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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 | domingomery/Balu-master | Bio_manyplot.m | .m | Balu-master/InputOutput/Bio_manyplot.m | 1,215 | utf_8 | f88fda7a9bd7b5d2f13dd5d3ad52e3d2 | % Bio_manyplot(x,y,labels,xlab,showper)
%
% Toolbox: Balu
%
% Plot of many (x,y) graphs
%
% Input:
% - x could be a column vector with n elements or a matrix with
% nxm elements
% - y is a matrix with nxm elements
% Output:
% - a plot of m curves, each curve has n points with differe... |
github | domingomery/Balu-master | Bio_plotfeatures.m | .m | Balu-master/InputOutput/Bio_plotfeatures.m | 4,944 | utf_8 | ef2f13c1a1d21d79d578bfa400278972 | % Bio_plotfeatures(X,d,Xn)
%
% Toolbox: Balu
% Plot features X acording classification d. If the feature names are
% given in Xn then they will labeled in each axis.
%
% For only one feature, histograms are ploted.
% For two (or three) features, plots in 2D (or 3D) are given.
% For m>3 featur... |
github | domingomery/Balu-master | Bio_sendmail.m | .m | Balu-master/InputOutput/Bio_sendmail.m | 1,611 | utf_8 | 55f1122029d857abda5f30fe1ef3f6de | % Bio_sendmail(mymail,mypassword,mailto,subject)
% Bio_sendmail(mymail,mypassword,mailto,subject,message)
% Bio_sendmail(mymail,mypassword,mailto,subject,message,attachment)
%
% Toolbox: Balu
% Send e-mail
%
% Send an e-mail form mymail to mailto, with subject, message (optionsl)
% and attachment (optional... |
github | domingomery/Balu-master | Bio_findex.m | .m | Balu-master/InputOutput/Bio_findex.m | 436 | utf_8 | ce9b6a520e7a8e6ea15dd58439740828 | % Obtain the index number of the feature wich names contain a given string.
function [ix,fnix] = Bio_findex(fn,str,inc)
if ~exist('inc','var')
inc = 1;
end
[N,M] = size(fn);
n = length(str);
T=ones(N,1)*str;
ix = [];
for i=1:M-n+1
D = sum(abs(T-fn(:,i:i+n-1))')';
ii = find(D==0);
if ~isempty(ii)
... |
github | domingomery/Balu-master | Bio_decisionline.m | .m | Balu-master/InputOutput/Bio_decisionline.m | 1,513 | utf_8 | 05d064a41f2cf114260a6d383b1815c9 | % Bio_decisionline(X,d,op)
%
% Toolbox: Balu
%
% Diaplay a 2D feature space and decision line.
%
% X: Sample data
% d: classification of samples
% op: output of a trained classifier.
%
% Example:
% load datagauss % simulated data (2 classes, 2 features)
% Xn = ['\b... |
github | domingomery/Balu-master | Bio_fmtconv.m | .m | Balu-master/InputOutput/Bio_fmtconv.m | 592 | utf_8 | 56ba969fa5f60391596fb27318ff012b | % Bio_fmtconv(fmt1,fmt2)
%
% Toolbox: Balu
% Image format conversion from format fmt1 to fmt2.
%
% This program convert all fmt1 images of current directory to fmt2
% images. fmt1 and fmt2 are strings.
%
% Example:
% Bio_fmtconv('jpg','png') % converts all jpg images into png images
%
% (c) GRIMA-DCCUC,... |
github | domingomery/Balu-master | Bio_edgeview.m | .m | Balu-master/InputOutput/Bio_edgeview.m | 1,561 | utf_8 | 97894a03219d2b5a9ad0fb6ad1fc785a | % Bio_edgeview(B,E,c,g)
%
% Toolbox: Balu
% Display gray or color image I overimposed by color pixels determined
% by binary image E. Useful to display the edges of an image.
% Variable c is the color vector [r g b] indicating the color to be displayed
% (default: c = [1 0 0], i.e., red)
% Variable g i... |
github | domingomery/Balu-master | Bio_plotroc.m | .m | Balu-master/InputOutput/Bio_plotroc.m | 910 | utf_8 | 416a3f90f5d3101cdce92f66261d786e | % Bio_plotroc(FPR,TPR,col)
%
% Toolbox: Balu
% Plot ROC curve and fit to an exponetial curve
%
% Example:
%
% th = 3;x = 0:0.05:1; y = 1-exp(-3*x)+randn(1,21)*0.05;
% Bio_plotroc(x,y)
%
% D.Mery, PUC-DCC, Apr. 2013
% http://dmery.ing.puc.cl
%
function [AUC,TPRs,FPRs,TPR05] = Bio_plotroc(x,y,col_line,col_point)
if ~... |
github | domingomery/Balu-master | Bio_segshow.m | .m | Balu-master/InputOutput/Bio_segshow.m | 1,424 | utf_8 | f50243f7c3b4c583e1508bb678c5d855 | % Bio_segshow(I,p)
%
% Toolbox: Balu
% Display original image and segmented image
%
% Bimshow display 4 images:
% 1: Original image (matrix I)
% 2: Segmented (using command Bsegbalu)
% 3: High contrast (using command Bsegbalu)
% 4: Edges (using Bedgeview)
%
% p is the parameter used ... |
github | domingomery/Balu-master | Bio_drawellipse.m | .m | Balu-master/InputOutput/Bio_drawellipse.m | 1,213 | utf_8 | 78e8e0a8b6677be967be516b6691e0ca | % Bio_drawellipse(v,ecol)
%
% Toolbox: Balu
% Draws an ellipse with a(1)x^2 + a(2)xy + a(3)y^2 + a(4)x + a(5)y + a(6) = 0
% ecol is the color of the ellipse
%
% Extracted from http://homepages.inf.ed.ac.uk/rbf/CVonline/
% CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium
% ... |
github | domingomery/Balu-master | Bio_maillist.m | .m | Balu-master/InputOutput/Bio_maillist.m | 1,572 | utf_8 | 401aa7f4de4ad67c91080287caf7f9eb | % Bio_maillist(mymail,mypassword,mails,subject,heads,body,signature)
%
% Toolbox: Balu
% Send e-mail list
%
% Send an e-mail form mymail to mails, with subject, message, head(s) and
% signature. Ir requires the password of mymail.
%
% mails = {'peter@gmail.com','rosa@hotmail.com','tomas@yahoo.es'};
% h... |
github | domingomery/Balu-master | Bio_labelregion.m | .m | Balu-master/InputOutput/Bio_labelregion.m | 2,719 | utf_8 | cd7f29d456b55ec1242ce4df66773c05 | % [d,D] = Bio_labelregion(I,L,c)
%
% Toolbox: Balu
% User interface to label regions of an image.
%
% I is the original image (color or grayvalue).
% L is a labeled image that indicates the segmented regions of I.
% c is the maximal number of classes.
% d(i) will be the class number of region i.
