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github | BigRedT/RGBD_Segmentation-master | get_GMM.m | .m | RGBD_Segmentation-master/code/third_party/GMM/GMM-HMRF_v1/GMM-HMRF_v1.1/code/three-dimensional/get_GMM.m | 392 | utf_8 | 7f9ed5a17f7c236229aaaa71ed63799d | %% Fitting Gaussian mixture model to data
% Copyright by Quan Wang, 2012/12/16
% Please cite: Quan Wang. GMM-Based Hidden Markov Random Field for
% Color Image and 3D Volume Segmentation. arXiv:1212.4527 [cs.CV], 2012.
function GMM=get_GMM(X,Y,g)
k=max(X(:));
GMM=cell(k,1);
for i=1:k
index=(... |
github | BigRedT/RGBD_Segmentation-master | MRF_MAP.m | .m | RGBD_Segmentation-master/code/third_party/GMM/GMM-HMRF_v1/GMM-HMRF_v1.1/code/color-image/MRF_MAP.m | 2,273 | utf_8 | 1ceed8b5a1f6f7a73d0750f23b02ee0d | %% The MAP algorithm
%---input---------------------------------------------------------
% X: initial 2D labels
% Y: 2D image
% GMM: Gaussian mixture model parameters
% k: number of labels
% g: number of components of each GMM
% MAP_iter: maximum number of iterations of the MAP algorithm
% show_pl... |
github | BigRedT/RGBD_Segmentation-master | HMRF_EM.m | .m | RGBD_Segmentation-master/code/third_party/GMM/GMM-HMRF_v1/GMM-HMRF_v1.1/code/color-image/HMRF_EM.m | 1,251 | utf_8 | 7d6d6d13eefc1a26dfd6ba05c4b048d0 | %% The EM algorithm
%---input---------------------------------------------------------
% X: initial 2D labels
% Y: image
% GMM: Gaussian mixture model parameters
% k: number of labels
% g: number of components of each GMM
% EM_iter: maximum number of iterations of the EM algorithm
% MAP_iter: max... |
github | BigRedT/RGBD_Segmentation-master | image_kmeans.m | .m | RGBD_Segmentation-master/code/third_party/GMM/GMM-HMRF_v1/GMM-HMRF_v1.1/code/color-image/image_kmeans.m | 452 | utf_8 | ab466d49f2da4075818179d2d7e7737e | %% kmeans algorithm for an image
%---input---------------------------------------------------------
% Y: 2D image
% k: number of clusters
% g: number of GMM components
%---output--------------------------------------------------------
% X: 2D labels
% GMM: Gaussian mixture model parameters
function... |
github | BigRedT/RGBD_Segmentation-master | ind2ij.m | .m | RGBD_Segmentation-master/code/third_party/GMM/GMM-HMRF_v1/GMM-HMRF_v1.1/code/color-image/ind2ij.m | 174 | utf_8 | 199daa095f3d2010f0cf4450a54b6367 | %% index to i and j conversion
% ind: index
% m: height of image
% i, j: image coordinates
function [i j]=ind2ij(ind,m)
i=mod(ind-1,m)+1;
j=floor((ind-1)/m)+1; |
github | BigRedT/RGBD_Segmentation-master | gaussianBlur.m | .m | RGBD_Segmentation-master/code/third_party/GMM/GMM-HMRF_v1/GMM-HMRF_v1.1/code/color-image/gaussianBlur.m | 441 | utf_8 | 9f39149506db35a6a07c179e9c82bede | % Copyright (C) 2012 Quan Wang <wangq10@rpi.edu>,
% Signal Analysis and Machine Perception Laboratory,
% Department of Electrical, Computer, and Systems Engineering,
% Rensselaer Polytechnic Institute, Troy, NY 12180, USA
function GI=gaussianBlur(I,s)
%% perform Gaussian blur
% I: input image
% s: stan... |
github | BigRedT/RGBD_Segmentation-master | get_GMM.m | .m | RGBD_Segmentation-master/code/third_party/GMM/GMM-HMRF_v1/GMM-HMRF_v1.1/code/color-image/get_GMM.m | 481 | utf_8 | 540a5a92625c7b6c8c9f8ab7bd81d303 | %% Fitting Gaussian mixture model to data
% Copyright by Quan Wang, 2012/12/16
% Please cite: Quan Wang. GMM-Based Hidden Markov Random Field for
% Color Image and 3D Volume Segmentation. arXiv:1212.4527 [cs.CV], 2012.
