plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | casaro/AutoExtend-master | AutoExtend.m | .m | AutoExtend-master/AutoExtend/AutoExtend.m | 17,591 | utf_8 | c5c2ac324d07d6b0dd1eb713417cde1a | function [] = AutoExtend(varargin)
if (nargin == 8)
folder = varargin{1};
normalizeWeights = varargin{2};
sWeight = varargin{3};
lWeight = varargin{4};
rWeight = varargin{5};
nWeight = varargin{6};
experiment = varargin{7};
RelationFiles = varargin{8};
... |
github | casaro/AutoExtend-master | columnNormalize.m | .m | AutoExtend-master/AutoExtend/columnNormalize.m | 525 | utf_8 | 1ec078e1f4f829132d6be4420d6a539d | function [ A ] = columnNormalize( A )
if issparse(A)
A = columnNormalizeSparse( A );
else
A = columnNormalizeFull( A );
end
end
function [ A ] = columnNormalizeSparse( A )
[i,j,values] = find(A);
colSum = (ones(1, size(A,1)) * A)';
values = values ./ colSum(j... |
github | casaro/AutoExtend-master | writeVectors.m | .m | AutoExtend-master/AutoExtend/writeVectors.m | 2,761 | utf_8 | ab60f229d1a280d17d21d16c146f57c3 | function [] = writeVectors(varargin)
folder = varargin{1};
experiment = varargin{2};
writeWords = true;
writeSynsets = true;
writeLexemes = false;
if (nargin == 5)
writeWords = varargin{3};
writeSynsets = varargin{4};
writeLexemes = varargin{5};
end
... |
github | jjaner/essentia-musicbricks-master | DetectPeaks.m | .m | essentia-musicbricks-master/test/src/descriptortests/tuning/DetectPeaks.m | 1,325 | utf_8 | c64072c666d438b9c9c181f2e5aba663 | % Function for peak detection
% Adaptations made to work with HPCP by emilia, 28-03-2007
function [ploc, pval]=DetectPeaks(spectrum, nPeaks)
% function DetectPeaks(spectrum)
% Inputs:
% spectrum: dB spectrum magnitude (abs(fft(signal))
% nPeaks: maximum number of peaks to pick
% Outputs:
% ploc: bin ... |
github | mhrztrk/thesis-src-master | GenerateRoadModel.m | .m | thesis-src-master/utils/GenerateRoadModel.m | 265 | utf_8 | eb76815e014019c8fd3e5a5fdae009b2 |
function model = GenerateRoadModel(model_type, kernel_size, width, angle, branch_width, branch_angle)
% I Model
%
% /
% /
% /
% /
% T model
%
% ----/----
% /
% /
% /
% X model
%
% \ /
% \/
% /\
% / \
end |
github | mhrztrk/thesis-src-master | prefiltering.m | .m | thesis-src-master/utils/prefiltering.m | 591 | utf_8 | fb71ceac857905d0d20df2e20a762d44 |
function [img_out] = prefiltering(img)
img_uint8(:,:,1) = uint8(img(:,:,1) * 255);
img_uint8(:,:,2) = uint8(img(:,:,2) * 255);
img_uint8(:,:,3) = uint8(img(:,:,3) * 255);
%img_uint8(:,:,4) = uint8(img(:,:,4) * 255);
img_ao = imareaopen(img_uint8,100,8);
img_ao_ac = imareaclose(img_ao,100,8);
img_out(:,:... |
github | mhrztrk/thesis-src-master | Draw_BinMask_On_Image.m | .m | thesis-src-master/utils/Draw_BinMask_On_Image.m | 267 | utf_8 | 248c90ba4f940a4d82cac225fb0c429d |
function [MI] = Draw_BinMask_On_Image(I, M)
MI = I(:,:,1:3);
[X Y] = find(M == 1);
MI(sub2ind(size(I), X, Y, 1*ones(size(X)))) = 1;
MI(sub2ind(size(I), X, Y, 2*ones(size(X)))) = 0;
MI(sub2ind(size(I), X, Y, 3*ones(size(X)))) = 0;
end |
github | mhrztrk/thesis-src-master | NetworkQuality.m | .m | thesis-src-master/utils/NetworkQuality.m | 11,133 | utf_8 | 77fc2a5e2f0a37d7e6e548cb4d412f3a | function [completeness correctness rmse] = NetworkQuality(refLine, extLine, labels, img)
% DrawLabelledLineList(img, extLine, labels);
%%
figure;imshow(img,[]); hold on;
matched_reference = cell(1, size(refLine,2));
for j=1:size(refLine,2)
%
% refLine ile extLine'in [x,y] siralamasi farkli,... |
github | mhrztrk/thesis-src-master | LineMinDist.m | .m | thesis-src-master/utils/LineMinDist.m | 11,250 | utf_8 | 0f8db2d1e482e49996f97f74127b3216 |
% state = 0 -> no connection
% state = 1 -> line1 & line2 connected
% state = 2 -> line1 divided
% state = 3 -> line2 divided
% state = 4 -> line1 & line2 connected over another line