... |
github | domingomery/Balu-master | Bio_loadimg.m | .m | Balu-master/InputOutput/Bio_loadimg.m | 4,338 | utf_8 | a0434706346aa5c8206980dd19f0403b | % I = Bloadimg(f,i)
%
% Toolbox: Balu
%
% Load image i of set image defined by structure f
%
% f.path : directory where are the files
% f.extension : extension (eg: 'jpg')
% f.prefix : prefix (eg: 'DSC_')
% f.digits : number of digits (eg:4)
% f.gray : 1 means rgb to g... |
github | domingomery/Balu-master | Bio_latextable.m | .m | Balu-master/InputOutput/Bio_latextable.m | 1,315 | utf_8 | 39dd12c1612493fcdb51ecf1126a51f8 | %function Bio_latextable(row_names,col_names,fmt,T)
%
% Toolbox: Balu
% Code for a latex table.
%
% row_names is a cell with the names of the rows.
% col_names is a cell with the names of the columns.
% fmt is a cell with the format of each column.
% T is the table.
%
% Example:
%
% col_names = {'col... |
github | domingomery/Balu-master | Bsq_trifocal.m | .m | Balu-master/SequenceProcessing/Bsq_trifocal.m | 1,511 | utf_8 | 88e029db5882015f7975ae21b960c995 | % F = Bsq_trifocal(P)
%
% Toolbox: Balu
%
% Trifocal tesonsors of a sequence.
%
% P includes the projection matrices of n views as follows:
% Projection Pk = P(k*3-2:k*3,:), for k=1,...,n
%
% T are the fundamental matrices stored as follows:
% T(p,q,r,:) are the trifocal tensors (as 27x1 vector) betwe... |
github | domingomery/Balu-master | Bsq_visualvoc.m | .m | Balu-master/SequenceProcessing/Bsq_visualvoc.m | 4,045 | utf_8 | 3cba9b1709bf6c98fe60dd585555a665 | % From Spyrou et al 2010:
% ... most of the cmmon visual words, i.e., those with smallest iDF values,
% are not descriminative and their abscence would facilitate the retrieval
% process. On the other hand, the rarest visual words are in most of the
% cases as result of noise and may distract the retrieval process. To
... |
github | domingomery/Balu-master | Bsq_vocabulary.m | .m | Balu-master/SequenceProcessing/Bsq_vocabulary.m | 3,004 | utf_8 | 4b7cf03feea650d98100b5670be8eb4f | % [v,Xcen,H] = Bsq_vocabulary(kp,V,options)
%
% Toolbox: Balu
%
% Visual vocabulary.
%
% kp.des is the descriptions of the keypoint.
% kp.img is a vector containing the number of the image of each keypoint.
% The minimum value of img is allways 1 and the maximum is the number of
% processed images, i.e.,... |
github | domingomery/Balu-master | Bsq_fundamentalSIFT.m | .m | Balu-master/SequenceProcessing/Bsq_fundamentalSIFT.m | 1,957 | utf_8 | de93354fcb011af6860d6243c67be119 | % function [Fpq,Bpq] = Bsq_fundamentalSIFT(kp,p,q)
%
% Toolbox: Balu
%
% Fundamental matrix between two views p and q of a sequence using SIFT
% descriptors kp.
%
% Example: case when keypoints must be extracted
%
% f.path = ''; % Balu directory as path or current directory
% f.extension ... |
github | domingomery/Balu-master | Bsq_movie.m | .m | Balu-master/SequenceProcessing/Bsq_movie.m | 1,868 | utf_8 | 40172c3d3a79e3c6b61ff0ff1899b605 | % M = Bsq_show(f,map,k)
%
% Toolbox: Balu
%
% Display a movie of an image sequence defined by structure f.
%
% map is the map of the image, if not given will be used "[]".
%
% f.path : directory where are the files
% f.extension : extension (eg: 'jpg')
% f.prefix : prefix (eg: 'DSC_')
% ... |
github | domingomery/Balu-master | Bsq_des.m | .m | Balu-master/SequenceProcessing/Bsq_des.m | 9,491 | utf_8 | aab8368709486b1a39dd2a711d00ad9c | % kp = Bsq_des(f,options)
%
% Toolbox: Balu
%
% Description of a sequence.
%
% f is the structure that defines the sequence
%
% f.path : directory where are the files
% f.extension : extension (eg: 'jpg')
% f.prefix : prefix (eg: 'DSC_')
% f.digits : number of digits (eg:4)
... |
github | domingomery/Balu-master | Bsq_sort.m | .m | Balu-master/SequenceProcessing/Bsq_sort.m | 4,689 | utf_8 | b7e6b7557c3924943e4e2fa32c851ce0 | % s = Bsq_sort(kp,f,options)
% [kp_new,files_new] = Bsq_sort(kp,files,options)
%
% Toolbox: Balu
%
% Sort an image sequence.
%
% f is the structure that defines the sequence
%
% f.path : directory where are the files
% f.extension : extension (eg: 'jpg')
% f.prefix : prefix (eg: 'DSC_... |
github | domingomery/Balu-master | Bsq_load.m | .m | Balu-master/SequenceProcessing/Bsq_load.m | 1,030 | utf_8 | d3512600879f06cd4d8637b6ccfbeb72 | % f_new = Bsq_load(f)
%
% Toolbox: Balu
%
% Load an image sequence.
%
% f is a file structure (see Bio_loadimg for details)
%
% f_new includes a fild called f_new.images with an array NxMxm for a
% sequence with NxM images
%
% Example:
% f.path = ''; % Balu directory as path or current... |
github | domingomery/Balu-master | Bsq_vgoogle.m | .m | Balu-master/SequenceProcessing/Bsq_vgoogle.m | 2,607 | utf_8 | 0362e101680bbe07d55060aa36e95924 | % [rk,j] = Bsq_vgoogle(f,i,ilu,v,show)
%
% Toolbox: Balu
%
% Search sequence images similar to image i.
%
% f is the structure that defines the sequence
%
% f.path : directory where are the files
% f.extension : extension (eg: 'jpg')
% f.prefix : prefix (eg: 'DSC_')
% f.digits ... |
github | domingomery/Balu-master | Bsq_fundamental.m | .m | Balu-master/SequenceProcessing/Bsq_fundamental.m | 1,824 | utf_8 | 539819854dbc3fda7b210c7507b9989d | % F = Bsq_fundamental(P)
%
% Toolbox: Balu
%
% Fundamental matrices of a sequence.
