksky64/imageProcessing-master | im_gauss.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_gauss.m | 1,667 | utf_8 | e3bfd4898cd066716bbbcc381e6e454f | %IM_GAUSS Gaussian filter of images stored in a dataset/datafile (Matlab)
%
% B = IM_GAUSS(A,SX,SY)
% B = A*IM_GAUSS([],SX,SY)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% SX Desired horizontal standard deviation for filter, default SX = 1
% SY Desired vertical standard devia... |
github | jacksky64/imageProcessing-master | emclust.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/emclust.m | 7,248 | utf_8 | 239d82760474b6ec630b238a2a51be88 | %EMCLUST Expectation-Maximization clustering
%
% [LABELS,W_EM] = EMCLUST (A,W_CLUST,K,LABTYPE,FID)
%
% INPUT
% A Dataset, possibly labeled
% W_CLUST Cluster model mapping, untrained (default: nmc)
% K Number of clusters (default: 2)
% LABTYPE Label type: 'crisp' or 'soft' (default: label ty... |
github | jacksky64/imageProcessing-master | normal_map.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/normal_map.m | 8,134 | utf_8 | 77e33f0dee2b9f0e6a636ddff92e37d9 | %NORMAL_MAP Map a dataset on normal-density classifiers or mappings
%
% F = NORMAL_MAP(A,W)
%
% INPUT
% A Dataset
% W Mapping
%
% OUTPUT
% F Density estimation for classes in A
%
% DESCRIPTION
% Maps the dataset A by the normal density based classifier or mapping W.
% For each object in A, F returns the ... |
github | jacksky64/imageProcessing-master | circles3d.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/circles3d.m | 930 | utf_8 | bf2367f2ff9b48e17f9421654f4ea159 | % CIRCLES3D Create a data set containing 2 circles in 3 dimensions.
%
% DATA = CIRCLES3D(N)
%
% Creates a data set containing N points in 3 dimensions.
%
% If N is a vector of sizes, exactly N(I) objects are generated
% for class I, I = 1,2.Default: N = [50 50].
%
% See also DATASETS, PRDATASETS
% Copyright: E. Pe... |
github | jacksky64/imageProcessing-master | nodatafile.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nodatafile.m | 442 | utf_8 | 63e4b7986ff24d4537c11522a30c50f2 | %NODATAFILE Error return in case of datafile
%
% NODATAFILE
%
% Error message
%
% B = NODATAFILE(A)
% B = A*NODATAFILE
%
% Error message in case A is a datafile, otherwise B = A
function a = nodatafile(a)
if (nargin == 0 & nargout == 0) | (nargin == 1 & isdatafile(a) & nargout == 0)
error('prtools:nodatafile',... |
github | jacksky64/imageProcessing-master | gendatr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatr.m | 784 | utf_8 | 5282de6dceaaa18c3d45f24df4b4b109 | %GENDATR Generation of regression data
%
% A = GENDATR(X,Y)
%
% INPUT
% X data matrix
% Y target values
%
% OUTPUT
% A regression dataset
%
% DESCRIPTION
% Generate a regression data from the data X and the target values Y.
%
% SEE ALSO
% SCATTERR, GENDATSINC
% Copyright: D.M.J. Tax, D.M.J.Tax@prt... |
github | jacksky64/imageProcessing-master | tree_map.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/tree_map.m | 2,570 | utf_8 | cfb50d5529a9f524e2f717ffeeeb2533 | %TREE_MAP Map a dataset by binary decision tree
%
% F = TREE_MAP(A,W)
%
% INPUT
% A Dataset
% W Decision tree mapping
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps the dataset A by the binary decision tree classifier W on the
% [0,1] interval for each of the classes W is trained on. The
% pos... |
github | jacksky64/imageProcessing-master | nu_svro.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nu_svro.m | 8,512 | utf_8 | f5e1da68470cd4080b3b5e0b71ceb0f4 | %NU_SVRO Support Vector Optimizer
%
% [V,J] = NU_SVRO(K,Y,C)
%
% INPUT
% K Similarity matrix
% NLAB Label list consisting of -1/+1
% C Scalar for weighting the errors (optional; default: 10)
%
% OUTPUT
% V Vector of weights for the support vectors
% J Index vector pointing to the support ve... |
github | jacksky64/imageProcessing-master | lines5d.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/lines5d.m | 1,045 | utf_8 | 97363967a36f35b3569e57b8804c04df | %LINES5D Generates three 5-dimensional lines
%
% A = LINES5D(N);
%
% Generates a data set of N points, on 3 non-crossing, non-parallel lines
% in 5 dimensions.