function [line1 line2 new_lines state] = LineMinDist(line1,line2)
% parameters
th_dist = 60; % max allowable... |
github | mhrztrk/thesis-src-master | ats_descriptors_mod.m | .m | thesis-src-master/utils/ats_descriptors_mod.m | 2,386 | utf_8 | a6d173b7ee7d1027f0c8b49727e0106d | %
% Automated Road Network Extraction from High Spatial Resolution Multi-Spectral Imagery (Thesis)
% URL:http://www.geomatics.ucalgary.ca/research/publications/GradTheses.html
%
function [mean comp ecc var] = ats_descriptors_mod(I, w, step)
% get image size
[imH imW] = size(I);
% Initialize vari... |
github | mhrztrk/thesis-src-master | ats_descriptors.m | .m | thesis-src-master/utils/ats_descriptors.m | 2,438 | utf_8 | b6fb45c7fdf6a8599329da808f7e3d56 | %
% Automated Road Network Extraction from High Spatial Resolution Multi-Spectral Imagery (Thesis)
% URL:http://www.geomatics.ucalgary.ca/research/publications/GradTheses.html
%
function [mean comp ecc var] = ats_descriptors(I, w, len, step)
% get image size
[imH imW] = size(I);
% Initialize var... |
github | mhrztrk/thesis-src-master | pixel_ats.m | .m | thesis-src-master/utils/pixel_ats.m | 477 | utf_8 | 4b378b4441251bb9a185ec8c76415bd4 | function [A] = pixel_ats(I, p, f, as)
rs_cnt = 180 / as;
A = zeros(1, rs_cnt);
for i=1:rs_cnt
krnl = imrotate(f, i*as,'crop');
A(1, i) = ApplyFilter(I, krnl, p(1), p(2));
end
end
function [fout] = ApplyFilter(img, filter, x, y)
fhsz = uint16((size(fi... |
github | mhrztrk/thesis-src-master | colorspace.m | .m | thesis-src-master/filtering/bilateral/colorspace.m | 14,019 | utf_8 | c8d23ed54d9745d5e49e7d99ee1ed95f | function varargout = colorspace(Conversion,varargin)
%COLORSPACE Convert a color image between color representations.
% B = COLORSPACE(S,A) converts the color representation of image A
% where S is a string specifying the conversion. S tells the
% source and destination color spaces, S = 'dest<-src', or
% ... |
github | mhrztrk/thesis-src-master | bfilter2.m | .m | thesis-src-master/filtering/bilateral/bfilter2.m | 4,845 | utf_8 | 3be3db92b7e341f01928e314fd5adffd |
%% BFILTER2 Two dimensional bilateral filtering.
% This function implements 2-D bilateral filtering using
% the method outlined in:
%
% C. Tomasi and R. Manduchi. Bilateral Filtering for
% Gray and Color Images. In Proceedings of the IEEE
% International Conference on Computer Vision, ... |
github | mhrztrk/thesis-src-master | RoadTemplateMatchingFilterEx.m | .m | thesis-src-master/filtering/tmfilter/RoadTemplateMatchingFilterEx.m | 2,513 | utf_8 | d1018a549c15763265cc7f694e2cdf2c | %
% I : binary or gray-level image
% N : iteration count
%
%
function [result] = RoadTemplateMatchingFilterEx(I, N, angle_step, road_range, show_results)
ws = 3 * road_range(2);
fb = gen_filter_bank_int2(ws, road_range(1), road_range(2));
Isc = mat2gray(I);
figure;imshow(Isc,[])... |
github | mhrztrk/thesis-src-master | gen_filter_bank.m | .m | thesis-src-master/filtering/tmfilter/gen_filter_bank.m | 537 | utf_8 | 0fead51b3c22a09b1deb2cd00c9a2ce3 | %
% ws - window size
% min_rw - minimum road width
% max_rw - maximum road width
%
% both must be odd number & ws > 3*max_rw
%
function fb = gen_filter_bank(ws, min_rw, max_rw)
fb = zeros(ws, ws, ((max_rw-min_rw)/2+1));
gf = fspecial('gaussian', [ws ws], ws/3);
for i=min_rw:2:max_rw
... |
github | mhrztrk/thesis-src-master | RoadTemplateMatchingFilterEx2.m | .m | thesis-src-master/filtering/tmfilter/RoadTemplateMatchingFilterEx2.m | 4,943 | utf_8 | 7f7725d8840efd8ff3fd6626d3df89dd | %
% I : binary or gray-level image
% N : iteration count
%
% road_range : [min_road_width max_road_width] (value of min & max must be be odd numbers )
% show_results : 0 -> display nothing
% 1 -> display result of each iteration
% 2 -> save each result of each iteration
