Assessing the pixel values of an image

#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "iostream"
 
using namespace cv;
using namespace std;
 
int main( )
{
 
 Mat src1;
 src1 = imread("lena.jpg", CV_LOAD_IMAGE_COLOR); 
 namedWindow( "Original image", CV_WINDOW_AUTOSIZE ); 
 imshow( "Original image", src1 ); 
 
 Mat gray;
 cvtColor(src1, gray, CV_BGR2GRAY);
 namedWindow( "Gray image", CV_WINDOW_AUTOSIZE ); 
 imshow( "Gray image", gray );
 
 // know the number of channels the image has
 cout<<"original image channels: "<<src1.channels()<<"gray image="" channels:="" "<="" *******************="" read="" the="" pixel="" intensity="" *********************="" single="" channel="" grey="" scale="" (type="" 8uc1)="" and="" coordinates="" x="5" y="2" by="" convention,="" {row="" number="y}" {column="" intensity.val[0]="" contains="" a="" value="" from="" 0="" to="" 255="" scalar="" intensity1="gray.at(2," 5);="" cout="" <<="" "intensity=" << endl << " "="" intensity1.val[0]="" endl="" endl;="" 3="" with="" bgr="" color="" 8uc3)="" values="" can="" be="" stored="" in="" "int"="" or="" "uchar".="" here="" int="" is="" used.="" vec3b="" intensity2="src1.at(10,15);" blue="intensity2.val[0];" green="intensity2.val[1];" red="intensity2.val[2];" write="" **********************="" this="" an="" example="" opencv="" 2.4.6.0="" documentation="" mat="" h(10,="" 10,="" cv_64f);="" for(int="" i="0;" <="" h.rows;="" i++)="" j="0;" h.cols;="" j++)="" h.at(i,j)="1./(i+j+1);" cout<<h<<endl<<endl;="" modify="" pixels="" of="" for="" (int="" {="" j<src1.cols;="" src1.at<vec3b="">(i,j)[0] = 0;
 src1.at(i,j)[1] = 200;
 src1.at(i,j)[2] = 0; 
 }
 }
 namedWindow( "Modify pixel", CV_WINDOW_AUTOSIZE ); 
 imshow( "Modify pixel", src1 );
 
 waitKey(0); 
 return 0;
}

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Image Processing Algorithms and Codes

Algorithms	The Image Processing and Measurement Cookbook by Dr. John C. Russ www.reindeergraphics.com/tutorial/index.shtml Conference Papers http://poseidon.csd.auth.gr/papers/confers.l_ind.html Computer Vision Source Code www.cs.cmu.edu/afs/cs/project/cil/ftp/html/txtv-source.html Algorithms www.ph.tn.tudelft.nl/Courses/FIP/noframes/fip–7.html Basic image processing demos showing some basic image processing filters: thresholding, Gaussian filter, and Canny edge detector using MATLAB. http://robotics.eecs.berkeley.edu/~sastry/ee20 or http://robotics.eecs.berkeley.edu/~mayi/imgproc Gaussian Masks, Scale Space and Edge Detection www.cogs.susx.ac.uk/users/davidy/teachvision/vision3.html The SUSAN algorithms cover image noise filtering, edge finding and corner finding. www.fmrib.ox.ac.uk/~steve/susan Vision Systems Course www.cm.cf.ac.uk/Dave/Vision_lecture/Vision_lecture_caller.html Various Simple Image Processing Techniques http://astronomy.swin.edu.au/~pbourke/colour/imageprocess Algorithms commonly used in spectral processing methods www.galactic.com/Algorithms Beyond Photography — The Digital Darkroom http://www.spinroot.com/pico  (online, Jan 2003)
Alpha blending	pp. 150-154,  High Performance Computer Imaging
Antialiasing	Aliasing and Antialiasing www.education.siggraph.org/slides/slides93/28_93.htm Antialiasing with Line Samples www.merl.com/reports/TR2000-21/index.html
Anti-vignetting	http://starmatt.com/articles/av.html
Area	efg’s Polygon Area and Centroid Lab Report
Astrophotos	Post-Processing Astrophotos How to Put the Astro in Photographer Star Shaping
Authentication	Image Authentication for a Slippery New Age, Dr. Dobb’s Journal, April 1995. www.ddj.com/articles/1995/9504/9504a/9504a.htm
Bar Codes	Introduction to Bar Coding www.taltech.com/resources/intro_to_bc/introbc.htm
Biometrics	Intelligent Biometric Techniques in Fingerprint and Face Recognition (book) Also see Recognition, Faces and Recognition, Fingerprints below.