%
% If N is a vector of sizes, exactly N(I) objects are generated
% for class I, I = 1,2.Default: N = [50 50 50].
%
% See also DATASETS, PRDATASETS
% Copyrig... |
github | jacksky64/imageProcessing-master | classnames.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/classnames.m | 1,827 | utf_8 | 5c6e1e8f88cda024ae125da56f552a6b | %CLASSNAMES Get names of classes of dataset or classifier
%
% NAMES = CLASSNAMES(A,C)
% NAMES = CLASSNAMES(W,C)
%
% INPUT
% A Dataset
% W Trained classifier
% C Class number(s) in class label list, default: all
%
% OUTPUT
% NAMES Names of classes (strings or numbers)
%
% DESCRIPTION
% Returns the ... |
github | jacksky64/imageProcessing-master | pinvr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/pinvr.m | 2,833 | utf_8 | c33e6d35b076b6ba37cda9cc196628b9 | %PINVR PSEUDO-INVERSE REGRESSION (PCR)
%
% [W,J,C] = PINVR(A,TYPE,PAR,C,SVR_TYPE,EPS_TOL,MC,PD)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (optional; default: 'p')
% PAR Kernel parameter (optional; default: 1)
%
% MC Do or do not data mean-centering (optional; default: 1 (to do))
% PD Do o... |
github | jacksky64/imageProcessing-master | parzenc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/parzenc.m | 4,533 | utf_8 | 26b1e34406d437806141f7014b7a5bc5 | %PARZENC Optimisation of the Parzen classifier
%
% [W,H] = PARZENC(A)
% W = PARZENC(A,H,FID)
%
% INPUT
% A dataset
% H smoothing parameter (may be scalar, vector of per-class
% parameters, or matrix with parameters for each class (rows) and
% dimension (columns))
% FID File ID to write progres... |
github | jacksky64/imageProcessing-master | datasetm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/datasetm.m | 841 | utf_8 | 732f812706da1496992bb0c3c92127af | %DATASETM Mapping conversion to dataset
%
% B = DATASETM(A)
% B = A*DATASETM
%
% INPUT
% A Datafile or double array
%
% OUTPUT
% B DATASET
%
% DESCRIPTION
% This command is almost identical to B = DATASET(A), except that it
% supports the mapping type of construct: B = A*DATASETM. This may be... |
github | jacksky64/imageProcessing-master | prversion.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prversion.m | 726 | utf_8 | 31fae3be7d1e6e9b4cc4eca5bf907ad6 | %PRVERSION PRtools version number
%
% [VERSION,STR,DATE] = PRVERSION
%
% OUTPUT
% VERSION Version number (double)
% STR Version number (string)
% DATE Version date (string)
%
% DESCRIPTION
% Returns the numerical version number of PRTools VER (e.g. VER = 3.2050)
% and as a string, e.g. STR = '3.2.5'. In DAT... |
github | jacksky64/imageProcessing-master | im_center.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_center.m | 1,438 | utf_8 | 00ed7fe66c0c585fdbfffedd6331d429 | %IM_CENTER Shift all binary images in dataset: center to center of gravity
%
% B = IM_CENTER(A)
% B = A*IM_CENTER
%
% The objects in the binary images are shifted such that their centers of
% gravities are in the image center.
%
% B = IM_CENTER(A,N)
%
% In all directions N rows and columns are added after shifti... |
github | jacksky64/imageProcessing-master | gendatlin.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatlin.m | 940 | utf_8 | f1930c8927d3b84b82eba5af2f1893ac | %GENDATLIN Generation of linear regression data
%
% A = GENDATLIN(N,B0,B1,SIGMA)
%
% INPUT
% N Number of objects to generate
% B0 Offset
% B1 Slope
% SIGMA Standard deviation of the noise
%
% OUTPUT
% A Regression dataset
%
% DESCRIPTION
% Generate regression data A, containing N ... |
github | jacksky64/imageProcessing-master | image_dbr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/image_dbr.m | 18,808 | utf_8 | 9b920c6c4de759707dd093ddb4db2928 | function varargout = image_dbr(varargin)
%IMAGE_DBR M-file for image_dbr.fig
% IMAGE_DBR, by itself, creates a new IMAGE_DBR or raises the existing
% singleton*.