%
function [result] ... |
github | mhrztrk/thesis-src-master | PeakCount.m | .m | thesis-src-master/filtering/tmfilter/PeakCount.m | 1,306 | utf_8 | 8198cd7705b5d7fe73f8b4e4d2b9fade | function [pcmap] = PeakCount(I, Template, Th)
[H W] = size(I);
tw = size(Template,1);
angle_step = 15;
rot_step_cnt = 360 / angle_step;
pcmap = zeros(H,W);
kernel = zeros(tw,tw,rot_step_cnt);
for i=1:rot_step_cnt
kernel(:,:,i) = imrotate(Template,i*angle_... |
github | mhrztrk/thesis-src-master | imclassify.m | .m | thesis-src-master/classification/imclassify.m | 8,837 | utf_8 | 0cf123798bc0496b7460b194f179de8d | %
% I : input image
% T : mask
% method :
%
function [clres qmetrics] = imclassify(I, M, ref, method, param, range, imgName, baseDir)
% image size
[imH imW imD] = size(I);
X = reshape(I, imH*imW, imD); % image pixels in vector form
% transformation
% X = transform_to_sth... |
github | mhrztrk/thesis-src-master | gmm_classification.m | .m | thesis-src-master/classification/training/gmm_classification.m | 8,775 | utf_8 | 15732fe80cbd557269d0d8ab25878f7d | function [Rmin, qmetrics] = gmm_classification(img, mask, ref, imgName, baseDir)
%% image size
[imH imW imD] = size(img);
X = reshape(img, imH*imW, imD); % image pixels in vector form
Y = X((reshape(mask, imH*imW, 1) == 1),:); % training set
%%
% minimum capture percentage, i.e at least climit p... |
github | mhrztrk/thesis-src-master | ocsvm_classification.m | .m | thesis-src-master/classification/training/ocsvm_classification.m | 6,018 | utf_8 | d7855446583f0d65114fe99f44534899 | function [Rmin, qmetrics] = ocsvm_classification(img, mask, ref, imgName, baseDir)
groups = ones(size(Y,1), 1);
nSample = size(groups,1);
% cross-validation
bestnu = param(1); %% nu is critical, change if feature set is changed.
bestg = 16;
bestcv = 0;
i = 1;
for log2g = -1:10
q... |
github | mhrztrk/thesis-src-master | impsi.m | .m | thesis-src-master/classification/features/other/impsi.m | 1,203 | utf_8 | 04b1e2782c2f15a0940b8167bc601f71 | %
% Pixel Shape Index
%
function [psi] = impsi(im,th)
% Spectral homogenity threshold.
T1 = th;
% Total # of pixels threshold in the direction line.
T2 = 15;
% Image size
[imH imW imS] = size(im);
% Angle step between direction lines.
ang_step = 30;
step... |
github | mhrztrk/thesis-src-master | ace.m | .m | thesis-src-master/ace/ace.m | 4,632 | utf_8 | e62ea5f779124497a81968f230d2d5a3 | function [ Iout ] = ace( img, min_dist, max_dist, min_defl, cc_th, display, img_name)
%
% Anti-parallel-edge Centerline Extraction (ACE) Algorithm
%
img = mat2gray(img);
% Image size
[imH imW] = size(img);
% find canny edges
% BW = edge(I,'canny',thresh,sigma)
CannyEdge = edge... |
github | mhrztrk/thesis-src-master | ace2.m | .m | thesis-src-master/ace/ace2.m | 5,398 | utf_8 | abe471b49086d733da8ebcf46eebe5b2 | function [ Iout ] = ace2( img, min_dist, max_dist, min_defl, cc_th, display, img_name)
%
% Anti-parallel-edge Centerline Extraction (ACE) Algorithm
%
img = mat2gray(img);
% Image size
[imH imW] = size(img);
% find canny edges
% BW = edge(I,'canny',thresh,sigma)
CannyEdge = edg... |
github | mhrztrk/thesis-src-master | SormCliquePotential2.m | .m | thesis-src-master/sorm/SormCliquePotential2.m | 4,697 | utf_8 | 87113668b5c8e0f1f970d60edfee63a0 | function [V] = SormCliquePotential2(linelist, labels, Clique, mrf)
% find lines labelled as road
plist = Clique((labels(Clique)==1));
Vmin = -1 * (-mrf.params.Kl*2 + mrf.params.Kc*sind(180));
switch(size(plist,2))
case 0
V = 0;
case 1 % only one connectio... |
github | mhrztrk/thesis-src-master | sormc.m | .m | thesis-src-master/sorm/sormc.m | 23,584 | utf_8 | 76c062954fc469a6696e32aa4bc632bc | %
% Applies Self-Organizing Road Map Algorithm
%
% I: Binary Image, image pixels with value 1 indicate possible
% road pixels.