Books	Digital Image Processing Algorithms and Applications US  UK  DE
Centroid	efg’s Polygon Area and Centroid Lab Report Also see “Moments of Inertia” below
Chain Codes	7. Processing Line Drawings http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html
Chromaticity Charts	efg’s Color Reference Library
Classification	Jean Vezina’s  automatic tree species identification from digitized aerial photographs.  PowerPoint presentation for forestry audience, TreeID.ZIP.      Abstract. Fourier Descriptors Allow Web-Inspection System to Classify Plastic Shapes Vision Systems Design, Dec. 98, pp. 25-35 Image Processing and Neural Networks Classify Complex Defects Vision Systems Design, Mar. 99, pp. 63-70 Chapter 17, Classification, pp. 509-520 Practical Handbook on Image Processing for Scientific Applications
Compression	Image Compression, pp. 179-215 Digital Image Processing:  Principles and Applications Image Compression, Chapter 9 Simplified Approach to Image Processing Compression Links www.compression-pointers.com Chapter 6, Image Compression, pp. 307-412 Digital Image Processing Chapter 9, Image data compression, High Performance Computer Imaging Huffman, run-length, DCT, JPEG (lossy and lossless), MPEG Mitsuharu ARIMURA’s Bookmarks on Source Coding/Data Compression www.hn.is.uec.ac.jp/~arimura/compression_links.html
Condensation	The Condensation Algorithm www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/ISARD1/condensation.html
Contrast	Contrast Stretching, pp. 54-59 Simplified Approach to Image Processing Contrast Perception www.imagecontent.com/lucis/cpe.html  efg’s HistoStretchGrays Lab Report Contrast Manipulation, pp. 228-232 The Image Processing Handbook Also see Histograms below.
Counting Peaks	Algorithm for counting peaks on chart efg’s UseNet Post efg’s Pixel Profile Lab Report
Deconvolution	Deconvolution, Fourier-Self www.galactic.com/Algorithms/deconvle_fs.htm Blind Deconvolution Page www.ls.eso.org/lasilla/Telescopes/360cat/adonis/Idac/bd/bd.html
De-mosaicing	Digital Camera Designers Face a Maze of Trade-Offs www.eetimes.com/news/98/1003news/digital_camera.html
Detection, Buildings	Building Detection in Aerial Images www.imm.dtu.dk/documents/users/bro/thesis.abstract.html
Detection, Corners	The SUSAN algorithms cover image noise filtering, edge finding and corner finding. www.fmrib.ox.ac.uk/~steve/susan
Detection, Edges	Edge Detection, pp. 79-85 Simplified Approach to Image Processing A New Method of Edge Detection www.prettyview.com/edge/edge.shtml 3. Differentiation, Sharpening, Enhancement, Caricatures and Shape Morphing http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html Evaluation of Subpixel Line and Edge Detection Precision and Accuracy http://www9.informatik.tu-muenchen.de/papers/1998/ISPRS-Comm-III-98-Steger.abstract.html Contour Extraction www.geocities.com/kimdesok/Adoic/contour.html Edge Detection www.cs.cf.ac.uk/Dave/Vision_lecture/node24.html#SECTION00150000000000000000 Canny Edge Detector Code ftp://figment.csee.usf.edu/pub/Edge_Comparison/source_code/canny.src The Canny Edge Detector www.cogs.susx.ac.uk/users/davidy/teachvision/vision3.html#heading7 Edges — The Canny Edge Detector www.icbl.hw.ac.uk/marble/vision/low/edges/canny.htm “An Imaging Edge:  Tips and Technique for Edge Extraction” Advanced Imaging, Jan 99, pp. 36-40 Understanding Edge- and Line-Based Segmentation Vision Systems Design, Mar. 99, pp. 27-32 www.vision-systems-design.com/vsd/archive/1999/03/0399soft.html “Data Structures:  Your Mind Doesn’t Process Pixels, so why Should Your Software?” Advanced