c# pdf reader text : Delete a page from a pdf online control Library platform web page asp.net .net web browser SzeliskiBook_20100903_draft80-part659

C.1 Data sets
779
Chapter5: Segmentation
Berkeley Segmentation Dataset and Benchmark of 1000 images labeled by 30humans,
along with an evaluation, http://www.eecs.berkeley.edu/Research/Projects/CS/vision/
grouping/segbench/(Martin,Fowlkes,Taletal.2001).
Weizmann segmentation evaluation database of 100 grayscale images with ground
truth segmentations,http://www.wisdom.weizmann.ac.il/vision/Seg
Evaluation
DB/
index.html(Alpert,Galun,Basrietal.2007).
Chapter8: Dense motion estimation
The Middlebury optic flow evaluation Web site,http://vision.middlebury.edu/flow/data
(Baker,Scharstein,Lewisetal.2009).
The Human-Assisted Motion Annotation database,
http://people.csail.mit.edu/celiu/motionAnnotation/(Liu,Freeman,Adelsonetal.2008)
Chapter10: Computational photography
High Dynamic Range radiance maps, http://www.debevec.org/Research/HDR/ (De-
bevec andMalik 1997).
Alpha matting evaluation Web site,http://alphamatting.com/(Rhemann,Rother,Wang
et al. 2009).
Chapter11: Stereo correspondence
MiddleburyStereo Datasets andEvaluation,http://vision.middlebury.edu/stereo/(Scharstein
and Szeliski 2002).
Stereo Classification and Performance Evaluation of different aggregation costs for
stereo matching, http://www.vision.deis.unibo.it/spe/SPEHome.aspx (Tombari, Mat-
toccia, Di Stefano et al. 2008).
MiddleburyMulti-ViewStereoDatasets,http://vision.middlebury.edu/mview/data/(Seitz,
Curless, Diebel et al. 2006).
Multi-view and Oxford Colleges building reconstructions,http://www.robots.ox.ac.uk/
vgg/data/data-mview.html.
Multi-View Stereo Datasets, http://cvlab.epfl.ch/data/strechamvs/(Strecha,Fransens,
and Van Gool 2006).
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780
Computer Vision: Algorithms and Applications (September 3, 2010 draft)
Multi-View Evaluation,http://cvlab.epfl.ch/
strecha/multiview/(Strecha,vonHansen,
Van Gool et al. 2008).
Chapter12: 3D reconstruction
HumanEva: synchronized video and motion capture dataset for evaluation of artic-
ulated human motion,http://vision.cs.brown.edu/humaneva/(Sigal,Balan,andBlack
2010).
Chapter13: Image-based rendering
The (New) Stanford LightFieldArchive,http://lightfield.stanford.edu/(Wilburn,Joshi,
Vaish et al. 2005).
Virtual Viewpoint Video: multi-viewpoint video with per-frame depth maps, http:
//research.microsoft.com/en-us/um/redmond/groups/ivm/vvv/(Zitnick,Kang,Uytten-
daele et al. 2004).
Chapter14: Recognition
For a list of visual recognition datasets, see Tables14.114.2. In addition to those,
there are also:
Buffyposeclasses,http://www.robots.ox.ac.uk/
vgg/data/buffy
pose
classes/andBuffy
stickmenV2.1,http://www.robots.ox.ac.uk/vgg/data/stickmen/index.html(Ferrari,Marin-
Jimenez, and Zisserman 2009; Eichner and Ferrari 2009).
H3D database of pose/jointannotated photographs of humans,http://www.eecs.berkeley.
edu/lbourdev/h3d/(BourdevandMalik2009).
ActionRecognitionDatasets,http://www.cs.berkeley.edu/projects/vision/action, has point-
ers to several datasets for action and activity recognition, as well as some papers. The
human action database athttp://www.nada.kth.se/cvap/actions/contains more action
sequences.