%
% d: grid spacing
%
%
function [CC, ST, pred] = sormc(I, d)
global dist;
dist = d;
min_dist = 11; % minimum distance betw... |
github | mhrztrk/thesis-src-master | sorm.m | .m | thesis-src-master/sorm/sorm.m | 23,581 | utf_8 | ab742479335db67b5cc71a0239b0f32b | %
% Applies Self-Organizing Road Map Algorithm
%
% I: Binary Image, image pixels with value 1 indicate possible
% road pixels.
%
% d: grid spacing
%
% minDist : minimum distance between the nodes
% minSampleCnt : minimum number of samples within cluster
%
func... |
github | mhrztrk/thesis-src-master | kMeansCluster.m | .m | thesis-src-master/sorm/kMeansCluster.m | 5,484 | utf_8 | 8e28cf7b738ad749c74733d6f2aec745 | function c=kMeansCluster(m,k,isRand)
%%%%%%%%%%%%%%%%
%
% kMeansCluster - Simple k means clustering algorithm
% Author: Kardi Teknomo, Ph.D. ... |
github | mhrztrk/thesis-src-master | SORMNetworkConstruct.m | .m | thesis-src-master/sorm/SORMNetworkConstruct.m | 6,233 | utf_8 | ea91b6eba23e7085da2b11b2291ed123 | function [linelist, Clqs] = SORMNetworkConstruct(CC, CM, pmap, mxDist, minProb)
ang_th = 16;
Adj = zeros(size(CC,1));
for i=1:size(CC,1)
% get points close to point i
D = sqrt((CC(:,1)-CC(i,1)).^2 + (CC(:,2)-CC(i,2)).^2);
clsPts = find(D < mxDist);
... |
github | mhrztrk/thesis-src-master | SormCliquePotential.m | .m | thesis-src-master/sorm/SormCliquePotential.m | 4,439 | utf_8 | ece89dd038429df64e555ae5dd7b32f4 | function [V] = SormCliquePotential(linelist, labels, Clique, mrf)
% find lines labelled as road
plist = Clique((labels(Clique)==1));
switch(size(plist,2))
case 0
V = mrf.params.Vo;
case 1 % only one connection labelled as road
V = mrf.... |
github | mhrztrk/thesis-src-master | kMediansCluster.m | .m | thesis-src-master/sorm/kMediansCluster.m | 5,508 | utf_8 | 1e2e9c7343fd603c29a91a23674c09e3 | function y=kMediansCluster(m,k,isRand)
%%%%%%%%%%%%%%%%
%
% kMeansCluster - Simple k means clustering algorithm
% Author: Kardi Teknomo, Ph.D. ... |
github | mhrztrk/thesis-src-master | SAwithGibbsSampler2.m | .m | thesis-src-master/mrf/src/SAwithGibbsSampler2.m | 3,235 | utf_8 | cfb6162e09a27a123eeef5e90c639d8d | %%
function [labels_GlbMin] = SAwithGibbsSampler2(linelist, iLabels, nstep, nlevel, iTemp, mrf, Cliques)
E_new = Inf;
E_prv = Inf;
E_GlbMin = Inf;
deltaE = Inf;
T = iTemp;
c = 0.5;
K = 1;
labels = iLabels;
labels_GlbMin = iLabels;
nclq = size(Cli... |
github | mhrztrk/thesis-src-master | MRFRelaxWithICM.m | .m | thesis-src-master/mrf/src/MRFRelaxWithICM.m | 1,851 | utf_8 | 8d7d1174b3779b6ede7b4d56f6abcf77 | function [labels] = MRFRelaxWithICM(linelist, iLabels, nstep, mrf, Cliques)
mrf.CliqPotMin = -1 * (-mrf.params.Kl*2 + mrf.params.Kc*sind(180));
for i=1:size(linelist,2)
linelist(i).SegPot(1) = (-0.4587 + linelist(i).prob);
if(mrf.params.allowjunc == 0)
... |
github | mhrztrk/thesis-src-master | DrawLabelledLineList.m | .m | thesis-src-master/mrf/src/DrawLabelledLineList.m | 929 | utf_8 | 012771cc42bd0218e00436f185d04cab | % DrawLabelledLineList
% DrawLabelledLineList(img,linelist,labels)
%
%
function [] = DrawLabelledLineList(varargin)
if (nargin <= 1)
error('Invalid argument!');
else
img = varargin{1};
linelist = varargin{2};
if( nargin == 2 )
labels = ones(size(lin... |
github | mhrztrk/thesis-src-master | CalculateEnergyLL.m | .m | thesis-src-master/mrf/src/CalculateEnergyLL.m | 395 | utf_8 | 87547366f8302d896634866f56ff2b1a |
% linelist: list of all detected and derived lines
% labels: Link labels (mx1)([0,1])
% clique: Clique Set
%
function E = CalculateEnergyLL(linelist, labels, clique, mrf)
Vtot = mrf.params.eratio * SegmentPotentialsLL(linelist, labels, mrf); % V(d_i|l_i)
Vc = CliquePotentialsLL(linelist,... |