%
% P includes the projection matrices of n views as follows:
% Projection Pk = P(k*3-2:k*3,:), for k=1,...,n
%
% F are the fundamental matrices stored as follows:
% F(p,q,:) is the Fundamental matrix (as 9x1 vector) be... |
github | domingomery/Balu-master | Bsq_multifundamental.m | .m | Balu-master/SequenceProcessing/Bsq_multifundamental.m | 2,250 | utf_8 | 1d51291e3817f5d620f4f0fc99afcf88 | % function function [F,Bo] = Bsq_multifundamental(kp,options)
%
% Toolbox: Balu
%
% (No calibrated) Multiple fundamental matrices of a sequence
%
% Example: case when keypoints must be extracted
%
% f.path = ''; % Balu directory as path or current directory
% f.extension = '.png';
% f.pref... |
github | domingomery/Balu-master | Bsq_visualvoc_bak.m | .m | Balu-master/SequenceProcessing/Bsq_visualvoc_bak.m | 4,007 | utf_8 | ad6bb77f38057b5193c205fc35b269ab | % From Spyrou et al 2010:
% ... most of the cmmon visual words, i.e., those with smallest iDF values,
% are not descriminative and their abscence would facilitate the retrieval
% process. On the other hand, the rarest visual words are in most of the
% cases as result of noise and may distract the retrieval process. To
... |
github | domingomery/Balu-master | Bsq_show.m | .m | Balu-master/SequenceProcessing/Bsq_show.m | 2,105 | utf_8 | a56ba86964f8832cd23477ff7ef1ece0 | % Iseq = Bsq_show(f,n,map,k)
%
% Toolbox: Balu
%
% Display an image sequence defined by structure f.
%
% n is the number of images per row (default is the number of images of
% the sequence).
%
% map is the map of the image, if not given will be used "[]".
%
% f.path : directory where are the files... |
github | domingomery/Balu-master | Bsq_stoplist.m | .m | Balu-master/SequenceProcessing/Bsq_stoplist.m | 2,649 | utf_8 | 9e2d1e8f1699640d23b2f43116850843 | % From Spyrou et al 2010:
% ... most of the common visual words, i.e., those with smallest iDF values,
% are not discriminative and their absence would facilitate the retrieval
% process. On the other hand, the rarest visual words are in most of the
% cases as result of noise and may distract the retrieval process. To
... |
github | domingomery/Balu-master | Bfx_moments.m | .m | Balu-master/FeatureExtraction/Bfx_moments.m | 1,948 | utf_8 | 653e002ee885a61555adb5dd36829228 | % [X,Xn] = Bfx_moments(R,options)
%
% Toolbox: Balu
%
% Extract moments and central moments.
%
% options.show = 1 display mesagges.
% options.central = 1 for central moments, and 0 for normal moments
% options.rs = n x 2 matrix that contains the indices of the
% moments... |
github | domingomery/Balu-master | Bfx_basicint.m | .m | Balu-master/FeatureExtraction/Bfx_basicint.m | 1,843 | utf_8 | 04c161a3907d7c57c57632eb0fc9accd | % [X,Xn] = Bfx_basicint(I,R,options)
% [X,Xn] = Bfx_basicint(I,options)
%
% Toolbox: Balu
% Basic intensity features
%
% X is the features vector, Xn is the list feature names (see Example to
% see how it works).
%
% Reference:
% Kumar, A.; Pang, G.K.H. (2002): Defect detection in textured mate... |
github | domingomery/Balu-master | Bfx_onesift.m | .m | Balu-master/FeatureExtraction/Bfx_onesift.m | 1,955 | utf_8 | c8fbe5fdc65eb5c2e40fbd368002f579 | % [X,Xn,options] = Bfx_onesift(I,R,options)
% [X,Xn,options] = Bfx_onesift(I,options)
% [X,Xn] = Bfx_onesift(I,R,options)
% [X,Xn] = Bfx_onesift(I,options)
%
% Toolbox: Balu
% Extract only one SIFT descriptor of region R of image I.
%
% X is the features vector, Xn is the list of feature names (see Example
% t... |
github | domingomery/Balu-master | Bfx_hugeo.m | .m | Balu-master/FeatureExtraction/Bfx_hugeo.m | 2,304 | utf_8 | 5e1a0fce78c514e4d097bc0f6941ba0e | % [X,Xn] = Bfx_hugeo(R)
% [X,Xn] = Bfx_hugeo(R,options)
%
% Toolbox: Balu
%
% Extract the seven Hu moments from binary image R.
%
% options.show = 1 display mesagges.
%
% X is a 7 elements vector:
% X(i): Hu-moment i for i=1,...,7.
% Xn is the list of feature names.
%
% Reference:
%... |
github | domingomery/Balu-master | Bfx_hog.m | .m | Balu-master/FeatureExtraction/Bfx_hog.m | 3,258 | utf_8 | a089b1fa3b7f1cb82642615fc6cf1c1f | % [X,Xn] = Bfx_hog(I,options)
%
% Toolbox: Balu
% Histogram of Orientated Gradients features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% options.nj; : number of HOG windows per bound box
% options.ni : in i (vertical) and j (horizaont... |
github | domingomery/Balu-master | Bfx_huint.m | .m | Balu-master/FeatureExtraction/Bfx_huint.m | 2,385 | utf_8 | 2d95567f2dde7180bc05e62ba3fdb92d | % [X,Xn,Xu] = Bfx_huint(I,R,options)
%
% Toolbox: Balu
% Hu moments with intensity.
%
% X is a 7 elements vector:
% X(i): Hu-moment i for i=1,...,7.
% Xn is the list of feature names (see Example to see how it works).
%
% Reference:
% Hu, M-K.: "Visual Pattern Recognition by Moment Invari... |
github | domingomery/Balu-master | Bfx_lbpint.m | .m | Balu-master/FeatureExtraction/Bfx_lbpint.m | 18,156 | utf_8 | 58b30c959a0a79e86ffcf6369760e789 | % [X,Xn,options] = Bfx_lbp(I,R,options)
% [X,Xn,options] = Bfx_lbp(I,options)
% [X,Xn] = Bfx_lbp(I,R,options)
% [X,Xn] = Bfx_lbp(I,options)
%
% Toolbox: Balu
% Local Binary Patterns features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% It calculates ... |
github | domingomery/Balu-master | Bfx_basicgeo.m | .m | Balu-master/FeatureExtraction/Bfx_basicgeo.m | 2,763 | utf_8 | 864db878ee7275da588d80683c9c45a7 | % [X,Xn] = Bfx_geobasic(R,options)
%
% Toolbox: Balu
%
% Standard geometric features of a binary image R. This function calls
% regionprops of Image Processing Toolbox.
%
% options.show = 1 display mesagges.
%
% X is the feature vector
% Xn is the list of feature names.
%
% See expamle:
%
% Example:... |
github | domingomery/Balu-master | Bfx_build.m | .m | Balu-master/FeatureExtraction/Bfx_build.m | 9,784 | utf_8 | b85a3456e0fcefa8ff8f3e6e8fa60df0 | % bf = Bfx_build({'all'})
% bf = Bfx_build({'allgeo'})
% bf = Bfx_build({'allint'})
% bf = Bfx_build({'haralick','lbp'})
%
% Toolbox: Balu
% Build structure for feature extraction with default values
%
% Posible geometric names:
% 'basicgeo',
% 'fitellipse',
% 'fourierdes',
% 'hugeo',
% 'flusser',
% 'gupta',
%
% Pos... |
github | domingomery/Balu-master | Bfx_gupta.m | .m | Balu-master/FeatureExtraction/Bfx_gupta.m | 1,702 | utf_8 | 9f533fbbc4a7c7b467d14e83acc0aa8c | % [X,Xn] = Bfx_gupta(R,options)
%
% Toolbox: Balu
%
% Extract the three Gupta moments from binary image R.