C.2 Software
One of the best sources for computer vision algorithms is the Open Source Computer Vision
(OpenCV) library (http://opencv.willowgarage.com/wiki/), which was developed by Gary
Bradski and his colleagues at Intel and is now being maintained and extended at Willow
Garage (BradskyandKaehler2008). A partial list of the available functions, taken from
http://opencv.willowgarage.com/documentation/cpp/includes:
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C.2 Software
781
 image processing and transforms (filtering, morphology, pyramids);
 geometric image transformations (rotations, resizing);
 miscellaneous image transformations (Fourier transforms, distance transforms);
 histograms;
 segmentation (watershed, mean shift);
 feature detection (Canny, Harris, Hough, MSER, SURF);
 motion analysis and object tracking (Lucas–Kanade, mean shift);
 camera calibration and 3D reconstruction;
 machine learning (k nearest neighbors, support vector machines, decision trees, boost-
ing, random trees, expectation-maximization, and neural networks).
The Intel Performance Primitives (IPP) library,http://software.intel.com/en-us/intel-ipp/,
contains highly optimized code for a variety of image processing tasks. Many of the routines
in OpenCV take advantage of this library, if it is installed, to run even faster. In terms of
functionality, it has many of the same operators as those found in OpenCV, plus additional
libraries for image and video compression, signaland speech processing, and matrix algebra.
The MATLAB Image Processing Toolbox,http://www.mathworks.com/products/image/,
contains routines for spatial transformations (rotations, resizing), normalized cross-correla-
tion, image analysis and statistics (edges, Hough transform), image enhancement (adaptive
histogram equalization, median filtering) and restoration (deblurring), linear filtering (con-
volution), image transforms (Fourier and DCT), and morphological operations (connected
components and distance transforms).
Two older libraries, which no longer appear to be under active development but contain
manyuseful routines, are VXL (C++ Libraries for Computer Vision Research and Implemen-
tation,http://vxl.sourceforge.net/) and LTI-Lib 2 (http://www.ie.itcr.ac.cr/palvarado/ltilib-2/
homepage/).
Photo editingandviewingpackages, suchas Windows LivePhoto Gallery, iPhoto, Picasa,
GIMP, and IrfanView, can beuseful for performing common processing tasks, convertingfor-
mats, and viewing your results. They canalso serve as interesting reference implementations
for image processing algorithms (such as tone correction or denoising) that you are trying to
develop from scratch.
There are also software packages and infrastructure that can be helpful for building real-
time video processing demos. Vision on Tap (http://www.visionontap.com/) provides a Web
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782
Computer Vision: Algorithms and Applications (September 3, 2010 draft)
service that will process your webcam video in real time (ChiuandRaskar2009). Video-
Man (VideoManager,http://videomanlib.sourceforge.net/) can be useful for getting real-time
video-baseddemos and applications running. Youcan alsouse imread inMATLAB to read
directly from any URL, such as a webcam.
Below, I list some additional software that canbe found onthe Web, grouped by the book
chapters to which they most correspond:
Chapter3: Image processing
matlabPyrTools—MATLAB source code for Laplacian pyramids, QMF/Wavelets, and
steerable pyramids,http://www.cns.nyu.edu/lcv/software.php (SimoncelliandAdel-
son 1990a; Simoncelli, Freeman, Adelson et al. 1992).
BLS-GSMimagedenoising,http://decsai.ugr.es/javier/denoise/(Portilla,Strela,Wain-
wright et al. 2003).
Fast bilateral filtering code,http://people.csail.mit.edu/jiawen/#code (Chen,Paris,and
Durand 2007).
C++ implementationof the fastdistancetransform algorithm,http://people.cs.uchicago.
edu/
pff/dt/(FelzenszwalbandHuttenlocher2004a).
GREYC’s Magic Image Converter, including image restoration software usingregular-
izationand anisotropicdiffusion,http://gmic.sourceforge.net/gimp.shtml(Tschumperl´e
and Deriche 2005).
Chapter4: Feature detection and matching
VLFeat, an open and portable library of computer vision algorithms,http://vlfeat.org/
(VedaldiandFulkerson2008).
SiftGPU: A GPU Implementation of Scale Invariant Feature Transform (SIFT),http:
//www.cs.unc.edu/
ccwu/siftgpu/(Wu2010).
SURF: Speeded Up Robust Features,http://www.vision.ee.ethz.ch/surf/ (Bay,Tuyte-
laars, and Van Gool 2006).
FASTcorner detection,http://mi.eng.cam.ac.uk/
er258/work/fast.html(RostenandDrum-
mond 2005, 2006).