github | mhrztrk/thesis-src-master | maximalCliques.m | .m | thesis-src-master/mrf/src/maximalCliques.m | 9,171 | utf_8 | 6c9dda045a5d5ca62ce9aa361cb406e1 | function maximalcliques_subgraphs = maximalCliques( X, MAX_CLIQUE_SIZE )
%MAXIMALCLIQUES finds all the maximal complete sub-graphs in a graph
% @args:
% X: The upper triangular adjacency graph
% MAX_CLIQUE_SIZE: (Optional) gives the maximum size of the maximal
% cliques wanted
%
% The grap... |
github | mhrztrk/thesis-src-master | AddLinesToLineList.m | .m | thesis-src-master/mrf/src/AddLinesToLineList.m | 4,769 | utf_8 | 9083f1b23e953775b7711bf2f535d4f0 | %
function [linelist_ex, labels_ex] = AddLinesToLineList(new_lines, linelist, labels, CC, img)
% first find the new one
node_new = zeros(size(new_lines,2),2);
node_exist = node_new;
% linelist_ex -> new created lines
% linelist_label_change
labels_ex = labels;
linelist_ex =... |
github | mhrztrk/thesis-src-master | ConnectTerminationPoints.m | .m | thesis-src-master/mrf/src/ConnectTerminationPoints.m | 8,275 | utf_8 | 75cfcdb4d0ecca9681f6f50cf6824ce5 | function [LabelMinEx, LineSetEx, CliqSetEx] = ConnectTerminationPoints(LineSet, CliqSet, LabelMin, mrf, centroids, pmap)
LineSetEx = LineSet;
CliqSetEx = CliqSet;
LabelMinEx = LabelMin;
mrf.params.allowjunc = 1;
extConn = [];
nconn = 0;
mxDist = 45;
for i=1:size(C... |
github | mhrztrk/thesis-src-master | KovesiLineModel2MRFNetwork.m | .m | thesis-src-master/mrf/src/KovesiLineModel2MRFNetwork.m | 7,761 | utf_8 | 28a2337bd51fa4d8750e8f91f8810a6b | function [linelist, Cliques, CC] = KovesiLineModel2MRFNetwork(rdclseglist, pmap, mxDist, minProb)
k = 0;
CC = [];
CC_isendp = [];
for j=1:size(rdclseglist,2)
for m=1:size(rdclseglist{j},1)
k = k + 1;
CC(k,:) = rdclseglist{j}(m,:)... |
github | mhrztrk/thesis-src-master | CalculateEnergy.m | .m | thesis-src-master/mrf/src/CalculateEnergy.m | 309 | utf_8 | 7ef8b75ed06ba2b0347be17b721e16af |
% S: Segments
% L: Link labels (mx1)([0,1])
% C: Clique Set
% O: Observations (mx1)([0 1])
%
function E = CalculateEnergy(S, L, C, O, mrf)
V = SegmentPotentials(L, O, mrf); % V(d_i|l_i)
Vc = CliquePotentials(S, L, C, mrf); % V_c(l)
E = sum(V) + sum(Vc);
end |
github | mhrztrk/thesis-src-master | DrawResultingNetwork.m | .m | thesis-src-master/mrf/src/DrawResultingNetwork.m | 13,399 | utf_8 | 4bdc91b09eb325393d62a485be551f1a | function [completeness correctness rmse] = DrawResultingNetwork(refLine, extLine, labels, img)
% DrawLabelledLineList(img, extLine, labels);
%%
LineWidth = 3.5;
figure;imshow(img,[]); hold on;
matched_reference = cell(1, size(refLine,2));
for j=1:size(refLine,2)
%
% refLine ile extLi... |
github | mhrztrk/thesis-src-master | SAwithGibbsSamplerEx.m | .m | thesis-src-master/mrf/src/SAwithGibbsSamplerEx.m | 3,695 | utf_8 | 240be0cb3a9247c1a125c13d6ff7db64 | %%
function [labels_GlbMin] = SAwithGibbsSamplerEx(linelist, iLabels, nstep, nlevel, iTemp, mrf, Cliques)
E_new = Inf;
E_prv = Inf;
E_GlbMin = Inf;
deltaE = Inf;
T = iTemp;
c = 0.5;
K = 1;
[Grp] = CreateGroupMatrix(linelist);
nconn = size(linelist, 2); % ... |
github | mhrztrk/thesis-src-master | SAwithGibbsSampler.m | .m | thesis-src-master/mrf/src/SAwithGibbsSampler.m | 4,401 | utf_8 | 011b1a7d514fa1a59e9113882e699f2b | %%
function [labels_GlbMin] = SAwithGibbsSampler(linelist, iLabels, nstep, nlevel, iTemp, mrf, Cliques, save_logs)
if(~exist('save_logs'))
save_logs = 0;
end
mrf.CliqPotMin = -1 * (-mrf.params.Kl*2 + mrf.params.Kc*sind(180));
for i=1:size(linelist,2)
linelist(i... |
github | mhrztrk/thesis-src-master | UpdateLineInfo.m | .m | thesis-src-master/mrf/src/UpdateLineInfo.m | 2,382 | utf_8 | 39f7fb249871bb10063c1a4770e39dd8 | function [linelist] = UpdateLineInfo(linelist, Cliques, pmap)
% calculate edge angles & lengths
for i=1:size(linelist, 2)
% linelist(i).ang = atand((linelist(i).e(2) - linelist(i).s(2))/...