%
% H = IMAGE_DBR returns the handle to a new IMAGE_DBR or the handle to
% the existing singleton*.
%
% IMAGE_DBR('Property','Value',... |
github | jacksky64/imageProcessing-master | wvotec.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/wvotec.m | 3,605 | utf_8 | e97b52d375f85b99e5bfbf4719945011 | %WVOTEC Weighted combiner (Adaboost weights)
%
% W = WVOTEC(A,V) compute weigths and store
% W = WVOTEC(V,U) Construct weighted combiner using weights U
%
% INPUT
% A Labeled dataset
% V Parallel or stacked set of trained classifiers
% U Set of classifier weights
%
% OUTPUT
% ... |
github | jacksky64/imageProcessing-master | im_mean.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_mean.m | 1,216 | utf_8 | 2bd973e381a00889d4869daeb84c6e27 | %IM_MEAN Computation of the centers of gravity of images
%
% B = IM_MEAN(A)
% B = A*IM_MEAN
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
%
% OUTPUT
% B Dataset with centers-of-gravity replacing images
% (possibly multi-band). The first component is always meas... |
github | jacksky64/imageProcessing-master | preig.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/preig.m | 419 | utf_8 | 3ab159964b67953da77039eaf03ca236 | %PREIG Call to EIG() including PRWAITBAR
%
% [E,D] = PREIG(A)
%
% This calls [E,D] = EIG(A) and includes a message to PRWAITBAR
% in case of a large A
function [E,D] = preig(A)
[m,n] = size(A);
if min([m,n]) > 500
prwaitbaronce('Computing %i x %i eigenvectors ...',[m,n]);
if nargout < 2
E = eig(A);
else
[E,D]... |
github | jacksky64/imageProcessing-master | mlrc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/mlrc.m | 3,609 | utf_8 | abbaafe79e6764055df8ab5351d3f24d | % MLRC Muli-response Linear Regression Combiner
%
% W = A*(WU*MLRC)
% W = WT*MLRC(B*WT)
% D = C*W
%
% INPUT
% A Dataset used for training base classifiers as well as combiner
% B Dataset used for training combiner of trained base classifiers
% C Dataset used for testing (executing) the combiner
... |
github | jacksky64/imageProcessing-master | obj2feat.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/obj2feat.m | 421 | utf_8 | a2aea409c80661f5c8384fd5c33e10b6 | %OBJ2FEAT Transform object images to feature images in dataset
%
% B = OBJ2FEAT(A)
%
% INPUT
% A Dataset with object images, possible with multiple bands.
%
% OUTPUT
% B Dataset with features images.
%
% SEE ALSO
% DATASETS, IM2OBJ, IM2FEAT, DATA2IM, FEAT2OBJ
function b = obj2feat(a)
prt... |
github | jacksky64/imageProcessing-master | minc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/minc.m | 1,724 | utf_8 | 16990543f698f89a59801ae3c4ca10d9 | %MINC Minimum combining classifier
%
% W = MINC(V)
% W = V*MINC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Minimum combining classifier on V
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the minimum combiner: it selects the class
% with th... |
github | jacksky64/imageProcessing-master | knnr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/knnr.m | 986 | utf_8 | 6f361a2a60209246c49a0820cef1a076 | %KNNR Nearest neighbor regression
%
% Y = KNNR(X,K)
%
% INPUT
% X Regression dataset
% K number of neighbors (default K=3)
%
% OUTPUT
% Y k-nearest neighbor regression
%
% DESCRIPTION
% Define a k-Nearest neighbor regression on dataset X.