Linux binaries for affine region detectors and descriptors, as well as MATLAB files to
compute repeatability and matching scores,http://www.robots.ox.ac.uk/vgg/research/
affine/.
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C.2 Software
783
Kanade–Lucas–Tomasi feature trackers: KLT, http://www.ces.clemson.edu/
stb/klt/
(ShiandTomasi1994); GPU-KLT,http://cs.unc.edu/cmzach/opensource.html(Zach,
Gallup, and Frahm 2008); andLucas–Kanade20Years On, http://www.ri.cmu.edu/
projects/project
515.html(BakerandMatthews2004).
Chapter5: Segmentation
Efficientgraph-based image segmentation,http://people.cs.uchicago.edu/pff/segment/
(FelzenszwalbandHuttenlocher2004b).
EDISON, edgedetection andimagesegmentation,http://coewww.rutgers.edu/riul/research/
code/EDISON/(MeerandGeorgescu2001;ComaniciuandMeer2002).
Normalized cuts segmentation including intervening contours,http://www.cis.upenn.
edu/jshi/software/(ShiandMalik2000;Malik,Belongie,Leungetal.2001).
Segmentation by weightedaggregation (SWA),http://www.cs.weizmann.ac.il/vision/
SWA/(Alpert,Galun,Basrietal.2007).
Chapter6: Feature-based alignment and calibration
Non-iterativePnP algorithm,http://cvlab.epfl.ch/software/EPnP/(Moreno-Noguer,Lep-
etit, and Fua 2007).
Tsai CameraCalibrationSoftware,http://www-2.cs.cmu.edu/rgw/TsaiCode.html(Tsai
1987).
Easy CameraCalibrationToolkit,http://research.microsoft.com/en-us/um/people/zhang/
Calib/(Zhang2000).
Camera Calibration Toolbox for MATLAB, http://www.vision.caltech.edu/bouguetj/
calib
doc/;aCversionisincludedinOpenCV.
MATLABfunctionsfor multipleviewgeometry,http://www.robots.ox.ac.uk/vgg/hzbook/
code/(HartleyandZisserman2004).
Chapter7: Structure from motion
SBA: A generic sparse bundle adjustment C/C++ package based on the Levenberg–
Marquardtalgorithm,http://www.ics.forth.gr/lourakis/sba/(LourakisandArgyros2009).
Simple sparsebundle adjustment(SSBA),http://cs.unc.edu/cmzach/opensource.html.
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784
Computer Vision: Algorithms and Applications (September 3, 2010 draft)
Bundler, structure from motion for unordered image collections, http://phototour.cs.
washington.edu/bundler/(Snavely,Seitz,andSzeliski2006).
Chapter8: Dense motion estimation
Optical flow software,http://www.cs.brown.edu/
black/code.html(BlackandAnan-
dan 1996).
Optical flow using total variation and conjugate gradient descent,http://people.csail.
mit.edu/celiu/OpticalFlow/(Liu2009).
TV-L1 optical flow on the GPU, http://cs.unc.edu/cmzach/opensource.html (Zach,
Pock, and Bischof 2007a).
elastix: a toolbox for rigid and nonrigid registration of images,http://elastix.isi.uu.nl/
(Klein,Staring,andPluim2007).
Deformableimageregistrationusingdiscreteoptimization,http://www.mrf-registration.
net/deformable/index.html(Glocker,Komodakis,Tziritasetal.2008).
Chapter9: Image stitching
Microsoft Research Image Compositing Editor for stitching images, http://research.
microsoft.com/en-us/um/redmond/groups/ivm/ice/.
Chapter10: Computational photography
HDRShop software for combining bracketed exposures into high-dynamic range radi-
ance images,http://projects.ict.usc.edu/graphics/HDRShop/.
Super-resolution code, http://www.robots.ox.ac.uk/vgg/software/SR/(Pickup2007;
Pickup, Capel, Roberts et al. 2007, 2009).
Chapter11: Stereo correspondence
StereoMatcher, standalone C++ stereo matching code, http://vision.middlebury.edu/
stereo/code/(ScharsteinandSzeliski2002).
Patch-based multi-view stereo software (PMVS Version 2),http://grail.cs.washington.
edu/software/pmvs/(FurukawaandPonce2011).