Imaging, Mar 99, pp. 34-35, 55 Gives example of Kanizsa Square that has illusory edges. J.F. Canny, “A computational approach to edge detection”, IEEE Patt. Anal. Machine Intell., Vol. 8, No. 6, pp. 55-73, 1990. Sobel Masks for Edge Detection www.cogs.susx.ac.uk/users/davidy/teachvision/vision2.html Line and edge detection:  One simple test image http://w3.ualg.pt/~dubuf/pubdat/ledge/ledge.html The SUSAN algorithms cover image noise filtering, edge finding and corner finding. www.fmrib.ox.ac.uk/~steve/susan Edges:  The Occurrence of Local Edges www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MARBLE/low/edges/occur.htm Chapter 1, Advanced Edge-Detection Techniques Algorithms for Image Processing and Computer Vision Chapter 12, Edges and Lines, pp. 387-413 Practical Handbook on Image Processing for Scientific Applications pp. 177-179, High Performance Computer Imaging
Distortion	Eliminating Distortion in Your Imaging System www.edmundoptics.com/techsup/pdf/elim-distortion.pdf Nonlinear Lens Distortion http://astronomy.swin.edu.au/~pbourke/projection/nonlinear
Dithering	Gernot Hoffmann’s “Dithering + Halftoning” (includes Hilbert-Peano dithering, Floyd-Steinberg dithering) www.fho-emden.de/~hoffmann/hilb010101.pdf Floyd-Steinberg http://thorkildsen.no/faqsys/docs/dither.c Dithering http://thorkildsen.no/faqsys/docs/dither5.txt http://thorkildsen.no/faqsys/docs/dither9.txt http://thorkildsen.no/faqsys/docs/dither10.txt http://thorkildsen.no/faqsys/docs/dither2.txt Average, Floyd-Steinberg, Ordered, Random Dithering www.visgraf.impa.br/Courses/ip00/proj/Dithering1/algoritmos_desenvolvidos.htm Test Page for Color Map Quantization (including Floyd-Steinberg error diffusion) www.cs.berkeley.edu/~laura/colormaps.html Halftoning www.cs.cmu.edu/afs/cs/user/webb/html/halftoning-paper.htm “A Balanced Dithering Technique,” December 1998, C/C++ Users Journal “Classic” dithering notes by Lee Cocker
Encryption / Decryption	efg’s ImageCrypt Lab Report
Enhancement	“Understanding Image Enhancement” median filter, rank filter, Nagao filter (edge-enhancement and image smoothing), Weymouth/Overton filter, contrast enhancement Vision Systems Design, July 1998, pp. 23-25; August 1998, pp. 21-24 Chapter 4, Image Enhancement, pp. 227-304 The Image Processing Handbook Chapter 4, pp. 161-251 Digital Image Processing, 2nd edition
Erosion	Dr. John Russ’ UseNet Post about erosion Also see Skeletonization Erosion and Dialation, pp. 129-135 Digital Image Processing:  Principles and Applications
Feature Extraction	Digital Image Processing:  Principles and Applications, pp. 153-167
Filters	Digital Filters www.dai.ed.ac.uk/HIPR2/filtops.htm “Understanding Image-Filtering Algorithms” spatial frequency, low-pass filtering, median filtering, high-pass, low-stop Vision Systems Design, June 1998, pp. 19-24 Chapter 6, Image neighborhood filtering High Performance Computer Imaging Section 4.3, Spatial Filtering, pp. 189-200 Section 4.4, Enhancement in the Frequency Domain, pp. 201-218 Digital Image Processing, 2nd edition Image Filtering in the Frequency Domain http://astronomy.swin.edu.au/~pbourke/analysis/imagefilter Image Transformations and Filters www.dai.ed.ac.uk/CVonline/transf.htm
