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github | CVML/toolbox-master | imagesAlign.m | .m | toolbox-master/videos/imagesAlign.m | 8,167 | utf_8 | d125eb5beb502d940be5bd145521f34b | function [H,Ip] = imagesAlign( I, Iref, varargin )
% Fast and robust estimation of homography relating two images.
%
% The algorithm for image alignment is a simple but effective variant of
% the inverse compositional algorithm. For a thorough overview, see:
% "Lucas-kanade 20 years on A unifying framework,"
% S. B... |
github | CVML/toolbox-master | opticalFlow.m | .m | toolbox-master/videos/opticalFlow.m | 7,385 | utf_8 | 0fdca13d3caa4421fc488d0031e7838c | function [Vx,Vy,reliab] = opticalFlow( I1, I2, varargin )
% Coarse-to-fine optical flow using Lucas&Kanade or Horn&Schunck.
%
% Implemented 'type' of optical flow estimation:
% LK: http://en.wikipedia.org/wiki/Lucas-Kanade_method
% HS: http://en.wikipedia.org/wiki/Horn-Schunck_method
% SD: Simple block-based sum of ... |
github | CVML/toolbox-master | seqWriterPlugin.m | .m | toolbox-master/videos/seqWriterPlugin.m | 8,280 | utf_8 | 597792f79fff08b8bb709313267c3860 | function varargout = seqWriterPlugin( cmd, h, varargin )
% Plugin for seqIo and videoIO to allow writing of seq files.
%
% Do not call directly, use as plugin for seqIo or videoIO instead.
% The following is a list of commands available (swp=seqWriterPlugin):
% h=swp('open',h,fName,info) % Open a seq file for writing ... |
github | CVML/toolbox-master | kernelTracker.m | .m | toolbox-master/videos/kernelTracker.m | 9,315 | utf_8 | 4a7d0235f1e518ab5f1c9f1b5450b3f0 | function [allRct, allSim, allIc] = kernelTracker( I, prm )
% Kernel Tracker from Comaniciu, Ramesh and Meer PAMI 2003.
%
% Implements the algorithm described in "Kernel-Based Object Tracking" by
% Dorin Comaniciu, Visvanathan Ramesh and Peter Meer, PAMI 25, 564-577,
% 2003. This is a fast tracking algorithm that utili... |
github | CVML/toolbox-master | seqIo.m | .m | toolbox-master/videos/seqIo.m | 17,019 | utf_8 | 9c631b324bb527372ec3eed3416c5dcc | function out = seqIo( fName, action, varargin )
% Utilities for reading and writing seq files.
%
% A seq file is a series of concatentated image frames with a fixed size
% header. It is essentially the same as merging a directory of images into
% a single file. seq files are convenient for storing videos because: (1)
%... |
github | CVML/toolbox-master | seqReaderPlugin.m | .m | toolbox-master/videos/seqReaderPlugin.m | 9,617 | utf_8 | ad8f912634cafe13df6fc7d67aeff05a | function varargout = seqReaderPlugin( cmd, h, varargin )
% Plugin for seqIo and videoIO to allow reading of seq files.
%
% Do not call directly, use as plugin for seqIo or videoIO instead.
% The following is a list of commands available (srp=seqReaderPlugin):
% h = srp('open',h,fName) % Open a seq file for reading ... |
github | CVML/toolbox-master | pcaApply.m | .m | toolbox-master/classify/pcaApply.m | 3,320 | utf_8 | a06fc0e54d85930cbc0536c874ac63b7 | function varargout = pcaApply( X, U, mu, k )
% Companion function to pca.
%
% Use pca.m to retrieve the principal components U and the mean mu from a
% set of vectors x, then use pcaApply to get the first k coefficients of
% x in the space spanned by the columns of U. See pca for general usage.
%
% If x is large, pcaAp... |
github | CVML/toolbox-master | forestTrain.m | .m | toolbox-master/classify/forestTrain.m | 6,138 | utf_8 | de534e2a010f452a7b13167dbf9df239 | function forest = forestTrain( data, hs, varargin )
% Train random forest classifier.
%
% Dimensions:
% M - number trees
% F - number features
% N - number input vectors
% H - number classes
%
% USAGE
% forest = forestTrain( data, hs, [varargin] )
%
% INPUTS
% data - [NxF] N length F feature vectors
% hs ... |
github | CVML/toolbox-master | fernsRegTrain.m | .m | toolbox-master/classify/fernsRegTrain.m | 5,914 | utf_8 | b9ed2d87a22cb9cbb1e2632495ddaf1d | function [ferns,ysPr] = fernsRegTrain( data, ys, varargin )
% Train boosted fern regressor.
%
% Boosted regression using random ferns as the weak regressor. See "Greedy
% function approximation: A gradient boosting machine", Friedman, Annals of
% Statistics 2001, for more details on boosted regression.
%
% A few notes ... |
github | CVML/toolbox-master | rbfDemo.m | .m | toolbox-master/classify/rbfDemo.m | 2,929 | utf_8 | 14cc64fb77bcac3edec51cf6b84ab681 | function rbfDemo( dataType, noiseSig, scale, k, cluster, show )
% Demonstration of rbf networks for regression.
%
% See rbfComputeBasis for discussion of rbfs.
%
% USAGE
% rbfDemo( dataType, noiseSig, scale, k, cluster, show )
%
% INPUTS
% dataType - 0: 1D sinusoid
% 1: 2D sinusoid
% 2: ... |
github | CVML/toolbox-master | pdist2.m | .m | toolbox-master/classify/pdist2.m | 5,162 | utf_8 | 768ff9e8818251f756c8325368ee7d90 | function D = pdist2( X, Y, metric )
% Calculates the distance between sets of vectors.
%
% Let X be an m-by-p matrix representing m points in p-dimensional space
% and Y be an n-by-p matrix representing another set of points in the same
% space. This function computes the m-by-n distance matrix D where D(i,j)
% is the ... |
github | CVML/toolbox-master | pca.m | .m | toolbox-master/classify/pca.m | 3,244 | utf_8 | 848f2eb05c18a6e448e9d22af27b9422 | function [U,mu,vars] = pca( X )
% Principal components analysis (alternative to princomp).