%
% SEE ALSO
% LINEARR, TESTR, PLOTR
% Copyright: D.M.J. Tax,... |
github | jacksky64/imageProcessing-master | kmeans.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/kmeans.m | 3,529 | utf_8 | b75218508c7fee80e8ca7e0ba1ced02a | %KMEANS k-means clustering
%
% [LABELS,A] = KMEANS(A,K,MAXIT,INIT,FID)
%
% INPUT
% A Matrix or dataset
% K Number of clusters to be found (optional; default: 2)
% MAXIT maximum number of iterations (optional; default: 50)
% INIT Labels for initialisation, or
% 'rand' : take at random... |
github | jacksky64/imageProcessing-master | im_norm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_norm.m | 974 | utf_8 | 108b2fee72ae6fd49c5ae1efc8afac78 | %IM_NORM Mapping for normalizing images: mean, variance
%
% B = IM_NORM(A)
% B = A*IM_NORM
%
% INPUT
% A Dataset or datafile
%
% OUTPUT
% B Dataset or datafile
%
% DESCRIPTION
% The objects stored as images in the dataset or datafile A are normalised
% w.r.t. their mean (0) and variance (1)... |
github | jacksky64/imageProcessing-master | logdens.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/logdens.m | 1,743 | utf_8 | f19d2f4edeb5a881769faebd6e7e4b2c | %LOGDENS Force density based classifiers to use log-densities
%
% V = LOGDENS(W)
% V = W*LOGDENS
%
% INPUT
% W Density based trained classifier
%
% OUTPUT
% V Log-density based trained classifier
%
% DESCRIPTION
% Density based classifiers suffer from a low numeric accuracy in the tails
% of th... |
github | jacksky64/imageProcessing-master | plsr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/plsr.m | 2,956 | utf_8 | 5ca665d50587aa96474a51fd0a120597 | % PLSR Partial Least Squares Regression
%
% W = PLSR
% W = PLSR([],MAXLV,METHOD)
%
% [W, INFORM] = PLSR(A,MAXLV,METHOD)
%
% INPUT
% A training dataset
% MAXLV maximal number of latent variables (will be corrected
% if > rank(A));
% MAXLV=inf means MAXLV=min(s... |
github | jacksky64/imageProcessing-master | im_select_blob.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_select_blob.m | 935 | utf_8 | e1e9f866b26bbf9e94a36e83d99d1fe3 | %IM_SELECT_BLOB Select largest blob in binary images in dataset (DIP_Image)
%
% B = IM_SELECT_BLOB(IM)
%
% Just the largest object in the image is returned.
%
% SEE ALSO
% DATASETS, DATAFILES, DIP_IMAGE
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031... |
github | jacksky64/imageProcessing-master | featrank.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featrank.m | 1,548 | utf_8 | 1b6fbcb41238f457e3235100517ed770 | %FEATRANK Feature ranking on individual performance for classification
%
% [I,F] = FEATRANK(A,CRIT,T)
%
% INPUT
% A input dataset
% CRIT string name of a method or untrained mapping
% T validation dataset (optional)
%
% OUTPUT
% I vector with sorted feature indices
% F ... |
github | jacksky64/imageProcessing-master | udc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/udc.m | 1,305 | utf_8 | 5f7a31ca4be7e6246f97f82f5ef2c63d | %UDC Uncorrelated normal based quadratic Bayes classifier (BayesNormal_U)
%
% W = UDC(A)
% W = A*UDC
%
% INPUT
% A input dataset
%
% OUTPUT
% W output mapping
%
% DESCRIPTION
% Computation a quadratic classifier between the classes in the
% dataset A assuming normal densities with uncorrelated features.
%
% T... |
github | jacksky64/imageProcessing-master | naivebc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/naivebc.m | 5,420 | utf_8 | 02e1f3a2851e88570a15d684978299f5 | %NAIVEBC Naive Bayes classifier
%
% W = NAIVEBC(A,N)
% W = A*NAIVEBC([],N)
%
% W = NAIVEBC(A,DENSMAP)
% W = A*NAIVEBC([],DENSMAP)
%
% INPUT
% A Training dataset
% N Scalar number of bins (default: 10)
% DENSMAP Untrained mapping for density estimation
%
% OUTPUT
% W Naive Bayes classifi... |
github | jacksky64/imageProcessing-master | im_profile.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_profile.m | 1,839 | utf_8 | 4ac4ef7c3a6157021c8a985dd9cd1c34 | %IM_PROFILE Computation of horizontal and vertical image profile
%
% P = IM_PROFILE(A,NX,NY)
% P = A*IM_PROFILE([],NX,NY)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% NX Number of bins for horizontal profile
% NY Number of bins for vertical profile
%
% OUTPUT
% P ... |
github | jacksky64/imageProcessing-master | plotf.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/plotf.m | 2,216 | utf_8 | e952c5f78e0c02fd34f82ba6895f5e72 | %PLOTF Plot feature distribution, special version
%
% h = PLOTF(A,N)
%
% Produces 1-D density plots for all the features in dataset A. The
% densities are estimated using PARZENML. N is the number of
% feature density plots on a row.