Chapter12: 3D reconstruction
Scanalyze: a system for aligning and merging range data,http://graphics.stanford.edu/
software/scanalyze/(CurlessandLevoy1996).
C.2 Software
785
MeshLab: software for processing, editing, and visualizing unstructured 3D triangular
meshes,http://meshlab.sourceforge.net/.
VRML viewers (various) are also a good way tovisualize texture-mapped 3D models.
Section12.6.4: Whole body modeling and tracking
Bayesian 3Dperson tracking,http://www.cs.brown.edu/
black/code.html(Sidenbladh,
Black, and Fleet 2000; Sidenbladh and Black 2003).
HumanEva: baseline code for the tracking of articulated human motion,http://vision.
cs.brown.edu/humaneva/(Sigal,Balan,andBlack2010).
Section14.1.1: Face detection
Sample face detection code and evaluation tools,
http://vision.ai.uiuc.edu/mhyang/face-detection-survey.html.
Section14.1.2: Pedestrian detection
Asimpleobjectdetector withboosting,http://people.csail.mit.edu/torralba/shortCourseRLOC/
boosting/boosting.html(Hastie,Tibshirani,andFriedman2001;Torralba,Murphy,and
Freeman 2007).
Discriminatively trained deformable part models, http://people.cs.uchicago.edu/pff/
latent/(Felzenszwalb,Girshick,McAllesteretal.2010).
Upper-body detector,http://www.robots.ox.ac.uk/vgg/software/UpperBody/(Ferrari,
Marin-Jimenez, and Zisserman 2008).
2D articulated human pose estimation software,http://www.vision.ee.ethz.ch/calvin/
articulated
human
pose
estimation
code/(EichnerandFerrari2009).
Section14.2.2: Active appearance and 3Dshape models
AAMtools: An active appearance modeling toolbox, http://cvsp.cs.ntua.gr/software/
AAMtools/(PapandreouandMaragos2008).
Section14.3: Instance recognition
FASTANNandFASTCLUSTER for approximatek-means (AKM),http://www.robots.
ox.ac.uk/vgg/software/(Philbin,Chum,Isardetal.2007).
Feature matching using fast approximate nearest neighbors, http://people.cs.ubc.ca/
mariusm/index.php/FLANN/FLANN(MujaandLowe2009).
786
Computer Vision: Algorithms and Applications (September 3, 2010 draft)
Section14.4.1: Bag of words
Twobag of words classifiers,http://people.csail.mit.edu/fergus/iccv2005/bagwords.html
(Fei-FeiandPerona2005;Sivic,Russell,Efrosetal.2005).
Bagof features andhierarchical k-means,http://www.vlfeat.org/(Nist´erandStew´enius
2006; Nowak, Jurie, and Triggs 2006).
Section14.4.2: Part-based models
Asimpleparts andstructureobjectdetector,http://people.csail.mit.edu/fergus/iccv2005/
partsstructure.html(FischlerandElschlager1973;FelzenszwalbandHuttenlocher2005).
Section14.5.1: Machine learning software
Support vector machines (SVM) software (http://www.support-vector-machines.org/
SVM
soft.html)has pointers tolots ofSVMlibraries, includingSVM
light
http://
svmlight.joachims.org/;LIBSVM,http://www.csie.ntu.edu.tw/cjlin/libsvm/(Fan,Chen,
and Lin 2005); andLIBLINEAR, http://www.csie.ntu.edu.tw/
cjlin/liblinear/ (Fan,
Chang, Hsieh et al. 2008).
Kernel Machines: links to SVM, Gaussian processes, boosting, and other machine
learning algorithms,http://www.kernel-machines.org/software.
Multiple kernels for image classification, http://www.robots.ox.ac.uk/vgg/software/
MKL/(VarmaandRay2007;Vedaldi,Gulshan,Varmaetal.2009).
AppendixA.1A.2: Matrix decompositions and linear least squares
2
BLAS (Basic Linear Algebra Subprograms), http://www.netlib.org/blas/(Blackford,
Demmel, Dongarra et al. 2002).
LAPACK (Linear Algebra PACKage), http://www.netlib.org/lapack/(Anderson,Bai,
Bischof et al. 1999).
GotoBLAS,http://www.tacc.utexas.edu/tacc-projects/.