%
% A simple linear dimensionality reduction technique. Use to create an
% orthonormal basis for the points in R^d such that the coordinates of a
% vector x in this basis are of decreasing importance. Instead of using all
% d bas... |
github | CVML/toolbox-master | kmeans2.m | .m | toolbox-master/classify/kmeans2.m | 5,251 | utf_8 | f941053f03c3e9eda40389a4cc64ee00 | function [ IDX, C, d ] = kmeans2( X, k, varargin )
% Fast version of kmeans clustering.
%
% Cluster the N x p matrix X into k clusters using the kmeans algorithm. It
% returns the cluster memberships for each data point in the N x 1 vector
% IDX and the K x p matrix of cluster means in C.
%
% This function is in some w... |
github | CVML/toolbox-master | acfModify.m | .m | toolbox-master/detector/acfModify.m | 4,202 | utf_8 | 7a49406d51e7a9431b8fd472be0476e8 | function detector = acfModify( detector, varargin )
% Modify aggregate channel features object detector.
%
% Takes an object detector trained by acfTrain() and modifies it. Only
% certain modifications are allowed to the detector and the detector should
% never be modified directly (this may cause the detector to be in... |
github | CVML/toolbox-master | acfDetect.m | .m | toolbox-master/detector/acfDetect.m | 3,659 | utf_8 | cf1384311b16371be6fa4715140e5c81 | function bbs = acfDetect( I, detector, fileName )
% Run aggregate channel features object detector on given image(s).
%
% The input 'I' can either be a single image (or filename) or a cell array
% of images (or filenames). In the first case, the return is a set of bbs
% where each row has the format [x y w h score] and... |
github | CVML/toolbox-master | acfSweeps.m | .m | toolbox-master/detector/acfSweeps.m | 10,730 | utf_8 | 78d640ed4b5b62600dd5164118a15408 | function acfSweeps
% Parameter sweeps for ACF pedestrian detector.
%
% Running the parameter sweeps requires altering internal flags.
% The sweeps are not well documented, use at your own discretion.
%
% Piotr's Computer Vision Matlab Toolbox Version NEW
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Lic... |
github | CVML/toolbox-master | bbGt.m | .m | toolbox-master/detector/bbGt.m | 34,046 | utf_8 | 69e66c9a0cc143fb9a794fbc9233246e | function varargout = bbGt( action, varargin )
% Bounding box (bb) annotations struct, evaluation and sampling routines.
%
% bbGt gives access to two types of routines:
% (1) Data structure for storing bb image annotations.
% (2) Routines for evaluating the Pascal criteria for object detection.
%
% The bb annotation sto... |
github | CVML/toolbox-master | bbApply.m | .m | toolbox-master/detector/bbApply.m | 21,195 | utf_8 | 8c02a6999a84bfb5fcbf2274b8b91a97 | function varargout = bbApply( action, varargin )
% Functions for manipulating bounding boxes (bb).
%
% A bounding box (bb) is also known as a position vector or a rectangle
% object. It is a four element vector with the fields: [x y w h]. A set of
% n bbs can be stores as an [nx4] array, most funcitons below can handle... |
github | CVML/toolbox-master | imwrite2.m | .m | toolbox-master/images/imwrite2.m | 5,086 | utf_8 | c98d66c2cddd9ec90beb9b1bbde31fe0 | function I = imwrite2( I, mulFlag, imagei, path, ...
name, ext, nDigits, nSplits, spliti, varargin )
% Similar to imwrite, except follows a strict naming convention.
%
% Wrapper for imwrite that writes file to the filename:
% fName = [path name int2str2(i,nDigits) '.' ext];
% Using imwrite:
% imwrite( I, fName, wri... |
github | CVML/toolbox-master | convnFast.m | .m | toolbox-master/images/convnFast.m | 9,102 | utf_8 | 03d05e74bb7ae2ecb0afd0ac115fda39 | function C = convnFast( A, B, shape )
% Fast convolution, replacement for both conv2 and convn.
%
% See conv2 or convn for more information on convolution in general.
%
% This works as a replacement for both conv2 and convn. Basically,
% performs convolution in either the frequency or spatial domain, depending
% on wh... |
github | CVML/toolbox-master | imMlGauss.m | .m | toolbox-master/images/imMlGauss.m | 5,674 | utf_8 | 56ead1b25fbe356f7912993d46468d02 | function varargout = imMlGauss( G, symmFlag, show )
% Calculates max likelihood params of Gaussian that gave rise to image G.
%
% Suppose G contains an image of a gaussian distribution. One way to
% recover the parameters of the gaussian is to threshold the image, and
% then estimate the mean/covariance based on the c... |
github | CVML/toolbox-master | montage2.m | .m | toolbox-master/images/montage2.m | 7,484 | utf_8 | 828f57d7b1f67d36eeb6056f06568ebf | function varargout = montage2( IS, prm )
% Used to display collections of images and videos.
%
% Improved version of montage, with more control over display.
% NOTE: Can convert between MxNxT and MxNx3xT image stack via:
% I = repmat( I, [1,1,1,3] ); I = permute(I, [1,2,4,3] );
%
% USAGE
% varargout = montage2( IS, ... |
github | CVML/toolbox-master | jitterImage.m | .m | toolbox-master/images/jitterImage.m | 5,252 | utf_8 | 3310f8412af00fd504c6f94b8c48992c | function IJ = jitterImage( I, varargin )
% Creates multiple, slightly jittered versions of an image.
%
% Takes an image I, and generates a number of images that are copies of the
% original image with slight translation, rotation and scaling applied. If
% the input image is actually an MxNxK stack of images then applie... |
github | CVML/toolbox-master | movieToImages.m | .m | toolbox-master/images/movieToImages.m | 889 | utf_8 | 28c71798642af276951ee27e2d332540 | function I = movieToImages( M )
% Creates a stack of images from a matlab movie M.
%
% Repeatedly calls frame2im. Useful for playback with playMovie.
%
% USAGE
% I = movieToImages( M )
%
% INPUTS
% M - a matlab movie
%
% OUTPUTS
% I - MxNxT array (of images)
%
% EXAMPLE
% load( 'images.mat' ); [X,map]=gray2ind... |
github | CVML/toolbox-master | toolboxUpdateHeader.m | .m | toolbox-master/external/toolboxUpdateHeader.m | 2,255 | utf_8 | 7a5b75e586be48da97c84d20b59887ff | function toolboxUpdateHeader
% Update the headers of all the files.