%
% options.show = 1 display mesagges.
%
% X is a 3 elements vector:
% X(i): Gupta-moment i for i=1,2,3.
% Xn is the list of feature names.
%
% Reference:
% Gupta, L. & Srina... |
github | domingomery/Balu-master | Bfx_clp.m | .m | Balu-master/FeatureExtraction/Bfx_clp.m | 4,246 | utf_8 | 6693b6f561692d32557a46eb19e87152 | % [X,Xn] = Bfx_clp(I,R,options)
% [X,Xn] = Bfx_clp(I,options)
%
% Toolbox Balu: Crossing Line Profile.
%
% X is the features vector, Xn is the list of feature names(see Example
% to see how it works).
%
% Reference:
% Mery, D.: Crossing line profile: a new approach to detecting defects
% in alu... |
github | domingomery/Balu-master | Bfx_gabor.m | .m | Balu-master/FeatureExtraction/Bfx_gabor.m | 3,540 | utf_8 | 36d1e30d3fea9e840ac1b5566302615e | % [X,Xn] = Bfx_gabor(I,R,options)
% [X,Xn] = Bfx_gabor(I,options)
%
% Toolbox: Balu
% Gabor features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% Reference:
% Kumar, A.; Pang, G.K.H. (2002): Defect detection in textured materials
% ... |
github | domingomery/Balu-master | Bfx_lbphogi.m | .m | Balu-master/FeatureExtraction/Bfx_lbphogi.m | 1,285 | utf_8 | 697f23969b6f2ca4fe58208a9d934672 | % [X,Xn] = Bfx_hog(I,options)
%
% Toolbox: Balu
% Histogram of Orientated Gradients features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% options.nj; : number of HOG windows per bound box
% options.ni : in i (vertical) and j (horizaont... |
github | domingomery/Balu-master | Bfx_gui.m | .m | Balu-master/FeatureExtraction/Bfx_gui.m | 47,257 | utf_8 | 55d8165e26f39e53841b072ed7bd1721 | % Bfx_gui
%
% Toolbox: Balu
%
% Graphic User Interface for feature extraction.
%
% (c) GRIMA-DCCUC, 2011
% http://grima.ing.puc.cl
function varargout = Bfx_gui(varargin)
% BFX_GUI M-file for Bfx_gui.fig
% BFX_GUI, by itself, creates a new BFX_GUI or raises the existing
% singleton*.
%
% H = BFX_GUI r... |
github | domingomery/Balu-master | Bfx_fourier.m | .m | Balu-master/FeatureExtraction/Bfx_fourier.m | 1,916 | utf_8 | a747d156cb43fd117284995b29b025b5 | % [X,Xn,Xu] = Xfourier(I,R,options)
% [X,Xn,Xu] = Xfourier(I,options)
%
% Toolbox Xvis: Fourier features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% Example:
% options.Nfourier = 64; % imresize vertical
% options.Mfo... |
github | domingomery/Balu-master | Bfx_hogi.m | .m | Balu-master/FeatureExtraction/Bfx_hogi.m | 3,334 | utf_8 | e85623c068f0196bd52a35b3453bb979 | % [X,Xn] = Bfx_hog(I,options)
%
% Toolbox: Balu
% Histogram of Orientated Gradients features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% options.nj; : number of HOG windows per bound box
% options.ni : in i (vertical) and j (horizaont... |
github | domingomery/Balu-master | Bfx_fourierdes.m | .m | Balu-master/FeatureExtraction/Bfx_fourierdes.m | 1,997 | utf_8 | 3026250f26d693f4384307cb01b351cf | % function [X,Xn] = Bfx_fourierdes(R,options)
%
% Toolbox: Balu
% Computes the Fourier descriptors of a binary image R.
%
% options.show = 1 display mesagges.
% options.Nfourierdes number of descriptors.
%
% X is the feature vector
% Xn is the list of feature names.
%
% Reference:
% Zah... |
github | domingomery/Balu-master | Bfx_contrast.m | .m | Balu-master/FeatureExtraction/Bfx_contrast.m | 4,036 | utf_8 | 8019b18ebe9a330ead03c4f53d925a0c | % [X,Xn] = Bfi_contrast(I,R,options)
% [X,Xn] = Bfi_contrast(I,options)
%
% Toolbox: Balu
% Contrast features.
%
% X is the features vector, Xn is the list of feature names(see Example
% to see how it works).
%
% References:
% Mery, D.; Filbert: Classification of Potential Defects in
% Aut... |
github | domingomery/Balu-master | Bfx_bsif.m | .m | Balu-master/FeatureExtraction/Bfx_bsif.m | 4,662 | utf_8 | ff6279e64bd0b6798ec5b8c1e69d967f | % [X,Xn,options] = Bfx_bsif(I,R,options)
% [X,Xn,options] = Bfx_bsif(I,options)
% [X,Xn] = Bfx_bsif(I,R,options)
% [X,Xn] = Bfx_bsif(I,options)
%
% Toolbox: Balu
% Binarized statistical image features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% It c... |
github | domingomery/Balu-master | Bfx_randomsliwin.m | .m | Balu-master/FeatureExtraction/Bfx_randomsliwin.m | 5,864 | utf_8 | e8ce09e5cd7d838b6159ae1aa8b838ff | % [X,d,Xn,x] = Bfx_randomsliwin(I,J,options)
%
% Toolbox: Balu
%
% Feature extraction of random sliding windows.
% This program select automatically detection windows sized mxm
% with label '1' and lable '0'. For each window
% Balu intensity features are extracted.
%
% Input:
% I original ... |
github | domingomery/Balu-master | Bfx_lbphog.m | .m | Balu-master/FeatureExtraction/Bfx_lbphog.m | 1,498 | utf_8 | 35bc94d93b874692e58084e3c10a81e7 | % [X,Xn] = Bfx_hog(I,options)
%
% Toolbox: Balu
% Histogram of Orientated Gradients features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% options.nj; : number of HOG windows per bound box
% options.ni : in i (vertical) and j (horizaont... |
github | domingomery/Balu-master | Bfx_vlhog.m | .m | Balu-master/FeatureExtraction/Bfx_vlhog.m | 1,557 | utf_8 | d708218a0d2591812a418a1ac0eadbdc | % [X,Xn] = Bfx_vlhog(I,options)
%
% Toolbox: Balu
% Histogram of Orientated Gradients features using Vlfeat Toolbox.