function GMM=get_GMM(X,Y,g)
k=max(X(:));
GMM=cell(k,1);
for i=1:k
index=(... |
github | BigRedT/RGBD_Segmentation-master | imagesAlign.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | opticalFlow.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/videos/opticalFlow.m | 7,361 | utf_8 | b97e8c1f623eca07c6f1a0fff26d171e | 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 | BigRedT/RGBD_Segmentation-master | seqWriterPlugin.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | kernelTracker.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | seqIo.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | seqReaderPlugin.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | pcaApply.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | forestTrain.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | fernsRegTrain.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | rbfDemo.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | pdist2.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | pca.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | kmeans2.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | acfModify.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | acfDetect.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | bbGt.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | bbApply.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/detector/bbApply.m | 21,195 | utf_8 | cc9744e55c6b8442486ba7f71e3f84ce | 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 | BigRedT/RGBD_Segmentation-master | imwrite2.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | convnFast.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | imMlGauss.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | montage2.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | jitterImage.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | movieToImages.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | toolboxUpdateHeader.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | toolboxGenDoc.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | toolboxHeader.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | mdot.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | m2html.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | doxysearch.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | doxywrite.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | doxyread.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | imwrite2split.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | playmovies.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | pca_apply_large.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | montages2.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | filter_gauss_1D.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | clfEcoc.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | getargs.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | normxcorrn_fg.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | makemovie.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | localsum_block.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | imrotate2.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | imSubsResize.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | imtranslate.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | randperm2.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | apply_homography.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | pca_apply.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | mode2.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | savefig.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | dirSynch.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | plotRoc.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | simpleCache.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | tpsInterpolate.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | checkNumArgs.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | fevalDistr.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | medfilt1m.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | BigRedT/RGBD_Segmentation-master | FbMake.m | .m | RGBD_Segmentation-master/code/third_party/toolbox/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 | mlubin/JuMPSupplement-master | doyalmip.m | .m | JuMPSupplement-master/clnlbeam/doyalmip.m | 548 | utf_8 | 131635d6cef99aca6114c0fd633ec956 |
function [] = doyalmip()
runinstance('clnlbeam',5000);
runinstance('clnlbeam',50000);
runinstance('clnlbeam',500000);
end
function [] = runinstance(name,N)
modelrep = 1;
% take mimimum over repetitions to decrease variability
buildtime = Inf;
for k = 1:modelrep
yalmip('clear')
tic
model = eval(sprin... |
github | geospace-code/matlab-rinex-reader-main | bessel_2.m | .m | matlab-rinex-reader-main/easy9/bessel_2.m | 4,047 | utf_8 | 71195c3449b35b2220699e18a921b028 | function [s12,A1,A2] = bessel_2(phi1d,phi1m,phi1s,l1d,l1m,...
l1s,phi2d,phi2m,phi2s,l2d,l2m,l2s,a,finv)
%BESSEL_2 Solution of the inverse geodetic problem according to
% the Bessel-Helmert method as described in Zhan Xue-Lian.
% Given two points with coordinates (phi1, l1) an... |
github | geospace-code/matlab-rinex-reader-main | bessel_1.m | .m | matlab-rinex-reader-main/easy9/bessel_1.m | 4,251 | utf_8 | c07fa2563f041531662bb8bfbe66c2e0 | function bessel_1(phi1d,phi1m,phi1s,l1d,l1m,...
l1s,A1d,A1m,A1s,s12,a,finv)
%BESSEL_1 Solution of the direct geodetic problem according to
% the Bessel-Helmert method as described in Zhang Xue-Lian.
% Given a point with coordinates (phi1, l1) and
% a ged... |
github | persistforever/WiFi-movement-identification-master | get_mimo2_SNRs.m | .m | WiFi-movement-identification-master/WiFi_MI/get_mimo2_SNRs.m | 1,509 | utf_8 | 649590ba3386a716f66da85cba2c1b35 | %GET_MIMO2_SNRS Calculates the MIMO2 SNRs for a scaled CSI matrix.