% (linelist(i).e(1) - linelist(i).s(1))); % link orientation
% ... |
github | mhrztrk/thesis-src-master | AddNewLineListEntry.m | .m | thesis-src-master/mrf/src/AddNewLineListEntry.m | 2,463 | utf_8 | 4db8c5ba0098944693b156ceeae0d164 | function [LineSetEx CliqSetEx nconn] = AddNewLineListEntry(LineSet, CliqSet, sp, ep, sCC, eCC, pmap)
LineSetEx = LineSet;
CliqSetEx = CliqSet;
nconn = size(LineSet, 2);
nconn = nconn + 1;
LineSetEx(nconn).s = sp; % link start point
LineSetEx(nconn).e = ep; % link end point
... |
github | mhrztrk/thesis-src-master | pcaApply.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/classify/pcaApply.m | 3,472 | utf_8 | 6c900a4827e7901fb6d4a39bd29456af | 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... |
github | mhrztrk/thesis-src-master | fernsRegTrain.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/classify/fernsRegTrain.m | 6,116 | utf_8 | 1320ee1ee2acb984dce9978544bf368e | 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... |
github | mhrztrk/thesis-src-master | rbfDemo.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/classify/rbfDemo.m | 3,081 | utf_8 | c4eade9c74cfd2c1010c1d31188084f0 | 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
% ... |
github | mhrztrk/thesis-src-master | pdist2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/classify/pdist2.m | 5,396 | utf_8 | 0cec0f1d8510111f8e15afc5c589fc4b | 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)
% i... |
github | mhrztrk/thesis-src-master | kmeans2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/classify/kmeans2.m | 5,412 | utf_8 | d1ccb6e824ed369996a35780a58cefb0 | 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... |
github | mhrztrk/thesis-src-master | imwrite2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/imwrite2.m | 5,302 | utf_8 | 0fe604d2406963a870d7d6dd1736967f | 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, fNa... |
github | mhrztrk/thesis-src-master | convnFast.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/convnFast.m | 8,875 | utf_8 | 256cecc091597975036db98cd97776b6 | 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
... |
github | mhrztrk/thesis-src-master | imMlGauss.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/imMlGauss.m | 5,891 | utf_8 | b9556a21eb938fce1ce42c5f5fca3c57 | 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 ... |
github | mhrztrk/thesis-src-master | optFlowCorr.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/optFlowCorr.m | 5,283 | utf_8 | 37cdb4073aee1ab32d9d3b5e5cd13da1 | function [Vx,Vy,reliab] = optFlowCorr( I1, I2, patchR, searchR, ...
sigma, thr, show )
% Calculate optical flow using cross-correlation.
%
% Calculate optical flow using correlation, followed by lucas & kanade on
% aligned squares for subpixel accuracy. Locally, the closest patch within
% some search radius is... |
github | mhrztrk/thesis-src-master | optFlowHorn.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/optFlowHorn.m | 2,429 | utf_8 | 3c1e2ef9ac6dcb36496491535ee4f278 | function [Vx,Vy] = optFlowHorn( I1, I2, sigma, show )
% Calculate optical flow using Horn & Schunck.