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% options.cellsize : size of the cells in pixels
% options.variant : 1 for ... |
github | domingomery/Balu-master | Bfx_fitellipse.m | .m | Balu-master/FeatureExtraction/Bfx_fitellipse.m | 3,128 | utf_8 | a1b463a5bd9a20f8037bd3f60f769d2c | % [X,Xn] = Bfx_fitellipse(R,options)
% [X,Xn] = Bfx_fitellipse(R)
%
% Toolbox: Balu
%
% Fit ellipse for the boundary of a binary image R.
%
% options.show = 1 display mesagges.
%
% X is a 6 elements vector:
% X(1): Ellipse-centre i direction
% X(2): Ellipse-centre j direction
% X(3):... |
github | domingomery/Balu-master | Bfx_files.m | .m | Balu-master/FeatureExtraction/Bfx_files.m | 8,658 | utf_8 | b57e06bded6b2d40a2bc7376dab47b76 | % [X,Xn,S] = Bfx_files(f,opf) % gemetric and intensity features
% [X,Xn,S] = Bfx_files(f,opf,labelling) % features + labelling
%
% Toolbox: Balu
%
% Feature extraction from a set of files.
%
% This function calls feature extraction procedures of all
% images defined in f. See example to see h... |
github | domingomery/Balu-master | Bfx_haralick.m | .m | Balu-master/FeatureExtraction/Bfx_haralick.m | 6,209 | utf_8 | 08e5cceaeada8d46d9b1ced71e2004ce | % [X,Xn] = Bfx_haralick(I,R,options)
% [X,Xn] = Bfx_haralick(I,options)
%
% Toolbox: Balu
% Haralick texture features.
%
% X is a 28 elements vector with mean and range of mean and range of
%
% 1 Angular Second Moment
% 2 Contrast
% 3 Correlacion
% 4 Sum of squares
% 5 Inverse Diff... |
github | domingomery/Balu-master | Bfx_dct.m | .m | Balu-master/FeatureExtraction/Bfx_dct.m | 1,807 | utf_8 | f7545cce1977db3f89d1e673978c198f | % [X,Xn,] = Bfx_dct(I,R,options)
% [X,Xn] = Bfx_dct(I,options)
%
% Toolbox: Balu
% DCT features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% Reference:
% Kumar, A.; Pang, G.K.H. (2002): Defect detection in textured materials
% using... |
github | domingomery/Balu-master | Bfx_gaborfull.m | .m | Balu-master/FeatureExtraction/Bfx_gaborfull.m | 7,865 | utf_8 | 175cda6c002b920179c6a48331bb5f13 | % [X,Xn] = Bfx_gaborfull(I,R,options)
% [X,Xn] = Bfx_gaborfull(I,options)
%
% Toolbox: Balu
% Gabor Full features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% Reference:
% M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, "CloudID: Trustworthy
% clou... |
github | domingomery/Balu-master | Bfx_lbp.m | .m | Balu-master/FeatureExtraction/Bfx_lbp.m | 18,701 | utf_8 | 0b6d8f300debf9639dc1a587453b85dc | % [X,Xn,options] = Bfx_lbp(I,R,options)
% [X,Xn,options] = Bfx_lbp(I,options)
% [X,Xn] = Bfx_lbp(I,R,options)
% [X,Xn] = Bfx_lbp(I,options)
%
% Toolbox: Balu
% Local Binary Patterns features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% It calculates ... |
github | domingomery/Balu-master | Bfx_lbpcontrast.m | .m | Balu-master/FeatureExtraction/Bfx_lbpcontrast.m | 1,609 | utf_8 | 44e9e37cce48e0b0ab58dbb5c1aa27f0 | % Pietikainen, M. et al (2011): Computer Vision Using Local Binary
% Patterns, Springer.
function J = Bfx_lbpcontrast(I,options)
if ~exist('options','var')
m = 3;
else
m = options.m;
end
n = (m-1)/2;
[N,M,P] = size(I);
if P==1 % 2D
m0 = (m^2+1)/2;
ii = [1:m0-1 m0+1:m^2];
J = zeros(N,M);
fo... |
github | domingomery/Balu-master | Bfx_flusser.m | .m | Balu-master/FeatureExtraction/Bfx_flusser.m | 2,208 | utf_8 | 05663d5a0b328c25995959249565bdf2 | % [X,Xn] = Bfx_flusser(R,options)
% [X,Xn] = Bfx_flusser(R)
%
% Toolbox: Balu
%
% Extract the four Flusser moments from binary image R.
%
% options.show = 1 display mesagges.
%
% X is a 4 elements vector:
% X(i): Flusser-moment i for i=1,...,4.
% Xn is the list of feature names.
%
% Ref... |
github | domingomery/Balu-master | Bfx_phog.m | .m | Balu-master/FeatureExtraction/Bfx_phog.m | 4,705 | utf_8 | 76718a146565c533700f9b922c118f81 | % [X,Xn,Xu] = Bfx_phog(I,R,options)
% [X,Xn,Xu] = Bfx_phog(I,options)
%
% Toolbox: Balu
%
% Pyramid Histogram of Oriented Gradients based on implementation by
% Anna Bosch from
%
% http://www.robots.ox.ac.uk/~vgg/research/caltech/phog.html
%
% IN:
% I - Images of size MxN (Color or Gray)
% options.bin - Nu... |
github | domingomery/Balu-master | Bfx_clp_old.m | .m | Balu-master/FeatureExtraction/Bfx_clp_old.m | 4,664 | utf_8 | c8bae5401c7b894bb426467bef73f207 | % [X,Xn] = Bfx_clp(I,R,options)
% [X,Xn] = Bfx_clp(I,options)
%
% Toolbox: Balu
% Crossing Line Profile.
%
% X is the features vector, Xn is the list of feature names(see Example
% to see how it works).
%
% Reference:
% Mery, D.: Crossing line profile: a new approach to detecting defects
% ... |
github | domingomery/Balu-master | Bfx_lbpi.m | .m | Balu-master/FeatureExtraction/Bfx_lbpi.m | 17,985 | utf_8 | 41dcbf073502e4149f5abf197cc3092e | % [X,Xn,options] = Bfx_lbp(I,R,options)
% [X,Xn,options] = Bfx_lbp(I,options)
% [X,Xn] = Bfx_lbp(I,R,options)
% [X,Xn] = Bfx_lbp(I,options)
%
% Toolbox: Balu
% Local Binary Patterns features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% It calculates ... |
github | domingomery/Balu-master | Bfx_lbp_old.m | .m | Balu-master/FeatureExtraction/Bfx_lbp_old.m | 18,020 | utf_8 | 5d0a1fbb2228d5669c26283336b539fe | % [X,Xn,options] = Bfx_lbp(I,R,options)
% [X,Xn,options] = Bfx_lbp(I,options)
% [X,Xn] = Bfx_lbp(I,R,options)
% [X,Xn] = Bfx_lbp(I,options)
%
% Toolbox: Balu
% Local Binary Patterns features
%
% X is the features vector, Xn is the list of feature names (see Example
% to see how it works).