% Note that the matrix is expected to have dimensions M x N x S, where
% M = # TX antennas
% N = # RX antennas
% S = # subcarriers
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>,
% Wenjun Hu
%
function ret ... |
github | persistforever/WiFi-movement-identification-master | qpsk_berinv.m | .m | WiFi-movement-identification-master/WiFi_MI/qpsk_berinv.m | 129 | utf_8 | 75022668a7a4e5ffe0506f45935105c2 | %
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = qpsk_berinv(ber)
ret = qfuncinv(ber).^2;
end
|
github | persistforever/WiFi-movement-identification-master | get_mimo3_SNRs_sm.m | .m | WiFi-movement-identification-master/WiFi_MI/get_mimo3_SNRs_sm.m | 1,508 | utf_8 | cae86bfe9d1535939efcab57fe9ff4d0 | %GET_MIMO3_SNRS_SM Calculates the MIMO3 SNRs for a scaled CSI matrix.
% Note that the matrix is expected to have dimensions M x N x S, where
% M = # TX antennas
% N = # RX antennas
% S = # subcarriers
% This version takes into account the spatial mapping performed by Intel NICs.
%
% (c) 2008-2011 Dan... |
github | persistforever/WiFi-movement-identification-master | get_simo_SNRs.m | .m | WiFi-movement-identification-master/WiFi_MI/get_simo_SNRs.m | 498 | utf_8 | 2ff206a1014335910405eaedc9b0fa92 | %GET_SIMO_SNRS Calculates the SIMO SNRs for a scaled CSI matrix.
% Note that the matrix is expected to have dimensions M x N x S, where
% M = # TX antennas
% N = # RX antennas
% S = # subcarriers
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>,
% Wenjun Hu
%
function ret = ... |
github | persistforever/WiFi-movement-identification-master | qam64_berinv.m | .m | WiFi-movement-identification-master/WiFi_MI/qam64_berinv.m | 138 | utf_8 | 12cac815747d7c959fe374a15b55a8a7 | %
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = qam64_berinv(ber)
ret = qfuncinv(12/7*ber).^2*21;
end
|
github | persistforever/WiFi-movement-identification-master | get_eff_SNRs_sm.m | .m | WiFi-movement-identification-master/WiFi_MI/get_eff_SNRs_sm.m | 2,715 | utf_8 | 22a6d57007d07bdd2c5a55391ae680dd | %GET_EFF_SNRS_SM Compute the effective SNR values from a CSI matrix
% Note that the matrix is expected to have dimensions M x N x S, where
% M = # TX antennas
% N = # RX antennas
% S = # subcarriers
% This version takes into account the spatial mapping performed by Intel NICs.
%
% (c) 2008-2011 Danie... |
github | persistforever/WiFi-movement-identification-master | get_total_rss.m | .m | WiFi-movement-identification-master/WiFi_MI/get_total_rss.m | 592 | utf_8 | 9f75c5f068248c64a969d4b65b5aa054 | %GET_TOTAL_RSS Calculates the Received Signal Strength (RSS) in dBm from
% a CSI struct.