%
% See also DATASETS, PARZENML
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.... |
github | jacksky64/imageProcessing-master | mds_init.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/mds_init.m | 2,981 | utf_8 | d3873ccbbaa3a28678a465dd0a47b32b | %MDS_INIT Initialization for MDS (variants of Sammon) mapping
%
% Y = MDS_INIT (D,N,INIT)
%
% INPUT
% D Square dissimilarity matrix of the size M x M
% N Desired output dimensionality (optional; default: 2)
% INIT Initialization method (optional; default: 'randnp')
%
% OUTPUT
% Y Initial configuration for ... |
github | jacksky64/imageProcessing-master | plotm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/plotm.m | 4,530 | utf_8 | af07489432cd67f10f4d42a6a96e2fc6 | %PLOTM Plot mapping values, contours or surface
%
% H = PLOTM(W,S,N)
%
% INPUT
% W Trained mapping
% S Plot strings, or scalar selecting type of plot
% 1: density plot;
% 2: contour plot (default);
% 3: 3D surface plot;
% 4: 3D surface plot above 2D contour plot;
% ... |
github | jacksky64/imageProcessing-master | datunif.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/datunif.m | 1,690 | utf_8 | c63527e249a4fe53eaca6011b7f02698 | %DATUNIF Apply uniform filter on images in a dataset
%
% B = DATUNIF(A,NX,NY)
%
% INPUT
% A Dataset containing images
% NX,NY Filtersize in X- and Y-direction (default: NY = NX)
%
% OUTPUT
% B Dataset with filtered images
%
% DESCRIPTION
% All images stored as objects (rows) or as features (colum... |
github | jacksky64/imageProcessing-master | regoptc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/regoptc.m | 5,370 | utf_8 | 02e08726d2d767122b8a1c7c730e6aed | %REGOPTC Optimise regularisation and complexity parameters by crossvalidation
%
% [W,PARS] = REGOPTC(A,CLASSF,PARS,DEFS,NPAR,PAR_MIN_MAX,TESTFUN,REALINT)
%
% INPUT
% A Dataset, training set
% CLASSF Untrained classifiers (mapping)
% PARS Cell array with parameters for CLASSF
% DEFS Default... |
github | jacksky64/imageProcessing-master | gendatc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatc.m | 2,506 | utf_8 | 6b89a3ef6b64f2046f10c2d6190fd8cc | %GENDATC Generation of two spherical classes with different variances
%
% A = GENDATC(N,K,U,LABTYPE)
%
% INPUT
% N Vector with class sizes (default: [50,50])
% K Dimensionality of the dataset (default: 2)
% U Mean of class 1 (default: 0)
% LABTYPE 'crisp' or 'soft' labels (default: 'cri... |
github | jacksky64/imageProcessing-master | gridsize.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gridsize.m | 1,279 | utf_8 | d7cf4b33766da4b039ee2ecd0d4dbf0a | %GRIDSIZE Set gridsize used in the plot commands
%
% O = GRIDSIZE(N)
%
% INPUT
% N New grid size (optional, default: display current gridsize)
%
% OUTPUT
% O New grid size (optional)
%
% DESCRIPTION
% The initial gridsize is 30, enabling fast plotting of PLOTC and PLOTM.