%
% It calculates ... |
github | domingomery/Balu-master | Bfx_geo.m | .m | Balu-master/FeatureExtraction/Bfx_geo.m | 2,922 | utf_8 | 446405d4d737c6637957ae9496e02e5a | % [X,Xn] = Bfx_geo(R,options)
% [X,Xn] = Bfx_geo(L,options)
%
% Toolbox: Balu
%
% Gemteric feature extraction.
%
% This function calls gemetric feature extraction procedures of binary
% image R or labelled image L.
%
% X is the feature matrix (one feature per column, one sample per row),
% Xn is the list... |
github | domingomery/Balu-master | Bfx_int.m | .m | Balu-master/FeatureExtraction/Bfx_int.m | 5,881 | utf_8 | 580daf50396b7da9cbd6894e5fc8da80 | % [X,Xn] = Bfx_int(I,R,b)
%
% Toolbox: Balu
%
% Intensity feature extraction.
%
% This function calls intensity feature extraction procedures of image I
% according binary image R. See example to see how it works.
%
% X is the feature matrix (one feature per column, one sample per row),
% Xn is the list ... |
github | domingomery/Balu-master | Bfx_all.m | .m | Balu-master/FeatureExtraction/Bfx_all.m | 499 | utf_8 | dd2f13d940eb76ecc4bb1cbf3cea7857 | % [X,Xn] = Bfx_all(I,R,options)
% [X,Xn] = Bfx_all(I,options)
%
% Toolbox: Balu
% All pixels.
%
% X is the features vector, Xn is the list feature names (see Example to
% see how it works).
%
%
% Example:
% I = imread('testimg1.jpg'); % input image
% [X,Xn] = Bfx_all(I); ... |
github | domingomery/Balu-master | num2fixstr.m | .m | Balu-master/Miscellaneous/num2fixstr.m | 347 | utf_8 | 842ba059f8799b2c4a46e890de6cee74 | %function st = num2fixstr(i,d)
%
% This function converts an integer i in a string
% st with a fixed number of characters (with '0'
% filled from left.
%
% Example:
% st = num2fixstr(3,4) % returns '0003'
%
% D.Mery, Aug-2012
% http://dmery.ing.puc.cl
%
function st = num2fixstr(i,d)
s = [char('0'*ones(1,d)) num2st... |
github | domingomery/Balu-master | enterpause.m | .m | Balu-master/Miscellaneous/enterpause.m | 311 | utf_8 | a24fcf539906b8dc95c0e17d240bd116 | % Toolbox: Balu
% enterpause display "press <Enter> to continue..." and wait for <Enter>
% enterpause(t) means pause(t)
%
% D.Mery, PUC-DCC, Apr. 2008
% http://dmery.ing.puc.cl
function enterpause(t)
if ~exist('t','var')
disp('press <Enter> to continue...')
pause
else
pause(t)
end |
github | domingomery/Balu-master | posrandom.m | .m | Balu-master/Miscellaneous/posrandom.m | 279 | utf_8 | e209ba3e41ac82dd560a8ff602aad8af | % It changes the order of the columns or rows of x randomly. Variable dim
% selects the dimension along which to change.
function [x_new,j] = posrandom(x,dim)
nx = size(x,dim);
r = rand(nx,1);
[i,j] = sort(r);
if dim==1
x_new = x(j,:);
else
x_new = x(:,j);
end
|
github | domingomery/Balu-master | cart2polpos.m | .m | Balu-master/Miscellaneous/cart2polpos.m | 870 | utf_8 | b291b2687cca1f26635f22687f1ebc7b | % [t,r] = cart2polpos(x,y,s)
%
% Toolbox: Balu
% transform Cartesian to polar coordinates. The angle t will be always
% positive. If s==0, the t is between 0 and 2*pi. If s > 0, the angles are
% subdivided into s bins, and t means in which bin the angle is located.
%
% Examples:
% [t,r] = cart2polpos(1,1) %... |
github | domingomery/Balu-master | distxy.m | .m | Balu-master/Miscellaneous/distxy.m | 2,111 | utf_8 | 549e0d02ce5a7c63b7150ee7d5e67ab1 | % function D = distxy(x,y,method)
%
% Toolbox: Balu
%
% It computes distances of each row of x with each row of y
% x is a Nx x n matrix, y is a Ny x n matrix
% D is a Nx x Ny matrix, where D(i,j) = norm(x(i,:)-y(j,:));
% method = 1: it computes row per row
% 2: it computes each row of x with all rows of y
% ... |
github | domingomery/Balu-master | andsift.m | .m | Balu-master/Miscellaneous/andsift.m | 714 | utf_8 | 783f127c786620dfefdb1c28025e7cb7 | % It separates sift descriptors into descriptors that belong to the region
% of interest R. R is a binary image. fi,di are the frames and descriptors
% of pixels = i (for i=0,1). (ff,dd) is the transposed output of vl_sift.
function [f1,d1,f0,d0,i1,i0] = andsift(ff,dd,R)
f = ff';
d = dd';
[N,M] = size(R);
ii = ro... |
github | domingomery/Balu-master | howis.m | .m | Balu-master/Miscellaneous/howis.m | 489 | utf_8 | 08b5567797d8a802206bf707280552b5 | % howis(x)
%
% Toolbox: Balu
% How is x? it displays min, max, size, class of x.
% If x is a structure, it display the field names.
%
% D.Mery, PUC-DCC, Jul 2009-2012
% http://dmery.ing.puc.cl
%
function howis(x)
if isstruct(x)
disp('Structure:')
x
else
fprintf('%s\n',['min = ' num2s... |
github | domingomery/Balu-master | Bfx_centroid.m | .m | Balu-master/Examples/Bfx_centroid.m | 1,071 | utf_8 | 535fc870f63dc5c0db9c6cd5bd5a99fc | % [X,Xn] = Bfx_centroid(R,options)
%
% Toolbox: Balu
%
% Centroid of a region.
%
% options.show = 1 display mesagges.
%
% X(1) is centroid-i, X(2) is centroid-j
% Xn is the list of the n feature names.
%
% Example (Centroid of a region)
% I = imread('testimg1.jpg'); % input im... |
github | domingomery/Balu-master | Bex_sfssvm.m | .m | Balu-master/Examples/Bex_sfssvm.m | 1,028 | utf_8 | e44139735f36fbd1b9b4572ee25ad370 | % s = Bex_sfssvm(f,d,m)
%
% Toolbox: Balu
% Example: Feature selection using Balu algorithm based on SFS and SVM.
%
% Bfs_balu has three steps:
% (1) normalizes (using Bft_norm),
% (2) cleans (using Bfs_clean), and
% (3) selects features (using Bfs_sfs).