%
% (c) 2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_total_rss(csi_st)
error(nargchk(1,1,nargin));
% Careful here: rssis could be zero
rssi_mag = 0;
if csi_st.rssi_a ~= 0
rssi_mag ... |
github | persistforever/WiFi-movement-identification-master | bpsk_berinv.m | .m | WiFi-movement-identification-master/WiFi_MI/bpsk_berinv.m | 133 | utf_8 | a52e2bcaf9cc5c31be0f0361dcf163e1 | %
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = bpsk_berinv(ber)
ret = qfuncinv(ber).^2 / 2;
end
|
github | persistforever/WiFi-movement-identification-master | get_eff_SNRs.m | .m | WiFi-movement-identification-master/WiFi_MI/get_eff_SNRs.m | 2,622 | utf_8 | ed7119854587f1abc99cab9b0ecd1312 | %GET_EFF_SNRS Compute the effective SNR values from a CSI matrix
% Note that the matrix is expected to have dimensions M x N x S, where
% M = # TX antennas
% N = # RX antennas
% S = # subcarriers
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>,
% Wenjun Hu
%
function ret=ge... |
github | persistforever/WiFi-movement-identification-master | qam64_ber.m | .m | WiFi-movement-identification-master/WiFi_MI/qam64_ber.m | 135 | utf_8 | b69e4178b8b5fb1814bbd08da448dde6 | %
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = qam64_ber(snr)
ret = 7/12*qfunc(sqrt(snr/21));
end
|
github | persistforever/WiFi-movement-identification-master | qam16_ber.m | .m | WiFi-movement-identification-master/WiFi_MI/qam16_ber.m | 133 | utf_8 | 0ca32fad3bc7762e1f60518d4c42d111 | %
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = qam16_ber(snr)
ret = 3/4*qfunc(sqrt(snr/5));
end
|
github | persistforever/WiFi-movement-identification-master | bpsk_ber.m | .m | WiFi-movement-identification-master/WiFi_MI/bpsk_ber.m | 128 | utf_8 | a9a523f580c2a2e545779d4ee0730a8c | %
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = bpsk_ber(snr)
ret = qfunc(sqrt(2*snr));
end
|
github | persistforever/WiFi-movement-identification-master | get_mimo2_SNRs_sm.m | .m | WiFi-movement-identification-master/WiFi_MI/get_mimo2_SNRs_sm.m | 1,742 | utf_8 | 670dbae461744cd76fb31583093bf395 | %GET_MIMO2_SNRS_SM Calculates the MIMO2 SNRs for a scaled CSI matrix.
% Note that the matrix is expected to have dimensions M x N x S, where
% M = # TX antennas
% N = # RX antennas
% S = # subcarriers
% This version takes into account the spatial mapping performed by Intel NICs.
%
% (c) 2008-2011 Dan... |
github | persistforever/WiFi-movement-identification-master | get_scaled_csi_sm.m | .m | WiFi-movement-identification-master/WiFi_MI/get_scaled_csi_sm.m | 458 | utf_8 | 43b9372e37fe6ec000a7de7a82c382e2 | %GET_SCALED_CSI_SM Converts a CSI struct to a channel matrix H.
% This version undoes Intel's spatial mapping to return the pure
% MIMO channel matrix H.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_scaled_csi_sm(csi_st)
% Normal procedure to scale normalized CSI to H
ret ... |
github | persistforever/WiFi-movement-identification-master | read_bf_file.m | .m | WiFi-movement-identification-master/WiFi_MI/read_bf_file.m | 2,577 | utf_8 | 3046107c2e85bb02155fda059099b086 | %READ_BF_FILE Reads in a file of beamforming feedback logs.
% This version uses the *C* version of read_bfee, compiled with
% MATLAB's MEX utility.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = read_bf_file(filename)
%% Input check
error(nargchk(1,1,nargin));
%% Open file
f = fope... |
github | persistforever/WiFi-movement-identification-master | qam16_berinv.m | .m | WiFi-movement-identification-master/WiFi_MI/qam16_berinv.m | 140 | utf_8 | d24a4e3740dae8efec18fc1e13a88f4a | %
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = qam16_berinv(ber)
ret = qfuncinv(ber * 4/3).^2 * 5;
end
|
github | persistforever/WiFi-movement-identification-master | get_mimo3_SNRs.m | .m | WiFi-movement-identification-master/WiFi_MI/get_mimo3_SNRs.m | 1,283 | utf_8 | 4f2af7a660d90257d179154ba23a715f | %GET_MIMO3_SNRS Calculates the MIMO3 SNRs for a scaled CSI matrix.