% This is, however, insufficien... |
github | jacksky64/imageProcessing-master | gendatsinc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatsinc.m | 912 | utf_8 | 28f4043efaddf36174a9db40003229ae | %GENDATSINC Generate Sinc data
%
% A = GENDATSINC(N,SIGMA)
%
% INPUT
% N Number of objects to generate
% SIGMA Standard deviation of the noise (default SIGMA=0.1)
%
% OUTPUT
% A Regression dataset
%
% DESCRIPTION
%
% Generate the standard 1D Sinc data containing N objects, with Gaussian
% noise... |
github | jacksky64/imageProcessing-master | parzenml.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/parzenml.m | 5,834 | utf_8 | c96aea24e5e46fcc58f6c854e494f330 | %PARZENML Optimum smoothing parameter in Parzen density estimation.
%
% H = PARZENML(A)
%
% INPUT
% A Input dataset
%
% OUTPUT
% H Scalar smoothing parameter (in case of crisp labels)
% Vector with smoothing parameters (in case of soft labels)
%
% DESCRIPTION
% Maximum likelihood estimation for th... |
github | jacksky64/imageProcessing-master | lassor.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/lassor.m | 973 | utf_8 | ba861862740a37071cd2dbdd02b46801 | %LASSOR LASSO regression
%
% W = LASSOR(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter
%
% OUTPUT
% W LASSO regression mapping
%
% DESCRIPTION
% The 'Least Absolute Shrinkage and Selection Operator' regression,
% using the regularization parameter LAMBDA.
%
% SEE AL... |
github | jacksky64/imageProcessing-master | wlsFilter.m | .m | imageProcessing-master/edgeAwareFilters/wlsFilter/wlsFilter.m | 2,515 | utf_8 | 364d9ee14f487f190131610f8461a4a5 | %
% original src: http://www.cs.huji.ac.il/~danix/epd/wlsFilter.m
% original author: Dani Lischinski
% <danix@mail.huji.ac.il>
% http://www.cs.huji.ac.il/~danix/
%
function OUT = wlsFilter(IN, lambda, alpha, L)
%WLSFILTER Edge-preserving smoothing based on the weighted least squares(WLS)
% optimization framewor... |
github | jacksky64/imageProcessing-master | bilateralFilter.m | .m | imageProcessing-master/edgeAwareFilters/bilateralFilter/bilateralFilter.m | 6,854 | utf_8 | 55e1c9ea9a2c9a29a09d57cc3da742c9 | %
% original src: http://people.csail.mit.edu/jiawen/software/bilateralFilter.m
% original author: Jiawen (Kevin) Chen
% <jiawen@csail.mit.edu>
% http://people.csail.mit.edu/jiawen/
%
% output = bilateralFilter( data, edge, ...
% edgeMin, edgeMax, ...
% sigmaSpati... |
github | jacksky64/imageProcessing-master | domainTransform.m | .m | imageProcessing-master/edgeAwareFilters/domainTransform/domainTransform.m | 6,133 | utf_8 | edf22a2a39db28d4f8e098ce7eab590d | % NC Domain transform normalized convolution edge-preserving filter.
%
% F = NC(img, sigma_s, sigma_r, num_iterations, joint_image)
%
% Parameters:
% img Input image to be filtered.
% sigma_s Filter spatial standard deviation.
% sigma_r Filter range standard deviation.
% num... |
github | jacksky64/imageProcessing-master | RF.m | .m | imageProcessing-master/edgeAwareFilters/domainTransform/RF.m | 4,586 | utf_8 | 68802a817292d988927c2ebcd90b8e91 | % RF Domain transform recursive edge-preserving filter.
%
% F = RF(img, sigma_s, sigma_r, num_iterations, joint_image)
%
% Parameters:
% img Input image to be filtered.
% sigma_s Filter spatial standard deviation.
% sigma_r Filter range standard deviation.
% num_iterations ... |
github | jacksky64/imageProcessing-master | NC.m | .m | imageProcessing-master/edgeAwareFilters/domainTransform/NC.m | 6,120 | utf_8 | fadc0cc0bab9db203394ccc9c4d2b953 | % NC Domain transform normalized convolution edge-preserving filter.
%
% F = NC(img, sigma_s, sigma_r, num_iterations, joint_image)
%
% Parameters:
% img Input image to be filtered.
% sigma_s Filter spatial standard deviation.
% sigma_r Filter range standard deviation.