%
% USAGE
% [Vx,Vy] = optFlowHorn( I1, I2, [sigma], [show] )
%
% INPUTS
% I1, I2 - input images to calculate flow between
% sigma - [1] amount to smooth by (may be 0)
% show - [0] figure t... |
github | mhrztrk/thesis-src-master | montage2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/montage2.m | 7,765 | utf_8 | 1e2747571d30ca97f555c85db994944a | 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 = montag... |
github | mhrztrk/thesis-src-master | jitterImage.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/jitterImage.m | 5,445 | utf_8 | 580238bf1cc3061799687a8c0930198f | 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 a... |
github | mhrztrk/thesis-src-master | movieToImages.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/movieToImages.m | 979 | utf_8 | 8b5c10dcbf510082131494773d97bbd4 | 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' ); [... |
github | mhrztrk/thesis-src-master | seqWriterPlugin.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/seqWriterPlugin.m | 8,270 | utf_8 | 0562cef4d2b3d21540316cd4d1332280 | 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 wri... |
github | mhrztrk/thesis-src-master | imtransform2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/imtransform2.m | 5,600 | utf_8 | 146253ca44967c3d2896308493c92421 | function J = imtransform2( I, varargin )
% Applies a general/special homography on an 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 tha... |
github | mhrztrk/thesis-src-master | kernelTracker.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/kernelTracker.m | 9,561 | utf_8 | 61fa635fe1b24bf93c906c422bf5ccb9 | 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 ... |
github | mhrztrk/thesis-src-master | seqIo.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/seqIo.m | 14,802 | utf_8 | b99a4c0b7d7c88f30784519518140ef4 | 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: ... |
github | mhrztrk/thesis-src-master | seqReaderPlugin.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/seqReaderPlugin.m | 9,167 | utf_8 | aabdc8490e71bb56a2720c6df5125ced | 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 rea... |
github | mhrztrk/thesis-src-master | bbGt.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/bbGt.m | 33,373 | utf_8 | ba474ccd9dba030bd49d5a0b1782b732 | function varargout = bbGt( action, varargin )
% Bounding box (bb) annotations struct, evaluation and sampling routines.
%
% bbGt gives acces to three types of routines:
% (1) Data structure for storing bb image annotations.
% (2) Routines for evaluating the Pascal criteria for object detection.
% (3) Routines for... |
github | mhrztrk/thesis-src-master | bbApply.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/images/bbApply.m | 19,026 | utf_8 | 4c7ee24d2915e3a9916963af8f93404c | 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 h... |
github | mhrztrk/thesis-src-master | toolboxUpdateHeader.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/toolboxUpdateHeader.m | 3,027 | utf_8 | ad64f080ec7b9b150758ab0bbccc4d51 | function toolboxUpdateHeader
% Update the headers of all the files.
%
% USAGE
% toolboxUpdateHeader
%
% INPUTS
%
% OUTPUTS
%
% EXAMPLE
%
% See also
%
% Piotr's Image&Video Toolbox Version 2.53
% Copyright 2010 Piotr Dollar. [pdollar-at-caltech.edu]
% Please email me if you find bugs, or have sugg... |
github | mhrztrk/thesis-src-master | toolboxGenDoc.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/toolboxGenDoc.m | 3,956 | utf_8 | 7cc9a4bb77b15deda5931b0c3ff3cbf6 | function toolboxGenDoc
% Generate documentation, must run from dir toolbox.
%
% 1) Make sure to run toolboxUpdateHeader.m
% 2) Update history.txt appropriately, including w current version
% 3) Update overview.html file with the version/data/link to zip:
% edit external/m2html/templates/frame-piotr/overview.h... |
github | mhrztrk/thesis-src-master | toolboxHeader.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/toolboxHeader.m | 2,508 | utf_8 | bdfab6d0c570f31ce43794ca5debb88b | 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.
%
% Ne... |
github | mhrztrk/thesis-src-master | demsvm2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/svm/demsvm2.m | 11,186 | utf_8 | 2d68238c12d17abf903b45e5f0f41ca6 | function demsvm2()
% DEMSVM2 - Demonstrate advanced Support Vector Machine features
%
% DEMSVM2 demonstrates the classification of a simple artificial data
% set by a Support Vector Machine classifier. The features of the SVM
% routines that make it useful for large data sets are shown.
%
% See also
% ... |
github | mhrztrk/thesis-src-master | svmtrain.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/svm/svmtrain.m | 22,359 | utf_8 | 76accfda8a8c589b49daf5d5dbae0c49 | function net = svmtrain(net, X, Y, alpha0, dodisplay)
% SVMTRAIN - Train a Support Vector Machine classifier
%
% NET = SVMTRAIN(NET, X, Y)
% Train the SVM given by NET using the training data X with target values
% Y. X is a matrix of size (N,NET.nin) with N training examples (one per
% row). Y is a colum... |
github | mhrztrk/thesis-src-master | demsvm1.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/svm/demsvm1.m | 8,052 | utf_8 | 5325891ea6a760e603461632fa1482b3 | function demsvm1()
% DEMSVM1 - Demonstrate basic Support Vector Machine classification
%
% DEMSVM1 demonstrates the classification of a simple artificial data
% set by a Support Vector Machine classifier, using different kernel
% functions.