%
% USAGE
% toolboxUpdateHeader
%
% INPUTS
%
% OUTPUTS
%
% EXAMPLE
%
% See also
%
% Piotr's Computer Vision Matlab Toolbox Version 3.40
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see extern... |
github | CVML/toolbox-master | toolboxGenDoc.m | .m | toolbox-master/external/toolboxGenDoc.m | 3,639 | utf_8 | 4c21fb34fa9b6002a1a98a28ab40c270 | function toolboxGenDoc
% Generate documentation, must run from dir toolbox.
%
% 1) Make sure to update and run toolboxUpdateHeader.m
% 2) Update history.txt appropriately, including w current version
% 3) Update overview.html file with the version/date/link to zip:
% edit external/m2html/templates/frame-piotr/overv... |
github | CVML/toolbox-master | toolboxHeader.m | .m | toolbox-master/external/toolboxHeader.m | 2,391 | utf_8 | 30c24a94fb54ca82622719adcab17903 | function [y1,y2] = toolboxHeader( x1, x2, x3, prm )
% One line description of function (will appear in file summary).
%
% General commments explaining purpose of function [width is 75
% characters]. There may be multiple paragraphs. In special cases some or
% all of these guidelines may need to be broken.
%
% Next come... |
github | CVML/toolbox-master | mdot.m | .m | toolbox-master/external/m2html/mdot.m | 2,516 | utf_8 | 34a14428c433e118d1810e23f5a6caf5 | function mdot(mmat, dotfile,f)
%MDOT - Export a dependency graph into DOT language
% MDOT(MMAT, DOTFILE) loads a .mat file generated by M2HTML using option
% ('save','on') and writes an ascii file using the DOT language that can
% be drawn using <dot> or <neato> .
% MDOT(MMAT, DOTFILE,F) builds the graph containing... |
github | CVML/toolbox-master | m2html.m | .m | toolbox-master/external/m2html/m2html.m | 49,063 | utf_8 | 472047b4c36a4f8b162012840e31b59b | function m2html(varargin)
%M2HTML - Documentation Generator for Matlab M-files and Toolboxes in HTML
% M2HTML by itself generates an HTML documentation of the Matlab M-files found
% in the direct subdirectories of the current directory. HTML files are
% written in a 'doc' directory (created if necessary). All the o... |
github | CVML/toolbox-master | doxysearch.m | .m | toolbox-master/external/m2html/private/doxysearch.m | 7,724 | utf_8 | 8331cde8495f34b86aef8c18656b37f2 | function result = doxysearch(query,filename)
%DOXYSEARCH Search a query in a 'search.idx' file
% RESULT = DOXYSEARCH(QUERY,FILENAME) looks for request QUERY
% in FILENAME (Doxygen search.idx format) and returns a list of
% files responding to the request in RESULT.
%
% See also DOXYREAD, DOXYWRITE
% Copyright (C)... |
github | CVML/toolbox-master | doxywrite.m | .m | toolbox-master/external/m2html/private/doxywrite.m | 3,584 | utf_8 | 3255d8f824957ebc173dde374d0f78af | function doxywrite(filename, kw, statinfo, docinfo)
%DOXYWRITE Write a 'search.idx' file compatible with DOXYGEN
% DOXYWRITE(FILENAME, KW, STATINFO, DOCINFO) writes file FILENAME
% (Doxygen search.idx. format) using the cell array KW containing the
% word list, the sparse matrix (nbword x nbfile) with non-null value... |
github | CVML/toolbox-master | doxyread.m | .m | toolbox-master/external/m2html/private/doxyread.m | 3,093 | utf_8 | 3152e7d26bf7ac64118be56f72832a20 | function [statlist, docinfo] = doxyread(filename)
%DOXYREAD Read a 'search.idx' file generated by DOXYGEN
% STATLIST = DOXYREAD(FILENAME) reads FILENAME (Doxygen search.idx
% format) and returns the list of keywords STATLIST as a cell array.
% [STATLIST, DOCINFO] = DOXYREAD(FILENAME) also returns a cell array
% con... |
github | CVML/toolbox-master | imwrite2split.m | .m | toolbox-master/external/deprecated/imwrite2split.m | 1,617 | utf_8 | 4222fd45df123e6dec9ef40ae793004f | % Writes/reads a large set of images into/from multiple directories.
%
% This is useful since certain OS handle very large directories (of say
% >20K images) rather poorly (I'm talking to you Bill). Thus, can take
% 100K images, and write into 5 separate directories, then read them back
% in.
%
% USAGE
% I = imwrite2... |
github | CVML/toolbox-master | playmovies.m | .m | toolbox-master/external/deprecated/playmovies.m | 1,935 | utf_8 | ef2eaad8a130936a1a281f1277ca0ea1 | % [4D] shows R videos simultaneously as a movie.
%
% Plays a movie.
%
% USAGE
% playmovies( I, [fps], [loop] )
%
% INPUTS
% I - MxNxTxR or MxNx1xTxR or MxNx3xTxR array (if MxNxT calls
% playmovie)
% fps - [100] maximum number of frames to display per second use
% fps==0 to introduce n... |
github | CVML/toolbox-master | pca_apply_large.m | .m | toolbox-master/external/deprecated/pca_apply_large.m | 2,062 | utf_8 | af84a2179b9d8042519bc6b378736a88 | % Wrapper for pca_apply that allows for application to large X.
%
% Wrapper for pca_apply that splits and processes X in parts, this may be
% useful if processing cannot be done fully in parallel because of memory
% constraints. See pca_apply for usage.
%
% USAGE
% same as pca_apply
%
% INPUTS
% same as pca_apply
%
%... |
github | CVML/toolbox-master | montages2.m | .m | toolbox-master/external/deprecated/montages2.m | 2,269 | utf_8 | 505e2be915d65fff8bfef8473875cc98 | % MONTAGES2 [4D] Used to display R sets of T images each.
%
% Displays one montage (see montage2) per row. Each of the R image sets is
% flattened to a single long image by concatenating the T images in the
% set. Alternative to montages.
%
% USAGE
% varargout = montages2( IS, [montage2prms], [padSiz] )
%
% INPUTS
% ... |
github | CVML/toolbox-master | filter_gauss_1D.m | .m | toolbox-master/external/deprecated/filter_gauss_1D.m | 1,137 | utf_8 | 94a453b82dcdeba67bd886e042d552d9 | % 1D Gaussian filter.