ATLAS (AutomaticallyTunedLinear AlgebraSoftware),http://math-atlas.sourceforge.
net/(Demmel,Dongarra,Eijkhoutetal.2005).
Intel Math Kernel Library (MKL),http://software.intel.com/en-us/intel-mkl/.
AMDCoreMath Library(ACML),http://developer.amd.com/cpu/Libraries/acml/Pages/
default.aspx.
2
Thanks to SameerAgarwalforsuggesting and describing most ofthese sites.
C.2 Software
787
Robust PCA code,http://www.salle.url.edu/
ftorre/papers/rpca2.html(DelaTorreand
Black 2003).
AppendixA.3: Non-linear least squares
MINPACK,http://www.netlib.org/minpack/.
levmar: Levenberg–Marquardtnonlinear leastsquaresalgorithms,http://www.ics.forth.
gr/
lourakis/levmar/(Madsen,Nielsen,andTingleff2004).
AppendixA.4A.5: Direct and iterative sparse matrix solvers
SuiteSparse (various reordering algorithms, CHOLMOD) and SuiteSparse QR,http:
//www.cise.ufl.edu/research/sparse/SuiteSparse/(Davis2006,2008).
PARDISO (iterative and sparse direct solution),http://www.pardiso-project.org/.
TAUCS (sparse direct, iterative, out of core, preconditioners), http://www.tau.ac.il/
stoledo/taucs/.
HSL Mathematical Software Library,http://www.hsl.rl.ac.uk/index.html.
Templates for the solutionof linear systems,http://www.netlib.org/linalg/html
templates/
Templates.html(Barrett,Berry,Chanetal.1994). DownloadthePDFforinstructions
on how to get the software.
ITSOL, MIQR, andother sparsesolvers,http://www-users.cs.umn.edu/
saad/software/
(Saad2003).
ILUPACK,http://www-public.tu-bs.de/bolle/ilupack/.
AppendixB: Bayesian modeling and inference
Middlebury source code for MRF minimization, http://vision.middlebury.edu/MRF/
code/(Szeliski,Zabih,Scharsteinetal.2008).
C++ code for efficient belief propagation for early vision, http://people.cs.uchicago.
edu/
pff/bp/(FelzenszwalbandHuttenlocher2006).
FastPD MRF optimization code,http://www.csd.uoc.gr/komod/FastPD (Komodakis
and Tziritas 2007a; Komodakis, Tziritas, and Paragios 2008)
788
Computer Vision: Algorithms and Applications (September 3, 2010 draft)
double urand()
f
return ((double) rand()) / ((double) RAND
MAX);
g
void grand(double& g1, double& g2)
f
#ifndef M
PI
#define M
PI 3.14159265358979323846
#endif // M
PI
double n1 = urand();
double n2 = urand();
double x1 = n1 + (n1 == 0); /
*
guard against log(0)
*
/
double sqlogn1 = sqrt(-2.0
*
log (x1));
double angl = (2.0
*
M
PI)
*
n2;
g1 = sqlogn1
*
cos(angl);
g2 = sqlogn1
*
sin(angl);
g
Algorithm C.1 C algorithm for Gaussian random noise generation, using the Box–Muller
transform.
Gaussian noise generation. Alotofbasicsoftwarepackagescomewithauniformrandom
noise generator (e.g., the rand() routine in Unix), but not all have a Gaussian random
noise generator. To compute a normally distributed random variable, you can use the Box–
Muller transform (BoxandMuller1958), whose C code is given in AlgorithmC.1—note that
this routine returns pairs of random variables. Alternative methods for generating Gaussian
random numbers are given byThomas,Luk,Leongetal.(2007).
Pseudocolor generation. Inmanyapplications,itisconvenienttobeabletovisualizethe
set of labels assigned to an image (or to image features such as lines). One of the easiest
ways to do this is to assign a unique color to each integer label. In my work, I have found it
convenient to distribute these labels in a quasi-uniform fashion around the RGB color cube
using the following idea.
For each (non-negative) labelvalue, consider the bits as being split among the three color
channels, e.g., for a nine-bit value, the bits could be labeled RGBRGBRGB. After collecting
eachof thethree color values, reverse the bits so that the low-order bits vary the most quickly.
Documents you may be interested
Documents you may be interested