% num... |
github | jacksky64/imageProcessing-master | IC.m | .m | imageProcessing-master/edgeAwareFilters/domainTransform/IC.m | 8,210 | utf_8 | bd552db5ff4ac282aeff763f73d9bfa4 | % IC Domain transform interpolated convolution edge-preserving filter.
%
% F = IC(img, sigma_s, sigma_r, num_iterations, joint_image)
%
% Parameters:
% img Input image to be filtered.
% sigma_s Filter spatial standard deviation.
% sigma_r Filter range standard deviation.
% n... |
github | jacksky64/imageProcessing-master | reconstruct_laplacian_pyramid.m | .m | imageProcessing-master/edgeAwareFilters/localLaplacian/reconstruct_laplacian_pyramid.m | 1,035 | utf_8 | 70a32d1857a137731c2af429fa31e3fa | % Reconstruction of image from Laplacian pyramid
%
% Arguments:
% pyramid 'pyr', as generated by function 'laplacian_pyramid'
% subwindow indices 'subwindow', given as [r1 r2 c1 c2] (optional)
%
% tom.mertens@gmail.com, August 2007
% sam.hasinoff@gmail.com, March 2011 [modified to handle subwindows]
%
%
% More in... |
github | jacksky64/imageProcessing-master | laplacian_pyramid.m | .m | imageProcessing-master/edgeAwareFilters/localLaplacian/laplacian_pyramid.m | 1,204 | utf_8 | e9029b66ed513370880964ed7feb583d | % Contruction of Laplacian pyramid
%
% Arguments:
% image 'I'
% 'nlev', number of levels in the pyramid (optional)
% subwindow indices 'subwindow', given as [r1 r2 c1 c2] (optional)
%
% tom.mertens@gmail.com, August 2007
% sam.hasinoff@gmail.com, March 2011 [modified to handle subwindows]
%
%
% More information... |
github | jacksky64/imageProcessing-master | gaussian_pyramid.m | .m | imageProcessing-master/edgeAwareFilters/localLaplacian/gaussian_pyramid.m | 754 | utf_8 | cae3a399a2b8402078ae3fdb967093e8 | % Construction of Gaussian pyramid
%
% Arguments:
% image 'I'
% 'nlev', number of levels in the pyramid (optional)
% subwindow indices 'subwindow', given as [r1 r2 c1 c2] (optional)
%
% tom.mertens@gmail.com, August 2007
% sam.hasinoff@gmail.com, March 2011 [modified to handle subwindows]
%
function pyr = gaus... |
github | jacksky64/imageProcessing-master | pyramid_filter.m | .m | imageProcessing-master/edgeAwareFilters/localLaplacian/pyramid_filter.m | 404 | utf_8 | e4ad49a0eb06ac9461d5eca754362697 | % This is a 2D separable low pass filter for constructing Gaussian and
% Laplacian pyramids, built from a 1D 5-tap low pass filter.
%
% tom.mertens@gmail.com, August 2007
% sam.hasinoff@gmail.com, March 2011 [imfilter faster with 2D filter]
%
function f = pyramid_filter()
f = [.05, .25, .4, .25, .05]; % original [B... |
github | jacksky64/imageProcessing-master | downsample.m | .m | imageProcessing-master/edgeAwareFilters/localLaplacian/downsample.m | 1,192 | utf_8 | 7181a465fce3611807bbaf1b8cc1cfc9 | % Downsampling procedure.
%
% Arguments:
% 'I': image
% downsampling filter 'filter', should be a 2D separable filter.
% 'border_mode' should be 'circular', 'symmetric', or 'replicate'. See 'imfilter'.
% subwindow indices 'subwindow', given as [r1 r2 c1 c2] (optional)
%
% tom.mertens@gmail.com, August 2007
% sa... |
github | jacksky64/imageProcessing-master | lapfilter_core.m | .m | imageProcessing-master/edgeAwareFilters/localLaplacian/lapfilter_core.m | 3,421 | utf_8 | 322bf34feaded4e8e02a88ea5334fb41 | % Laplacian Filtering
% - public Matlab implementation for reproducibility
% - about 30x slower than our single-thread C++ version
%
% This script implements the core image processing algorithm
% described in Paris, Hasinoff, and Kautz, "Local Laplacian Filters:
% Edge-aware Image Processing with a Laplacian Pyra... |
github | jacksky64/imageProcessing-master | upsample.m | .m | imageProcessing-master/edgeAwareFilters/localLaplacian/upsample.m | 1,455 | utf_8 | 00e6ea292a13419fd7dcb31030dc82fd | % Upsampling procedure.