%
% Example:
% load datareal
% s = ... |
github | domingomery/Balu-master | Bex_decisionline.m | .m | Balu-master/Examples/Bex_decisionline.m | 707 | utf_8 | 49c2a2387a4afed7977a6df45a94a357 | % Bex_decisionline(X,d,bcl)
%
% Toolbox: Balu
% Example: Decision lines of features X with labels d for classifiers
% defined in bcl
%
% Example:
% load datagauss % simulated data (2 classes, 2 features)
% Xn = ['x_1';'x_2'];
% bcl(1).name = 'knn'; bcl(1).options.k =... |
github | domingomery/Balu-master | Bex_exsknn.m | .m | Balu-master/Examples/Bex_exsknn.m | 1,344 | utf_8 | 1cc04f149ac136e42e9984448caeee04 | % s = Bex_exsknn(f,d,m)
%
% Toolbox: Balu
% Example: Feature selection using exhaustive search and KNN.
%
% Bfs_balu has three steps:
% (1) normalizes (using Bft_norm),
% (2) cleans (using Bfs_clean), and
% (3) selects features (using Bfs_sfs).
%
% Example:
% load datareal
% s = Bex_exsknn(f... |
github | domingomery/Balu-master | Bex_fscombination.m | .m | Balu-master/Examples/Bex_fscombination.m | 3,104 | utf_8 | 4470da31ed3e0aa327ccb8d95e0cbbff | % Bex_fscombination
%
% Toolbox: Balu
% Combination of feature selection algorithms.
%
% This example shows how to combine different feature selection algorthm
% in orde to obtain the highest performance.
%
% The evaluation of the performance is using a simple LDA classifier
%
% Example:
% load datare... |
github | domingomery/Balu-master | Bex_lsef.m | .m | Balu-master/Examples/Bex_lsef.m | 1,379 | utf_8 | 80041176a9d5dd6cf64549a184d4ad9b | % s = Bex_lsef(f,d,m)
%
% Toolbox: Balu
% Example: Feature selection using lsef algorithm
%
% Example:
% load datareal
% s = Bex_lsef(f,d,6)
% fn(s,:)
%
% See also Bfs_lsef
%
% (c) GRIMA-DCCUC, 2011
% http://grima.ing.puc.cl
function s = Bex_lsef(f,d,m)
op.m = 2*m; % 2*m features will ... |
github | domingomery/Balu-master | Bfs_clean.m | .m | Balu-master/FeatureSelection/Bfs_clean.m | 1,773 | utf_8 | 13cedc8d94836230ab53973127f23964 | % selec = Bfs_clean(X,show)
%
% Toolbox: Balu
% Feature selection cleaning.
%
% It eliminates constant features and correlated features.
%
% Input: X is the feature matrix.
% show = 1 displays results (default show=0)
% Output: selec is the indices of the selected features
%
% Exampl... |
github | domingomery/Balu-master | Bfs_sfscorr.m | .m | Balu-master/FeatureSelection/Bfs_sfscorr.m | 1,392 | utf_8 | 42d6288b4c73a7cbcc34d92b3fa48c76 | % [R,selec] = Bfa_sfscorr(f,d,m)
%
% Toolbox: Balu
% Sequential Forward Selection for features f according to measurment d.
% The algorithm searchs the linear combination of the features that
% best correlates with d.
% m features will be selected.
% R is the obtained correlation coefficient and s... |
github | domingomery/Balu-master | Bfs_noposition.m | .m | Balu-master/FeatureSelection/Bfs_noposition.m | 1,380 | utf_8 | 6d584f9b6a2290b942e2363403888915 | % [f_new,fn_new] = Bfs_noposition(f,fn)
%
% Toolbox: Balu
% This procedure deletes the features related to the position.
% It deletes the following features:
% - center of grav i
% - center of grav j
% - Ellipse-centre i
% - Ellipse-centre j
%
% Example:
% I = imread(... |
github | domingomery/Balu-master | Bfs_fosmod.m | .m | Balu-master/FeatureSelection/Bfs_fosmod.m | 3,173 | utf_8 | 459b2c4292d50ca2234e5e3f78eda0fc | % selec = Bfs_fosmod(X,options)
%
% Toolbox: Balu
% Feature Selection using FOS-MOD algorithm
%
% input: X feature matrix
% options.m number of features to be selected
% options.show = 1 displays results
%
% output: selec selected features
%
% FOS_MOD is a forward orthogonal s... |
github | domingomery/Balu-master | Bfs_random.m | .m | Balu-master/FeatureSelection/Bfs_random.m | 2,187 | utf_8 | 99545454631b8da08b171978d75886f7 | % selec = Bfs_random(X,d,options)
%
% Toolbox: Balu
% Select best features from random subsets of X according to ideal
% classification d.
% options.m is the number features will be selected.
% options.M is the number of random subsets to be tested.
% options.b.method = 'fisher' uses Fisher objet... |
github | domingomery/Balu-master | Bfs_norotation.m | .m | Balu-master/FeatureSelection/Bfs_norotation.m | 1,537 | utf_8 | b1c6fa0ea43372e402874e2f3e6db857 | % [f_new,fn_new] = Bfs_norotation(f,fn)
%
% Toolbox: Balu
% This procedure deletes all no rotation invariant features.
% It deletes the features that have in their name the strings:
% - orient
% - Gabor(
% - [8,u2] for LBP
% - sLBP
%
% E... |
github | domingomery/Balu-master | Bfs_all.m | .m | Balu-master/FeatureSelection/Bfs_all.m | 131 | utf_8 | 844e98d7bb621a3acffe6f60df432c78 | % Dummy file called by Bfx_gui
% It selects all features of X.
function selec = Bfs_all(X,d,options)
m = size(X,2);
selec = (1:m)'; |
github | domingomery/Balu-master | Bfs_balu.m | .m | Balu-master/FeatureSelection/Bfs_balu.m | 2,884 | utf_8 | 553453caae55239bed210fd8551d179a | % selec = Bfs_balu(X,d,options)
%
% Toolbox: Balu
% Feature selection of "best" options.m features of X to ideal
% classification d. This function uses only a portion of options.s
% samples to select the features.
% Bfs_balu (1) normalizes (using Bft_norm), (2) cleans (using Bfs_clean)
% and (3) ... |
github | domingomery/Balu-master | Bfs_mRMR.m | .m | Balu-master/FeatureSelection/Bfs_mRMR.m | 4,499 | utf_8 | e8736538d2bdd540fea3d416d8e0dee6 | % selec = Bfs_mRMR(X,d,options)
%
% Toolbox: Balu
% Feature selection using Criteria of Max-Dependency, Max-Relevance, and
% Min-Redundancy after Peng et al. (2005)
%
% X extracted features (NxM): N samples, M features
% d ideal classification (Nx!) for the N samples
% options.m number of selected featur... |
github | domingomery/Balu-master | Bfs_bb.m | .m | Balu-master/FeatureSelection/Bfs_bb.m | 4,988 | utf_8 | 149d76a182777b35b877fa7080dc39a7 | % selec = Bfs_bb(X,d,options)
%
% Toolbox: Balu
% Feature selection using Branch & Bound for fatures X according to
% ideal classification d. optins.m features will be selected.