%
% Equivalent to (but faster then):
% f = fspecial('Gaussian',[2*r+1,1],sigma);
% f = filter_gauss_nD( 2*r+1, r+1, sigma^2 );
%
% USAGE
% f = filter_gauss_1D( r, sigma, [show] )
%
% INPUTS
% r - filter size=2r+1, if r=[] -> r=ceil(2.25*sigma)
% sigma - standard deviation of filter
% ... |
github | CVML/toolbox-master | clfEcoc.m | .m | toolbox-master/external/deprecated/clfEcoc.m | 1,493 | utf_8 | e77e1b4fd5469ed39f47dd6ed15f130f | function clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets)
% Wrapper for ecoc that makes ecoc compatible with nfoldxval.
%
% Requires the SVM toolbox by Anton Schwaighofer.
%
% USAGE
% clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets)
%
% INPUTS
% p - data dimension
% clfInit - bi... |
github | CVML/toolbox-master | getargs.m | .m | toolbox-master/external/deprecated/getargs.m | 3,455 | utf_8 | de2bab917fa6b9ba3099f1c6b6d68cf0 | % Utility to process parameter name/value pairs.
%
% DEPRECATED -- ONLY USED BY KMEANS2? SHOULD BE REMOVED.
% USE GETPARAMDEFAULTS INSTEAD.
%
% Based on code fromt Matlab Statistics Toolobox's "private/statgetargs.m"
%
% [EMSG,A,B,...]=GETARGS(PNAMES,DFLTS,'NAME1',VAL1,'NAME2',VAL2,...)
% accepts a cell array PNAMES o... |
github | CVML/toolbox-master | normxcorrn_fg.m | .m | toolbox-master/external/deprecated/normxcorrn_fg.m | 2,699 | utf_8 | e65c38d97efb3a624e0fa94a97f75eb6 | % Normalized n-dimensional cross-correlation with a mask.
%
% Similar to normxcorrn, except takes an additional argument that specifies
% a figure ground mask for the T. That is T_fg must be of the same
% dimensions as T, with each entry being 0 or 1, where zero specifies
% regions to ignore (the ground) and 1 specifi... |
github | CVML/toolbox-master | makemovie.m | .m | toolbox-master/external/deprecated/makemovie.m | 1,266 | utf_8 | 9a03d9a5227c4eaa86520f206ce283e7 | % [3D] Used to convert a stack of T images into a movie.
%
% To display same data statically use montage.
%
% USAGE
% M = makemovies( IS )
%
% INPUTS
% IS - MxNxT or MxNx1xT or MxNx3xT array of movies.
%
% OUTPUTS
% M - resulting movie
%
% EXAMPLE
% load( 'images.mat' );
% M = makemovie(... |
github | CVML/toolbox-master | localsum_block.m | .m | toolbox-master/external/deprecated/localsum_block.m | 815 | utf_8 | 1216b03a3bd44ff1fc3256de16a2f1c6 | % Calculates the sum in non-overlapping blocks of I of size dims.
%
% Similar to localsum except gets sum in non-overlapping windows.
% Equivalent to doing localsum, and then subsampling (except more
% efficient).
%
% USAGE
% I = localsum_block( I, dims )
%
% INPUTS
% I - matrix to compute sum over
% dims -... |
github | CVML/toolbox-master | imrotate2.m | .m | toolbox-master/external/deprecated/imrotate2.m | 1,326 | utf_8 | bb2ff6c3138ce5f53154d58d7ebc4f31 | % Custom version of imrotate that demonstrates use of apply_homography.
%
% Works exactly the same as imrotate. For usage see imrotate.
%
% USAGE
% IR = imrotate2( I, angle, [method], [bbox] )
%
% INPUTS
% I - 2D image [converted to double]
% angle - angle to rotate in degrees
% method - ['linear'] 'neare... |
github | CVML/toolbox-master | imSubsResize.m | .m | toolbox-master/external/deprecated/imSubsResize.m | 1,338 | utf_8 | cd7dedf790c015adfb1f2d620e9ed82f | % Resizes subs by resizVals.
%
% Resizes subs in subs/vals image representation by resizVals.
%
% This essentially replaces each sub by sub.*resizVals. The only subtlety
% is that in images the leftmost sub value is .5, so for example when
% resizing by a factor of 2, the first pixel is replaced by 2 pixels and so
% l... |
github | CVML/toolbox-master | imtranslate.m | .m | toolbox-master/external/deprecated/imtranslate.m | 1,183 | utf_8 | 054727fb31c105414b655c0f938b6ced | % Translate an image to subpixel accuracy.
%
% Note that for subplixel accuracy cannot use nearest neighbor interp.
%
% USAGE
% IR = imtranslate( I, dx, dy, [method], [bbox] )
%
% INPUTS
% I - 2D image [converted to double]
% dx - x translation (right)
% dy - y translation (up)
% method - ['linear... |
github | CVML/toolbox-master | randperm2.m | .m | toolbox-master/external/deprecated/randperm2.m | 1,398 | utf_8 | 5007722f3d5f5ba7c0f83f32ef8a3a2c | % Returns a random permutation of integers.
%
% randperm2(n) is a random permutation of the integers from 1 to n. For
% example, randperm2(6) might be [2 4 5 6 1 3]. randperm2(n,k) is only
% returns the first k elements of the permuation, so for example
% randperm2(6) might be [2 4]. This is a faster version of randp... |
github | CVML/toolbox-master | apply_homography.m | .m | toolbox-master/external/deprecated/apply_homography.m | 3,582 | utf_8 | 9c3ed72d35b1145f41114e6e6135b44f | % Applies the homography defined by H on the image I.
%
% Takes the center of the image as the origin, not the top left corner.
% Also, the coordinate system is row/ column format, so H must be also.
%
% The bounding box of the image is set by the BBOX argument, a string that
% can be 'loose' (default) or 'crop'. When ... |
github | CVML/toolbox-master | pca_apply.m | .m | toolbox-master/external/deprecated/pca_apply.m | 2,427 | utf_8 | 0831befb6057f8502bc492227455019a | % Companion function to pca.
%
% Use pca to retrieve the principal components U and the mean mu from a
% set fo vectors X1 via [U,mu,vars] = pca(X1). Then given a new
% vector x, use y = pca_apply( x, U, mu, vars, k ) to get the first k
% coefficients of x in the space spanned by the columns of U. See pca for
% genera... |
github | CVML/toolbox-master | mode2.m | .m | toolbox-master/external/deprecated/mode2.m | 731 | utf_8 | 5c9321ef4b610b4f4a2d43902a68838e | % Returns the mode of a vector.
%
% Was mode not part of Matlab before?