%
% Argments:
% 'I': image
% 'filter': 2D separable upsampling filter
% parent subwindow indices 'subwindow', given as [r1 r2 c1 c2]
%
% tom.mertens@gmail.com, August 2007
% sam.hasinoff@gmail.com, March 2011 [handle subwindows, reweighted boundaries]
%
function R = upsample(I, filter, s... |
github | jacksky64/imageProcessing-master | lapfilter.m | .m | imageProcessing-master/edgeAwareFilters/localLaplacian/lapfilter.m | 4,441 | utf_8 | 63a454542b341b47dbc6c0498e09e930 | % Laplacian Filtering
% - public Matlab implementation for reproducibility
% - about 30x slower than our single-thread C++ version
%
% This script implements edge-aware detail and tone manipulation as
% described in Paris, Hasinoff, and Kautz, "Local Laplacian Filters:
% Edge-aware Image Processing with a Laplaci... |
github | jacksky64/imageProcessing-master | localExtrema.m | .m | imageProcessing-master/edgeAwareFilters/localExtrema/localExtrema.m | 1,486 | iso_8859_13 | a49498397d018caeabfa95129072da39 | %
% [M, Sminima, Smaxima, Eminima, Emaxima, D] = localExtrema(I, k)
%
% Local Extrema filter
%
% I: the input image data
% Y: the reference/cross/joint data, default to luminance(I)
% k: the width of neighborhood for idenfication of local minima/maxima
% default to 3
%
% M: smoothed image (base)
% Sminima: local min... |
github | jacksky64/imageProcessing-master | l0Minimization.m | .m | imageProcessing-master/edgeAwareFilters/l0Minimization/l0Minimization.m | 2,319 | utf_8 | 6d1efb02aba3e8da95d8d1c8f6c435ac | % Distribution code Version 1.0 -- 09/23/2011 by Jiaya Jia Copyright 2011, The Chinese University of Hong Kong.
%
% The Code is created based on the method described in the following paper
% [1] "Image Smoothing via L0 Gradient Minimization", Li Xu, Cewu Lu, Yi Xu, Jiaya Jia, ACM Transactions on Graphics,
% (... |
github | jacksky64/imageProcessing-master | kde.m | .m | imageProcessing-master/kde/kde.m | 5,629 | utf_8 | 3e2bd285297fe3ee3a2e8bc3fe7b3c00 | function [bandwidth,density,xmesh,cdf]=kde(data,n,MIN,MAX)
% Reliable and extremely fast kernel density estimator for one-dimensional data;
% Gaussian kernel is assumed and the bandwidth is chosen automatically;
% Unlike many other implementations, this one is immune to problems
% caused by mul... |
github | jacksky64/imageProcessing-master | GraphCut.m | .m | imageProcessing-master/segmentation/GCmex1.9/GraphCut.m | 15,577 | utf_8 | 5b4177c7da3c1f8130580912c90db429 | function [gch, varargout] = GraphCut(mode, varargin)
%
% Performing Graph Cut energy minimization operations on a 2D grid.
%
% Usage:
% [gch ...] = GraphCut(mode, ...);
%
%
% Inputs:
% - mode: a string specifying mode of operation. See details below.
%
% Output:
% - gch: A handle to ... |
github | jacksky64/imageProcessing-master | lse_bfe_3Phase.m | .m | imageProcessing-master/segmentation/levelset_segmentation_biasCorrection_v1/levelset_segmentation_biasCorrection_v1/lse_bfe_3Phase.m | 3,611 | utf_8 | c4544181814d6f42f72d47a243691641 | function [u, b, C]= lse_bfe_3Phase(u,Img,b,Ksigma,KONE, nu,timestep,mu, epsilon,Iter)
% This code implements the level set evolution (LSE) and bias field estimation
% proposed in the following paper:
% C. Li, R. Huang, Z. Ding, C. Gatenby, D. N. Metaxas, and J. C. Gore,
% "A Level Set Method for Image ... |
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