% options.b.method = 'fisher' uses Fisher objetctive function.
% options.b.method = 'sp100' uses as criteria Sp @Sn=100%.
%... |
github | domingomery/Balu-master | Bfs_nobackground.m | .m | Balu-master/FeatureSelection/Bfs_nobackground.m | 1,499 | utf_8 | 98cdc4bfb82c9a67adee6e5799d28faa | % [f_new,fn_new] = Bfs_nobackground(f,fn)
%
% Toolbox: Balu
% This procedure deletes the features related to the position.
% It deletes the following features related to the contrast:
% - contrast-K1
% - contrast-K2
% - contrast-K3
% - contrast-Ks ... |
github | domingomery/Balu-master | Bfs_exsearch.m | .m | Balu-master/FeatureSelection/Bfs_exsearch.m | 2,602 | utf_8 | 74412cf1479a6c06be68936e449dcd5a | % selec = Bfs_exsearch(X,d,options)
%
% Toolbox: Balu
% Feature selection using exhaustive search for fatures X according to
% ideal classification d. optins.m features will be selected.
% options.b.method = 'fisher' uses Fisher objetctive function.
% options.b.method = 'sp100' uses as criteria Sp @S... |
github | domingomery/Balu-master | Bfs_ransac.m | .m | Balu-master/FeatureSelection/Bfs_ransac.m | 1,759 | utf_8 | f416554f1291419b4b55cfb98cc3c01b | % selec = Bfsransac(X,d,m,show,method,param,param2,param3)
%
% Toolbox: Balu
% Sequential Forward Selection for fatures X according to ideal
% classification d. m features will be selected.
% method = 'fisher' uses Fisher objetctive function (in this case param
% is the a priori probability of each cl... |
github | domingomery/Balu-master | Bfs_sfs.m | .m | Balu-master/FeatureSelection/Bfs_sfs.m | 3,719 | utf_8 | f9620a7255abdf0e8d1fe5a3fe189ce1 | % selec = Bfs_sfs(X,d,options)
%
% Toolbox: Balu
% Sequential Forward Selection for fatures X according to ideal
% classification d. optins.m features will be selected.
% options.b.method = 'fisher' uses Fisher objetctive function.
% options.b.method = 'sp100' uses as criteria Sp @Sn=100%.
% optio... |
github | domingomery/Balu-master | Bfs_rank.m | .m | Balu-master/FeatureSelection/Bfs_rank.m | 2,709 | utf_8 | 04c850f50a535397c9a1dc6eaaf5ce03 | % selec = Bfs_rank(X,d,options)%
%
% Toolbox: Balu
% Feature selection based on command rankfeatures (from MATLAB
% Bioinformatics Toolbox) that ranks ranks key features by class
% separability criteria.
%
% input: X feature matrix
% options.m number of features to be selected
% ... |
github | domingomery/Balu-master | Bfs_lsef.m | .m | Balu-master/FeatureSelection/Bfs_lsef.m | 4,224 | utf_8 | 43b4356a3d4d8fcb5317ebef270f1153 | % [selec,Y,th] = Bfs_lsef(X,options)
%
% Toolbox: Balu
% Feature Selection using LSE-forward algorithm
%
% input: X feature matrix
% options.m number of features to be selected
% optoins.show = 1 displays results
%
% output: selec selected features
% Y is equal to A*th,... |
github | domingomery/Balu-master | Bmv_epidist.m | .m | Balu-master/MultiView/Bmv_epidist.m | 1,671 | utf_8 | 23fe88b6fd0a2d7bb2ba85a4e316b8ed | % d = Bmv_epidist(m1,m2,F,method)
%
% Toolbox: Balu
%
% Distance from m2 to epipolar line l2 = F*m1
%
% d = distance2(m1,m2,F,'method') returns the distance
% error. The posible corresponding points are m2 and m1.
% F is the fundamental matrix. The distance is calculated
% using the following met... |
github | domingomery/Balu-master | Bmv_reco3dna.m | .m | Balu-master/MultiView/Bmv_reco3dna.m | 2,239 | utf_8 | 29e34b59c3f3155e8f17b93b6b09344e | % [M,err,ms] = Bmv_reco3dna(m,P)
%
% Toolbox: Balu
%
% 3D affine reconstruction from n corresponding points
%
% It returns a 3D point M that fullfils
% the following projective equations:
%
% m1 = P1*M
% m2 = P2*M
% :
% where mk = m(:,k) are the 2D projection points of 3D point M
% in... |
github | domingomery/Balu-master | Bmv_tqsift.m | .m | Balu-master/MultiView/Bmv_tqsift.m | 4,170 | utf_8 | 1849d73ac546ca5daf5e04b4877f1f29 | % D = Bmv_tqsift(Iq,It,options)
%
% Toolbox: Balu
%
% Search of image query (Iq) in target image (It) using SIFT.
%
% options.q : sliding windows's size in pixels
% options.d : sliding step in pixels
% options.nkp : minimal number of matching keypoints
% options.fast : '1' computes all SIFT keypoi... |
github | domingomery/Balu-master | Bmv_projective2D.m | .m | Balu-master/MultiView/Bmv_projective2D.m | 2,069 | utf_8 | 2d4ea0df539208a583752e87f6bec971 | % J = Bmv_projective2D(I,H,SJ,show)
%
% Toolbox: Balu
%
% 2D proyective transformation.
%
% J = projective2D(I,H,SJ,show) returns a new image J that is computed
% from the 2D projective transformation H of I.
%
% SJ is [NJ MJ] the size of the transformed image J. The
% default of SJ is [NJ,MJ] ... |
github | domingomery/Balu-master | Bmv_guihomography.m | .m | Balu-master/MultiView/Bmv_guihomography.m | 12,514 | utf_8 | e3256a2abe57f425b28ed28cb4c4ba1d | function varargout = Bmv_guihomography(varargin)
% BMV_GUIHOMOGRAPHY M-file for Bmv_guihomography.fig
% BMV_GUIHOMOGRAPHY, by itself, creates a new BMV_GUIHOMOGRAPHY or raises the existing
% singleton*.
%
% H = BMV_GUIHOMOGRAPHY returns the handle to a new BMV_GUIHOMOGRAPHY or the handle to
% ... |
github | domingomery/Balu-master | Bmv_antisimetric.m | .m | Balu-master/MultiView/Bmv_antisimetric.m | 654 | utf_8 | 86236235c55d1fd69c735d673022fa1c | % U = Bmv_antisimetric(u)
%
% Toolbox: Balu
%
% Antisimetric matrix
%
% antisimetric(u) returns the antisimetric matrix of a
% a 3x1 vector u.
%
% U = antisimetric(u) is a 3x3 matrix that
% U*v = cross(u,v) where cross(u,v) is the cross
% product between u and a 3x1 vector v.
%
% Examp... |
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