%
% USAGE
% y = mode2( x )
%
% INPUTS
% x - vector of integers
%
% OUTPUTS
% y - mode
%
% EXAMPLE
% x = randint2( 1, 10, [1 3] )
% mode(x), mode2( x )
%
% See also MODE
% Piotr's Image&Video Toolbox Version 1.5
% Written and maintain... |
github | CVML/toolbox-master | savefig.m | .m | toolbox-master/external/other/savefig.m | 13,459 | utf_8 | 2b8463f9b01ceb743e440d8fb5755829 | function savefig(fname, varargin)
% Usage: savefig(filename, fighdl, options)
%
% Saves a pdf, eps, png, jpeg, and/or tiff of the contents of the fighandle's (or current) figure.
% It saves an eps of the figure and the uses Ghostscript to convert to the other formats.
% The result is a cropped, clean picture. There a... |
github | CVML/toolbox-master | dirSynch.m | .m | toolbox-master/matlab/dirSynch.m | 4,570 | utf_8 | d288299d31d15f1804183206d0aa0227 | function dirSynch( root1, root2, showOnly, flag, ignDate )
% Synchronize two directory trees (or show differences between them).
%
% If a file or directory 'name' is found in both tree1 and tree2:
% 1) if 'name' is a file in both the pair is considered the same if they
% have identical size and identical datestamp... |
github | CVML/toolbox-master | plotRoc.m | .m | toolbox-master/matlab/plotRoc.m | 5,212 | utf_8 | 008f9c63073c6400c4960e9e213c47e5 | function [h,miss,stds] = plotRoc( D, varargin )
% Function for display of rocs (receiver operator characteristic curves).
%
% Display roc curves. Consistent usage ensures uniform look for rocs. The
% input D should have n rows, each of which is of the form:
% D = [falsePosRate truePosRate]
% D is generated, for exampl... |
github | CVML/toolbox-master | simpleCache.m | .m | toolbox-master/matlab/simpleCache.m | 4,098 | utf_8 | 92df86b0b7e919c9a26388e598e4d370 | function varargout = simpleCache( op, cache, varargin )
% A simple cache that can be used to store results of computations.
%
% Can save and retrieve arbitrary values using a vector (includnig char
% vectors) as a key. Especially useful if a function must perform heavy
% computation but is often called with the same in... |
github | CVML/toolbox-master | tpsInterpolate.m | .m | toolbox-master/matlab/tpsInterpolate.m | 1,646 | utf_8 | d3bd3a26d048f32cfdc17884ccae6d8c | function [xsR,ysR] = tpsInterpolate( warp, xs, ys, show )
% Apply warp (obtained by tpsGetWarp) to a set of new points.
%
% USAGE
% [xsR,ysR] = tpsInterpolate( warp, xs, ys, [show] )
%
% INPUTS
% warp - [see tpsGetWarp] bookstein warping parameters
% xs, ys - points to apply warp to
% show - [1] will disp... |
github | CVML/toolbox-master | checkNumArgs.m | .m | toolbox-master/matlab/checkNumArgs.m | 3,796 | utf_8 | 726c125c7dc994c4989c0e53ad4be747 | function [ x, er ] = checkNumArgs( x, siz, intFlag, signFlag )
% Helper utility for checking numeric vector arguments.
%
% Runs a number of tests on the numeric array x. Tests to see if x has all
% integer values, all positive values, and so on, depending on the values
% for intFlag and signFlag. Also tests to see if ... |
github | CVML/toolbox-master | fevalDistr.m | .m | toolbox-master/matlab/fevalDistr.m | 11,227 | utf_8 | 7e4d5077ef3d7a891b2847cb858a2c6c | function [out,res] = fevalDistr( funNm, jobs, varargin )
% Wrapper for embarrassingly parallel function evaluation.
%
% Runs "r=feval(funNm,jobs{i}{:})" for each job in a parallel manner. jobs
% should be a cell array of length nJob and each job should be a cell array
% of parameters to pass to funNm. funNm must be a f... |
github | CVML/toolbox-master | medfilt1m.m | .m | toolbox-master/filters/medfilt1m.m | 2,998 | utf_8 | a3733d27c60efefd57ada9d83ccbaa3d | function y = medfilt1m( x, r, z )
% One-dimensional adaptive median filtering with missing values.
%
% Applies a width s=2*r+1 one-dimensional median filter to vector x, which
% may contain missing values (elements equal to z). If x contains no
% missing values, y(j) is set to the median of x(j-r:j+r). If x contains
% ... |
github | CVML/toolbox-master | FbMake.m | .m | toolbox-master/filters/FbMake.m | 6,692 | utf_8 | b625c1461a61485af27e490333350b4b | function FB = FbMake( dim, flag, show )
% Various 1D/2D/3D filterbanks (hardcoded).
%
% USAGE
% FB = FbMake( dim, flag, [show] )
%
% INPUTS
% dim - dimension
% flag - controls type of filterbank to create
% - if d==1
% 1: gabor filter bank for spatiotemporal stuff
% - if d==2
% ... |
github | andtem2000/PPG-master | BlandAltman.m | .m | PPG-master/BlandAltman.m | 13,740 | utf_8 | ef845c29d0d55ebc112cccc6976e6463 | % BlandAltman - draws a Blant-Altman and correlation graph for two
% datasets.