Filters, Convolution	Note from Lazikas o Pontios about convolution filters. Dr. John Russ’ UseNet Post to comp.graphics.algorithms (19 July 2000): “… applying convolutions to the RGB channels in an image is usually wrong. For most purposes processing the image in an HSI space and modifying only the I channel, leaving the color information alone, produces the best results” Convolution www.cogs.susx.ac.uk/users/davidy/teachvision/vision2.html Convolution, pp. 67-73 Simplified Approach to Image Processing Section 6.3, Linear filtering using convolution, High Performance Computer Imaging. Discusses alternatives wasy to handle boundary pixels: zero fill the edges, don’t write the edges, extend the edges, reflect the edges Chris Russ’ UseNet Post about Fast Convolution Algorithms
Filters, Derivatives	pp. 250-254, The Image Processing Handbook Filters, Sobel and Kirsch pp. 255-268, The Image Processing Handbook pp. 178-179, High Performance Computer Imaging First Order Derivatives Operators, pp. 86-88 Roberts, Prewitt, Sobel, Frei-Chen; Second Order Derivative Operators, pp. 88-93 Prewitt, Kirsch, Robinson (3-level, 5-level) Simplified Approach to Image Processing
Filters, Laplacian	pp. 242-250,  The Image Processing Handbook Gaussian Filter www.fho-emden.de/~hoffmann/gauss25092001.pdf LoG (Laplacian of Gaussian):  Chris Russ’ UseNet Post about LoG operators of various sizes
Filters, Median and Rank	Optimal median smoothing, Applied Statistics, 44(2): 258-264, W. Haerdle, W. and M. Steiger, 1995. Haerdle and Steiger’s approach is O(log p) per pixel, where p is the width of the kernel. ftp://amadeus.wiwi.hu-berlin.de/pub/papers/sfb373/sfb1994/dpsfb940015.ps.Z Median Filtering, pp. 95-107 (includes color median filtering, p. 103) Simplified Approach to Image Processing Fast Median Search: an ANSI C implementation http://ndevilla.free.fr/median Median Filter www.dca.fee.unicamp.br/dipcourse/html-dip/c9/s3/front-page.html www.cee.hw.ac.uk/hipr/html/median.html Adaptive Center Weighted Median Filter http://Astro.MartianBachelor.com/CB245/ACWM.html Filtre Médian (3×3, 5×5) http://callisto.si.usherb.ca/~98715732/ift539/ Diagram of optimal way to compute median value from 3×3 array in hardware. Use the same logic in software. www.xilinx.com/xcell/xl23/xl23_16.pdf Median filters are useful tools in digital signal processing. Wesley examines their use for removing impulsive signal noise while maintaining signal trends. Additional resources include aa1099.txt(listings). Dr. Dobb’s Journal, October 1999.  www.ddj.com/articles/1999/9910/9910toc.htm Spyros’ UseNet Post about  Huang’s Algorithm Rank operations, pp. 268-277 The Image Processing Handbook Section 6.4, Nonlinear filtering I:  the median filter and its variations High Performance Computer Imaging
Filters, Morphological	Section 6.5, High Performance Computer Imaging Nonlinear filtering II:  morphological filters
Filters, Sharpening	p. 177, High Performance Computer Imaging. Includes discussion of unsharp masking. 3. Differentiation, Sharpening, Enhancement, Caricatures and Shape Morphing http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html Sharpening, pp. 77-79 Simplified Approach to Image Processing
Filters, Smoothing	pp. 176-177, High Performance Computer Imaging Gaussian Smoothing www.dai.ed.ac.uk/HIPR2/gsmooth.htm
Filters, Unsharp Mask	Chris Russ’ UseNet Post about implementing unsharp mask Chris Russ’ UseNet Post about two common uses for unsharp mask Unsharp masking is a photographic technique that increases the sharpness of photographic images. Tim presents an algorithm that implements this concept.   Additional resources include aa1199.zip (source code). Dr. Dobb’s Journal, Nov. 1999.  www.ddj.com/articles/1999/9911/9911toc.htm
Fingerprints	See Recognition, Fingerprints
Fluorescence Imagnig	Fluorescence Imaging Applications Guide Fluorescence Imaging Principles and Methods
Focus	Chris Russ’ UseNet Post about AutoFocus methods