% Note that the matrix is expected to have dimensions M x N x S, where
% M = # TX antennas
% N = # RX antennas
% S = # subcarriers
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>,
% Wenjun Hu
%
function ret ... |
github | persistforever/WiFi-movement-identification-master | dbinv.m | .m | WiFi-movement-identification-master/WiFi_MI/dbinv.m | 145 | utf_8 | f3e0b99630ef3ad7fcc8ca3ac3daade3 | %DBINV Convert from decibels.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = dbinv(x)
ret = 10.^(x/10);
end
|
github | persistforever/WiFi-movement-identification-master | qpsk_ber.m | .m | WiFi-movement-identification-master/WiFi_MI/qpsk_ber.m | 126 | utf_8 | b827a22d46e1ba9500f8b4837858f6a3 | %
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = qpsk_ber(snr)
ret = qfunc(sqrt(snr));
end
|
github | persistforever/WiFi-movement-identification-master | LDA.m | .m | WiFi-movement-identification-master/WiFi_MI/Experiment/LDA.m | 8,401 | utf_8 | ccd473de34e37ad84e802ebd3f5632f8 | function [eigvector, eigvalue] = LDA(gnd,options,data)
% LDA: Linear Discriminant Analysis
%
% [eigvector, eigvalue] = LDA(gnd, options, data)
%
% Input:
% data - Data matrix. Each row vector of fea is a data point.
% gnd - Colunm vector of the label information for ea... |
github | persistforever/WiFi-movement-identification-master | get_scaled_csi.m | .m | WiFi-movement-identification-master/WiFi_MI/Experiment/get_scaled_csi.m | 1,842 | utf_8 | 25f6ee30c68e10fbfaaeff35624ab758 | %GET_SCALED_CSI Converts a CSI struct to a channel matrix H.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_scaled_csi(csi_st)
% Pull out CSI
csi = csi_st.csi;
% Calculate the scale factor between normalized CSI and RSSI (mW)
csi_sq = csi .* conj(csi);
csi_pwr =... |
github | persistforever/WiFi-movement-identification-master | get_total_rss.m | .m | WiFi-movement-identification-master/WiFi_MI/Experiment/get_total_rss.m | 592 | utf_8 | 9f75c5f068248c64a969d4b65b5aa054 | %GET_TOTAL_RSS Calculates the Received Signal Strength (RSS) in dBm from
% a CSI struct.
%
% (c) 2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_total_rss(csi_st)
error(nargchk(1,1,nargin));
% Careful here: rssis could be zero
rssi_mag = 0;
if csi_st.rssi_a ~= 0
rssi_mag ... |
github | persistforever/WiFi-movement-identification-master | get_scaled_csi_sm.m | .m | WiFi-movement-identification-master/WiFi_MI/Experiment/get_scaled_csi_sm.m | 458 | utf_8 | 43b9372e37fe6ec000a7de7a82c382e2 | %GET_SCALED_CSI_SM Converts a CSI struct to a channel matrix H.
% This version undoes Intel's spatial mapping to return the pure
% MIMO channel matrix H.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_scaled_csi_sm(csi_st)
% Normal procedure to scale normalized CSI to H
ret ... |
github | persistforever/WiFi-movement-identification-master | attributes_extraction.m | .m | WiFi-movement-identification-master/WiFi_MI/Experiment/attributes_extraction.m | 3,893 | utf_8 | a809c2ee285da299d988211083c24940 | function trainData = attributes_extraction(y)
% extract attributes from y
% input : y - sequence of dataSet
% output : etp - std value
% -------------------------------------------------------------------------
n = 8 ; % number of attributes
trainData = zeros(1, n) ; % training dataset
trainData(1,1) =... |
github | persistforever/WiFi-movement-identification-master | plotTimeGraph.m | .m | WiFi-movement-identification-master/WiFi_MI/Experiment/plotTimeGraph.m | 2,137 | utf_8 | 6d9c4c80f4cbf68eed58b2434409585c | function dataSet = plotTimeGraph(csi_trace)
% plot time-CSI graph
% input : csi_trace - csi infomation(n*3)
% output : dataSet - new csi_trace
% -----------------------------------------------------------------------
m = size(csi_trace, 1);
dataSet = zeros(m, 3) ;
for k=1:m
t = get_scaled_cs... |
github | persistforever/WiFi-movement-identification-master | dbinv.m | .m | WiFi-movement-identification-master/WiFi_MI/Experiment/dbinv.m | 145 | utf_8 | f3e0b99630ef3ad7fcc8ca3ac3daade3 | %DBINV Convert from decibels.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = dbinv(x)
ret = 10.^(x/10);
end
|
github | persistforever/WiFi-movement-identification-master | feature_selection.m | .m | WiFi-movement-identification-master/WiFi_MI/Experiment/feature_selection.m | 3,395 | utf_8 | a3e0964a1e2c33cde2a2d8e4fda01d59 | function [fea_col, wrong_rate] = feature_selection(trainData, trainLabel, trainer)
% use different algorithms to select optimal features
% input : svm_trainer - svm trainer for each class
% testData - attributes set of examples
% trainer - which classfier to use(1-SVM, 2-LDA)
% output : fea_c... |
github | persistforever/WiFi-movement-identification-master | get_total_rss.m | .m | WiFi-movement-identification-master/WiFi_MI/Experiment/pattern_extraction_code/get_total_rss.m | 592 | utf_8 | 9f75c5f068248c64a969d4b65b5aa054 | %GET_TOTAL_RSS Calculates the Received Signal Strength (RSS) in dBm from
% a CSI struct.