%
% BlandAltman(data1, data2)
% BlandAltman(data1, data2,label) - Names of data sets. Formats can be
% - {'Name1'}
% - {'Name1, 'Name2'}
% - {'Name1, 'Name2', 'Units'}
% BlandAltman(data1, data2,label,tit,gnames)
% BlandAltman(data1, d... |
github | isrish/EM_WD-master | cWiseEM_WDMML.m | .m | EM_WD-master/cWiseEM_WDMML.m | 17,354 | utf_8 | 80269da440d8bc604ca6ec71d7678883 | function obj = cWiseEM_WDMML(X,Kmax,varargin)
% CWiseEM_WDMML EM algorithm for weighted data clustering
% obj = cWiseEM_WDMML(X,Kmax)
% obj = cWiseEM_WDMML(X,Kmax,varargin)
% X: [n x d] data matrix where n is the number of data points and d is the feature dimention
% WSampesAprior: is [n x 1] a wei... |
github | isrish/EM_WD-master | EM_WDF.m | .m | EM_WD-master/EM_WDF.m | 5,671 | utf_8 | e5471cf47c4f6edb79ce33d03e6f7a74 | function [obj] = EM_WDF(X,WS,K,varargin)
% [W,M,V,L,WS,X] = EM_WDF(X,WSampesAprior,K,Init)
%
% EM algorithm for weigthed data GMM WITH fixed weight
%
% Inputs:
% X(n,d) - input data, n=number of observations, d=dimension of variable
% WS -input data weight
% K - maximum number of Gaussian components allo... |
github | isrish/EM_WD-master | EM_WD.m | .m | EM_WD-master/EM_WD.m | 9,143 | utf_8 | 30c7860b20efc4c0d8e57dd8b261ccbb | function obj = EM_WD(X,K,varargin)
% [obj] = EM_WD(X,K,varargin)
%
% EM algorithm for weigthed data clustering
%
% Inputs:
% X(n,d) - input data, n=number of observations, d=dimension of variable
% K - maximum number of Gaussian components allowed excluding out-liner
% varargin
% 'Init' - struct of... |
github | utiasSTARS/msckf-swf-comparison-master | loadCalibrationCamToCam.m | .m | msckf-swf-comparison-master/kitti_extraction/utils/devkit/loadCalibrationCamToCam.m | 1,894 | utf_8 | 88db832a2338f205ea36b1a9f6231aed | function calib = loadCalibrationCamToCam(filename)
% open file
fid = fopen(filename,'r');
if fid<0
calib = [];
return;
end
% read corner distance
calib.cornerdist = readVariable(fid,'corner_dist',1,1);
% read all cameras (maximum: 100)
for cam=1:100
% read variables
S_ = readVariable(fid,['S_' num2s... |
github | utiasSTARS/msckf-swf-comparison-master | loadCalibrationRigid.m | .m | msckf-swf-comparison-master/kitti_extraction/utils/devkit/loadCalibrationRigid.m | 855 | utf_8 | 9148661cd7335b41dace4f57bd25b3a4 | function Tr = loadCalibrationRigid(filename)
% open file
fid = fopen(filename,'r');
if fid<0
error(['ERROR: Could not load: ' filename]);
end
% read calibration
R = readVariable(fid,'R',3,3);
T = readVariable(fid,'T',3,1);
Tr = [R T;0 0 0 1];
% close file
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | srandoux/manyears-code-master | hannWin.m | .m | manyears-code-master/Experiment/hannWin.m | 1,923 | utf_8 | 6d7553e268917d936c702d8d7b7ff5cf | %
% *************************************************************************
% * Authors: Francois Grondin *
% *************************************************************************
% * Inputs: nSamples Size of the window *
% * Outpu... |
github | srandoux/manyears-code-master | createFrames.m | .m | manyears-code-master/Experiment/createFrames.m | 3,824 | utf_8 | 626e9434de04a655c14c77805cbaf12d | %
% *************************************************************************
% * Authors: Laurier Demers, Francois Grondin, Arash Vakili *
% *************************************************************************
% * Inputs: x Vector *
% * ... |
github | srandoux/manyears-code-master | spectrogram.m | .m | manyears-code-master/Experiment/spectrogram.m | 3,567 | utf_8 | dc7caed6a54be0bf47ef8316f7c7f836 | %
% *************************************************************************
% * Authors: Francois Grondin *
% *************************************************************************
% * Inputs: x Vector *
% * ... |
github | srandoux/manyears-code-master | fusionFrames.m | .m | manyears-code-master/Experiment/fusionFrames.m | 3,575 | utf_8 | 5bd7ae509176e893e307ec1463a5f02b | %
% *************************************************************************
% * Authors: Laurier Demers, Francois Grondin, Arash Vakili *
% *************************************************************************
% * Inputs: framesMatrix Matrix made of all the frames *
% * ... |
github | srandoux/manyears-code-master | extractTrackSources.m | .m | manyears-code-master/Experiment/extractTrackSources.m | 3,719 | utf_8 | 86dbb3be1ce3661c3bceec62d2cfdaeb | %
% *************************************************************************
% * Authors: Francois Grondin *
% *************************************************************************
% * Inputs: trackFilePath Path of the track file *
% * Outpu... |
github | haoyio/LowRankMDP-master | lanbpro.m | .m | LowRankMDP-master/lrm/PROPACK/lanbpro.m | 19,514 | utf_8 | 897b157335c2a5c269845380328709c4 | function [U,B_k,V,p,ierr,work] = lanbpro(varargin)
%LANBPRO Lanczos bidiagonalization with partial reorthogonalization.
% LANBPRO computes the Lanczos bidiagonalization of a real
% matrix using the with partial reorthogonalization.
%
% [U_k,B_k,V_k,R,ierr,work] = LANBPRO(A,K,R0,OPTIONS,U_old,B_old,V_old)
% ... |
github | haoyio/LowRankMDP-master | lanpro.m | .m | LowRankMDP-master/lrm/PROPACK/lanpro.m | 14,762 | utf_8 | ff3aa513289e3776117575af43b5ed1b | function [Q_k,T_k,r,anorm,ierr,work] = lanpro(A,nin,kmax,r,options,...