Fractal analysis	pp. 282-288, The Image Processing Handbook
Gray Scales	Perceptually Optimized Grayscales www.fho-emden.de/~hoffmann/optigray06102001.pdf
Gamma correction	efg’s Color Reference Library
Gray Scale Images	Fast Gaussian blurring; Operations on gray scale images; Generalized order statistic filters; Contour construction for 2D image.  (Requires Magic Software) www.magic-software.com/im_gray.htm
Halftoning	A Review of Halftoning Techniques Digital Halftoning, Chapter 6 Simplified Approach to Image Processing Gernot Hoffmann’s “Dithering + Halftoning”  www.fho-emden.de/~hoffmann/hilb010101.pdf
Histogram equalization	Use of computer graphic simulation to explain color histogram structure www.graphics.cornell.edu/pubs/1997/PLG97.html pp. 233- 241, The Image Processing Handbook pp. 146-150,  High Performance Computer Imaging, includes discussion of adaptive histogram equalization
Histograms	Display/Print Histograms (R, G, B, H, S, V) efg’s Show Image   efg’s HistoStretchGrays Lab Report Histogram-base Operations: Contrast stretching, equalization www.ph.tn.tudelft.nl/Courses/FIP/noframes/fip-istogram.html Histogram Specification, pp. 49-54 Simplified Approach to Image Processing Histogram-based operations, pp. 143-150 High Performance Computer Imaging Three-way histograms (RGB, YUV, HSI) The Image Processing Handbook Section 4.2.2, Histogram Processing, pp. 171-185 Digital Image Processing, 2nd edition
Hough Transform	Hough Transform:  Journal and Conference Papers http://ipml.ee.duth.gr/~papamark/Hough.html 8. Detection of Structure in Noisy Pictures and Dot Patterns http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html On the Efficient Sampling Interval of the Parameter in Hough Transform www.ecip.tohoku.ac.jp/~hgot/HT Hough Transforms www.cogs.susx.ac.uk/users/davidy/teachvision/vision4.html www.cs.cf.ac.uk/Dave/Vision_lecture/node32.html#SECTION00162000000000000000 XHoughTool www.lut.fi/dep/tite/XHoughtool/xhoughtool.html Kim Madsen’s UseNet Post about Hough Transform
Image Analogies	http://mrl.nyu.edu/projects/image-analogies/index.html
Image Query	Fast Multiresolution Image Querying www.cs.washington.edu/research/projects/grail2/www/projects/query Shape Queries Using Image Databases www.ee.surrey.ac.uk/Research/VSSP/imagedb/demo.html Also see book Wavelets for Computer Graphics
Interpolation and Extrapolation	See Resampling Michel Chabroux’s UseNet Post explaining bilinear interpolation
Jitter	Infrared jitter imaging data reduction algorithms www.eso.org/projects/dfs/papers/jitter99
Lens Transformation	http://thorkildsen.no/faqsys/docs/lens.txt Nonlinear Lens Distortion http://astronomy.swin.edu.au/~pbourke/projection/nonlinear Computer Generated Angular Fisheye Projections http://astronomy.swin.edu.au/~pbourke/projection/fisheye Eric Rudd’s UseNet Post about fisheye lens simulation
Lighting	efg’s Color Reference Library
Masks	Chapter 7, Processing Binary Images, pp. 431-508 The Image Processing Handbook
Measurements	Chapter 8, Image Measurements, pp. 509-574 The Image Processing Handbook Chapter 16, Size and Shape, pp. 485-508 Practical Handbook on Image Processing for Scientific Applications
Metamorphisis	Feature-Based Image Metamorphosis www.hammerhead.com/thad/morph.html
Metrology	Metrology based on Computer Vision www.cranfield.ac.uk/sme/amac/research/metrology/metrology.html
Moiré Methods	“Moiré Methods Make Shape Recognition Easier” Vision Systems Design, March 1997, pp. 32-37 Modelization of the Moiré Phenomenon http://diwww.epfl.ch/w3lsp/pub/publications/moire Moiré Effects With Overlayed Line Screens www.education.siggraph.org/slides/slides95/s13.htm