%
% See also
% SVM, SVMTRAIN, SVMFWD, SVMKERNEL, DEMSVM2
%
... |
github | mhrztrk/thesis-src-master | mdot.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/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 | mhrztrk/thesis-src-master | m2html.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/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 | mhrztrk/thesis-src-master | doxysearch.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/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 | mhrztrk/thesis-src-master | doxywrite.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/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 | mhrztrk/thesis-src-master | doxyread.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/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 | mhrztrk/thesis-src-master | imwrite2split.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/imwrite2split.m | 1,664 | utf_8 | 8fa6eef9378b3b9aa633d6c096cd78f7 | % 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 = ... |
github | mhrztrk/thesis-src-master | playmovies.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/playmovies.m | 1,997 | utf_8 | 6d94553879ed29dca7238d26459ec685 | % [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 ... |
github | mhrztrk/thesis-src-master | pca_apply_large.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/pca_apply_large.m | 2,132 | utf_8 | daf33ba3f73412c5d5b488d89b7cc495 | % 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... |
github | mhrztrk/thesis-src-master | montages2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/montages2.m | 2,335 | utf_8 | 3ff14e5c538db6af4c9e1c99144d7c15 | % 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] )
%
% ... |
github | mhrztrk/thesis-src-master | filter_gauss_1D.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/filter_gauss_1D.m | 1,178 | utf_8 | 42acb7f8ac4ce94b6dc1dabe1e51e313 | % 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 o... |
github | mhrztrk/thesis-src-master | clfEcoc.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/clfEcoc.m | 1,539 | utf_8 | 3cb7110d53d7eefcca505365b078fe8e | 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 ... |
github | mhrztrk/thesis-src-master | getargs.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/getargs.m | 3,550 | utf_8 | b8ee235c28195894f0743b6cd00fd609 | % 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 ... |
github | mhrztrk/thesis-src-master | normxcorrn_fg.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/normxcorrn_fg.m | 2,771 | utf_8 | c0817b10bbc5d1793efad5fa7b4932ac | % 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 sp... |
github | mhrztrk/thesis-src-master | makemovie.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/makemovie.m | 1,313 | utf_8 | 7cadf737855cee5cb9a336e1204773c4 | % [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' );
% ... |
github | mhrztrk/thesis-src-master | localsum_block.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/localsum_block.m | 845 | utf_8 | d420d188aa320337183491498c7cc483 | % 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
%... |
github | mhrztrk/thesis-src-master | imrotate2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/imrotate2.m | 1,367 | utf_8 | 21148eb1e941255d74de56b5cb8994b1 | % 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 - ['linea... |
github | mhrztrk/thesis-src-master | imSubsResize.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/imSubsResize.m | 1,379 | utf_8 | 7bcc46113f2b8619d539a20ecb5a1835 | % 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 ... |
github | mhrztrk/thesis-src-master | imtranslate.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/imtranslate.m | 1,221 | utf_8 | 8a46326f7c0b520a89e2c4a153118ec9 | % 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 ... |
github | mhrztrk/thesis-src-master | randperm2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/randperm2.m | 1,444 | utf_8 | b8dc96688b2afddb3bd486803d3847b5 | % 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 ... |
github | mhrztrk/thesis-src-master | apply_homography.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/apply_homography.m | 3,676 | utf_8 | 18c79558e14e422d02c755d8363c7460 | % 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'.... |
github | mhrztrk/thesis-src-master | pca_apply.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/pca_apply.m | 2,506 | utf_8 | 4f38cdb1a3375943269e24dc6be6af65 | % 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
% ... |
github | mhrztrk/thesis-src-master | mode2.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/deprecated/mode2.m | 765 | utf_8 | f6b3681635cfe16059c5483214fb9814 | % 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
% ... |
github | mhrztrk/thesis-src-master | savefig.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/external/other/savefig.m | 13,343 | utf_8 | 5e55383fee448146f66f14d4c342b027 | 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 | mhrztrk/thesis-src-master | dirSynch.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/matlab/dirSynch.m | 4,749 | utf_8 | 8e959daf7125509e5d753828f89c5721 | 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 date... |
github | mhrztrk/thesis-src-master | plotRoc.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/matlab/plotRoc.m | 4,765 | utf_8 | 3ccb5cde4b7f9bd741ac8e7e93f25bb4 | function [h,det] = plotRoc( D, varargin )
% Function for display of rocs (receiver operator characteristic curves).
%
% Displays nice clearly visible curves. Consistent usage ensures uniform
% look for rocs. The input D should have n rows, each of which is of the
% form [false-positive rate true-positive rate]. D ... |
github | mhrztrk/thesis-src-master | simpleCache.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/matlab/simpleCache.m | 4,773 | utf_8 | bc0419795858dfe7124c77226c97ae49 | 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 sa... |
github | mhrztrk/thesis-src-master | tpsInterpolate.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/matlab/tpsInterpolate.m | 1,749 | utf_8 | 67b09e42630dbc49e4c0c1a6728b812f | 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] ... |
github | mhrztrk/thesis-src-master | checkNumArgs.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/matlab/checkNumArgs.m | 3,957 | utf_8 | 09747b332bb38c3b649a390642f1db28 | 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 se... |
github | mhrztrk/thesis-src-master | medfilt1m.m | .m | thesis-src-master/toolboxes/piotr_toolbox_V2.53/filters/medfilt1m.m | 3,127 | utf_8 | 390d33d58293d54211aa7255c9b9e220 | 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 contai... |
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