Q_k,T_k,anorm)
%LANPRO Lanczos tridiagonalization with partial reorthogonalization
% LANPRO computes the Lanczos tridiagonalization of a real symmetric
% matrix using the symmetric Lanczos algorithm with partial
% reorthogonalization... |
github | guoyilin/face-alignment-master | calc_meanshape.m | .m | face-alignment-master/initial_shape/calc_meanshape.m | 1,298 | utf_8 | c679ed8763187320b2e46763a3dcf9b7 | function mean_shape = calc_meanshape(shapepathlistfile)
fid = fopen(shapepathlistfile);
shapepathlist = textscan(fid, '%s', 'delimiter', '\n');
if isempty(shapepathlist)
error('no shape file found');
mean_shape = [];
return;
end
shape_header = loadshape(shapepathlist{1}{1});
if isempty(shape_header)
... |
github | guoyilin/face-alignment-master | globalregression.m | .m | face-alignment-master/src/globalregression.m | 6,485 | utf_8 | 5426cba4296d5053506ba11c7d46a44c | function [W, Tr_Data] = globalregression(binaryfeatures, Tr_Data, params, stage)
%GLOBALREGRESSION Summary of this function goes here
% Function: implement global regression given binary features and
% groundtruth shape
% Detailed explanation goes here
% Input:
% binaryfeatures: extracted binary features... |
github | guoyilin/face-alignment-master | derivebinaryfeat.m | .m | face-alignment-master/src/derivebinaryfeat.m | 11,958 | utf_8 | 38a64e44f89ad8a72457eea002dac439 | function binfeatures = derivebinaryfeat(randf, Tr_Data, params, stage)
%DERIVEBINARYFEAT Summary of this function goes here
% Function: Derive binary features for each sample given learned random forest
% Detailed explanation goes here
% Input:
% lmarkID: the ID of landmark
% randf: learned random f... |
github | guoyilin/face-alignment-master | loadsamples.m | .m | face-alignment-master/src/loadsamples.m | 4,514 | utf_8 | 0e2e043605a594bbca1bfde22be1d9f8 | function Data = loadsamples(imgpathlistfile, exc_setlabel)
%LOADSAMPLES Summary of this function goes here
% Function: load samples from dbname database
% Detailed explanation goes here
% Input:
% dbname: the name of one database
% exc_setlabel: excluded set label
% Output:
% Data: loaded ... |
github | guoyilin/face-alignment-master | train_randomfs.m | .m | face-alignment-master/src/train_randomfs.m | 11,616 | utf_8 | f1ce1fa72bf0c95cd742c020f7f571f7 | function rfs = train_randomfs(Tr_Data, params, stage)
%TRAIN_RANDOMFS Summary of this function goes here
% Function: train random forest for each landmark
% Detailed explanation goes here
% Input:
% lmarkID: ID of landmark
% stage: the stage of training process
% Output:
% randf: learned ra... |
github | guoyilin/face-alignment-master | globalprediction.m | .m | face-alignment-master/src/globalprediction.m | 5,695 | utf_8 | ca49ba91b99f5b1ade283952a0c8cdb4 | function Te_Data = globalprediction(binaryfeatures, W, Te_Data, params, stage)
%GLOBALREGRESSION Summary of this function goes here
% Function: implement global regression given binary features and
% groundtruth shape
% Detailed explanation goes here
% Input:
% binaryfeatures: extracted binary features f... |
github | INCF/MUSIC-master | makeTestSpikes.m | .m | MUSIC-master/examples/makeTestSpikes.m | 376 | utf_8 | 4f4467437b980cf58997fbe54b6d237d | % makeTestSpikes('viewevents-spikes0.dat',5000,10,1)
function makeTestSpikes(filename, width, freq, maxTime)
spikeTimes = sort(maxTime*rand(ceil(width*freq*maxTime),1));
spikeTimes(spikeTimes < 1e-5) = [];
id = floor(width*rand(size(spikeTimes)));
fid = fopen(filename,'w');
for i=1:length(spikeTimes)
fprintf(f... |
github | INCF/MUSIC-master | makeNeuronGrid.m | .m | MUSIC-master/examples/makeNeuronGrid.m | 755 | utf_8 | 0fb920b0ac443b0a6529fd4162c9647b | % makeNeuronGrid.m by Johannes Hjorth
%
% This function generates a grid of neurons for use with
% viewevents.cpp
%
% makeNeuronGrid('neuronGrid.data', ...
% (-1:1)*150,(-1:1)*150, (-1:1)*150, 30, ...
% [0.25 0.53 0.1], [1 0.9 0])
%
%makeNeuronGrid('neuronGridPlane.data',(-11:11)*50,(-11:1... |
github | HongkunGe/Image-Registration-by-Learning-Common-Feature-master | KCCA_SimilarityMap.m | .m | Image-Registration-by-Learning-Common-Feature-master/utility/KCCA_SimilarityMap.m | 2,504 | utf_8 | 1e1dc8c3495a24355714974dd840cd33 | function [ ] = KCCA_SimilarityMap( currentFolder,No, coordinate, CTfeatureT,MRIfeatureT)
% KCCA_SimilarityMap aims to evaluate the CTfeatureT and MRIfeatureT, will
% generate a color code map of similarity.
% INPUT: currentFolder -FOLDER for work space.
% No -index of subjec... |
github | HongkunGe/Image-Registration-by-Learning-Common-Feature-master | freezeColors.m | .m | Image-Registration-by-Learning-Common-Feature-master/utility/freezeColors_v23_cbfreeze/freezeColors/freezeColors.m | 9,815 | utf_8 | 2068d7a4f7a74d251e2519c4c5c1c171 | function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors freeze... |
github | lucastheis/nlpcaffe-master | prepare_batch.m | .m | nlpcaffe-master/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | lucastheis/nlpcaffe-master | matcaffe_demo_vgg.m | .m | nlpcaffe-master/matlab/caffe/matcaffe_demo_vgg.m | 3,036 | utf_8 | f836eefad26027ac1be6e24421b59543 | function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
%
% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional... |
github | lucastheis/nlpcaffe-master | matcaffe_demo.m | .m | nlpcaffe-master/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | lucastheis/nlpcaffe-master | matcaffe_demo_vgg_mean_pix.m | .m | nlpcaffe-master/matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 3,069 | utf_8 | 04b831d0f205ef0932c4f3cfa930d6f9 | function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep Convo... |
github | giyoungjeon/Visualize-DBN-master | sigmoid.m | .m | Visualize-DBN-master/sigmoid.m | 217 | utf_8 | 1aa59f098df5b3427462b5d340d93448 | % simple wrapper for sigmoid function