%
% (c) 2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_total_rss(csi_st)
error(nargchk(1,1,nargin));
% Careful here: rssis could be zero
rssi_mag = 0;
if csi_st.rssi_a ~= 0
rssi_mag ... |
github | Spstolar/Network-Estimates-master | interactive3layer.m | .m | Network-Estimates-master/interactive3layer.m | 7,174 | utf_8 | 350d268b5506ab5f0770045fcd8a1c2c | function varargout = interactive3layer(varargin)
% INTERACTIVE3LAYER MATLAB code for interactive3layer.fig
% INTERACTIVE3LAYER, by itself, creates a new INTERACTIVE3LAYER or raises the existing
% singleton*.
%
% H = INTERACTIVE3LAYER returns the handle to a new INTERACTIVE3LAYER or the handle to
% t... |
github | gpeyre/2015-SIGGRAPH-convolutional-ot-master | colorspace.m | .m | 2015-SIGGRAPH-convolutional-ot-master/code/colors_functions/colorspace.m | 16,178 | utf_8 | 2ca0aee9ae4d0f5c12a7028c45ef2b8d | function varargout = colorspace(Conversion,varargin)
%COLORSPACE Transform a color image between color representations.
% B = COLORSPACE(S,A) transforms the color representation of image A
% where S is a string specifying the conversion. The input array A
% should be a real full double array of size Mx3 or MxN... |
github | gpeyre/2015-SIGGRAPH-convolutional-ot-master | plot_tensor_field.m | .m | 2015-SIGGRAPH-convolutional-ot-master/code/toolbox/plot_tensor_field.m | 5,847 | utf_8 | 719112cee4795f6d5720aae22476dc9f | function h = plot_tensor_field(H, M, options)
% plot_tensor_field - display a tensor field
%
% h = plot_tensor_field(H, M, options);
%
% options.sub controls sub-sampling
% options.color controls color
%
% Copyright (c) 2006 Gabriel Peyre
if nargin<3
options.null = 0;
end
if not( isstruct(op... |
github | gpeyre/2015-SIGGRAPH-convolutional-ot-master | load_image.m | .m | 2015-SIGGRAPH-convolutional-ot-master/code/toolbox/load_image.m | 20,071 | utf_8 | 2407ce9458db2bca9c208e744785af5a | function M = load_image(type, n, options)
% load_image - load benchmark images.
%
% M = load_image(name, n, options);
%
% name can be:
% Synthetic images:
% 'chessboard1', 'chessboard', 'square', 'squareregular', 'disk', 'diskregular', 'quaterdisk', '3contours', 'line',
% 'line_vertical', 'l... |
github | gpeyre/2015-SIGGRAPH-convolutional-ot-master | inpolyhedron.m | .m | 2015-SIGGRAPH-convolutional-ot-master/code/toolbox/inpolyhedron.m | 22,756 | utf_8 | 16738ef64a83b8c37c57511248fb93cf | function IN = inpolyhedron(varargin)
%INPOLYHEDRON Tests if points are inside a 3D triangulated (faces/vertices) surface
% BY CONVENTION, SURFACE NORMALS SHOULD POINT OUT from the object. (see
% FLIPNORMALS option below for details)
%
% IN = INPOLYHEDRON(FV,QPTS) tests if the query points (QPTS) are inside ... |
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