function [y] = sigmoid(x, use_tanh)
if nargin < 2
use_tanh = 0;
end
switch use_tanh
case 0
y = 1./(1 + exp(-x));
case 1
y = tanh(x);
case 2
y = max (x, 0);
end
|
github | Robert0812/deepsaldet-master | prepare_batch.m | .m | deepsaldet-master/caffe-sal/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | Robert0812/deepsaldet-master | matcaffe_demo.m | .m | deepsaldet-master/caffe-sal/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | rosrad/chime3-master | sp_LSA_SR.m | .m | chime3-master/noise-reduction/I2R/betaMath/sp_LSA_SR.m | 1,620 | utf_8 | ae7cdb489ac0e1a3cd0fb00b1ec765e9 |
function eh = sp_LSA_SR(noisy)
noisy = noisy*32767;
len = length(noisy);
[PSD_n, pSNR] = sp_noiseEstimation_SR(noisy);
eh = zeros(len,1);
load betaParr;
load hypergeo;
R = ones(129,1);
A_pre = ones(129,1);
frame_num = floor(len/128);
d... |
github | rosrad/chime3-master | sp_noiseEstimation_SR.m | .m | chime3-master/noise-reduction/I2R/betaMath/sp_noiseEstimation_SR.m | 931 | utf_8 | d5e2fa863d3e794ea55f95280e2ffcf7 |
function [PSD_n, pSNR] = sp_noiseEstimation_SR(noisy)
% ----- initial --------
n = noisy( 1 : 256 );
N1 = fft(n);
N = abs(N1(1:129));
Dw = N;
D1 = N;
minBuffer = N*[1 1 1 1];
FrameNum = 0;
frame_num = min(20000, floor(length(noisy)/128));
% -------------------------
Y = zeros(129,1);
for ij = 0 :... |
github | rosrad/chime3-master | sp_Wiener_SR.m | .m | chime3-master/noise-reduction/I2R/betaMath/sp_Wiener_SR.m | 1,061 | utf_8 | a5db033aaa6725e235d7da316a59dcac |
function eh = sp_Wiener_SR(noisy)
noisy = noisy*32767;
len = length(noisy);
[PSD_n, pSNR] = sp_noiseEstimation_SR(noisy);
eh = zeros(len,1);
load betaParr;
load hypergeo;
R = ones(129,1);
A_pre = ones(129,1);
frame_num = floor(len/128);
di... |
github | rosrad/chime3-master | sp_betaOrder_SR.m | .m | chime3-master/noise-reduction/I2R/betaMath/sp_betaOrder_SR.m | 1,737 | utf_8 | 9fafd7ccf4860270f3a2edcbe4d65547 |
function eh = sp_betaOrder_SR(noisy)
noisy = noisy*32767;
len = length(noisy);
[PSD_n, pSNR] = sp_noiseEstimation_SR(noisy);
eh = zeros(len,1);
load betaParr;
load hypergeo;
R = ones(129,1);
A_pre = ones(129,1);
frame_num = floor(len/128);
... |
github | rosrad/chime3-master | sp_MMSE_SR.m | .m | chime3-master/noise-reduction/I2R/betaMath/sp_MMSE_SR.m | 1,679 | utf_8 | c2591b1d19fc70366daba8e39a01f748 | % Subroutine code
function eh = sp_MMSE_SR(noisy, dB)
noisy = noisy*32767;
len = length(noisy);
[PSD_n, pSNR] = sp_noiseEstimation_SR(noisy);
% PSD_n = PSD_n*((0.5-dB/(30))^2);
eh = zeros(len,1);
load betaParr;
load hypergeo;
R = ones(129,1);
A_p... |
github | rosrad/chime3-master | sphWrite.m | .m | chime3-master/noise-reduction/I2R/betaUltis/sphWrite.m | 267 | utf_8 | 0de6607fb797a9464535b4d1b289f2fe |
% to write signal into sphere format
function sphWrite(sphNameW, header, signal)
sig = [header; lin2mu(signal)];
fid_w = fopen(sphNameW, 'wb');
fwrite(fid_w, sig, 'uint8');
fclose(fid_w);
return; |
github | ingle/soupr-master | cplane_crossval.m | .m | soupr-master/cplane_crossval.m | 2,446 | utf_8 | b060f6f2575670d6e9caef538e9947e3 | % FUNCTION [zgrid, xgrid, ygrid, ocvlambda, lambdaVec, valVec] = ...
% CPLANE_CROSSVAL( x, y, f, xvec, yvec )
% Inputs: (x,y) known data locations
% f known data point at location (x,y)
% (xvec,yvec) query location(s), both monotonic increasing vectors
% Outputs: zgrid imputed values at grid locat... |
github | rboman/progs-master | interp.m | .m | progs-master/classes/sph0/matlab/interp.m | 1,246 | utf_8 | baa4fe7b354da4d850d4302c5770f21c |
function interp()
close all; clear all;
% plot M4 spline kernel
x=linspace(-3,3,100);
figure;
h=0.5;
plot(x,m4spline(x,h));
hold on
plot(x,gausskernel(x,h));
grid;
xlabel('x')
ylabel('kernel')
legend('M4 spline', 'gaussian')
% verify that int W = 1
%int... |
github | Wiss/LabControl1-master | resample_sys_dp.m | .m | LabControl1-master/resample_sys_dp.m | 534 | utf_8 | 02c0e39c02af03190cf9184cf2e9d1ef | %% Resampling function
function [xk, wk, idx] = resample_sys_dp(xk, wk)
Ns = length(wk); % Ns = number of particles
% this is performing latin hypercube sampling on wk
edges = min([0 cumsum(wk)'],1); % protect against accumulated round-off
edges(end) = 1; % get the upper edge exact
u1 = rand/Ns;
% th... |
github | TTgogogo/Global-contrast-based-salient-region-detection-master | xml2yaml.m | .m | Global-contrast-based-salient-region-detection-master/Objectness/xml2yaml.m | 882 | utf_8 | 24788cfc055b5979909c95d1a16395ae | %% Convert the file type of opencv xml annotations to yaml thus it can be read by opencv
% This functions relies on http://code.google.com/p/yamlmatlab/
% The results needs to be further refined to deal with indentation problem
function xml2yaml(wkDir)
fNs = dir([wkDir '*.xml']);
fNum = length(fNs);
for ... |
github | rohitgirdhar-cmu-experimental/3dObjectDetection-master | genImgsAndLabs.m | .m | 3dObjectDetection-master/eval/NYU/genImgsAndLabs.m | 1,551 | utf_8 | 07df6c9b7844f9df9bf1dc614ca1878b | function genImgsAndLabs(images, labels)
if nargin == 0
load('nyu_depth_v2_labeled.mat');
end
DEBUG = 1;
load('splits.mat');
% names = {'bed', 'chair', 'mtv', 'sofa', 'table'};
ids = {[157], [5], [49, 172], [83], [19]};
myids = {[1], [2], [3], [4], [5]};
imgsdir = 'JPEGImages';
labelsdir = 'Labels';
for i = 1 : s... |
github | rohitgirdhar-cmu-experimental/3dObjectDetection-master | example_layout.m | .m | 3dObjectDetection-master/eval/NYUdevkit/example_layout.m | 4,470 | utf_8 | faaf53dfba2457f3f7e5542cd51ad5fb | function example_layout
% change this path if you install the VOC code elsewhere
addpath([cd '/VOCcode']);
% initialize VOC options
VOCinit;
% train and test detector
cls='person';
detector=train(VOCopts,cls); % train detector
test(VOCopts,cls,detector); ... |
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