Moments of Inertia	www.cm.cf.ac.uk/Dave/AI2/node194.html On the Calculation of Arbitrary Moments of Polygons http://www9.informatik.tu-muenchen.de/forschung/fgbv/tech-reports/1996/FGBV-96-05-Steger.abstract.html
Morphology	Understanding mathematical morphology  Vision Systems Design, May 1999 Understanding more mathematical morphology  Vision Systems Design, June 1999 Morphological Image Analysis:  Principles and Applications (June 1999). Author’s page The Morphology Digest is intended as a forum between workers in the field of Mathematical Morphology and related fields (stochastic geometry, random set theory, image algebra, etc.). www.cwi.nl/ftp/morphology/digest Mathematical Morphology and Image Interpolation http://cmm.ensmp.fr/~beucher/interpol/interpol.html SDC Morphology Toolbox for MATLAB:   includes fast queue-based algorithms for distance transform, watershed, reconstruction, labeling, area-opening, etc. www.mmorph.com
Mosaicing	Rho Ophiucus Mosiac Processing Example www.jps.net/jrfcomet/tech/rho.htm An Introduction to Image Mosaicing www.pitt.edu/~sevgum/research/mosaicing Image Registration and Mosaicking www.PrettyView.com/mtch/mtch.shtml Automatic Panoramic Image Merging www.sgi.com/grafica/merge/index.html Mosaicing with Super Resolution www.cs.huji.ac.il/~zomet/superResolution.html
Motion	Image Sequence Segmentation www.uwm.edu/~gjb/thesis.html Estimation of Visual Motion in Image Sequences www.imm.dtu.dk/documents/users/rl/publications/abstracts/phdthesis.abstract.html Chris Russ’ UseNet Post with suggestion on how to detect motion by image difference Section 5.4.2, Removal of Blur Caused by Uniform Linear Motion, pp. 272-278 Section 7.5, The Use of Motion in Segmentation Digital Image Processing Motion www.cogs.susx.ac.uk/users/davidy/teachvision/vision6.html Introduction to Active Contours and Visual Dynamics www.robots.ox.ac.uk/~ab/dynamics.html Motion and Time Sequence Analysis www.dai.ed.ac.uk/CVonline/motion.htm Thomas Kragh’s UseNet Post about Motion Blur Chapter 13, Orientation and Velocity, 415-440 Practical Handbook on Image Processing for Scientific Applications
Neural Networks	Neural Network as a Tool for Feature Selection www.prettyview.com/ann/nnfs.shtml
Noise Removal	Noise Removal from Images www.math.berkeley.edu/~sethian/Movies/Movienoiseremoval.html The SUSAN algorithms cover image noise filtering, edge finding and corner finding. www.fmrib.ox.ac.uk/~steve/susan Median filters are useful tools in digital signal processing. Wesley examines their use for removing impulsive signal noise while maintaining signal trends. Additional resources include aa1099.txt(listings). Dr. Dobb’s Journal, October 1999.  www.ddj.com/articles/1999/9910/9910toc.htm Also see Median Filters
Nyquist Limit	Using a Nyquist Chart to Evaluate Digital Camera Systems Part 1. www.photobit.com/Technology/White_Papers/Using_a_Nyquist_Chart_1/using_a_nyquist_chart_1.htm Part 2. www.photobit.com/Technology/White_Papers/Using_a_Nyquist_Chart_2/using_a_nyquist_chart_2.htm
Optimization	Using MMX Technology to Speed Up Machine Vision Algorithms Part 1. www.imaging.com/techinfo/tutorials/00000009/tutorial.html Part 2. www.imaging.com/techinfo/tutorials/00000015/tutorial.html
Panoramic Images	Helmut Dersch’s “Panorama Tools: Documentation, Info and More Uses” http://www.fh-furtwangen.de/~dersch (Be wary of IPIX, however.)
Perimeter	efg’s E-mail to Engineering Student at kmutt about how to computer perimeter of an object
Photogrammetry	See General Info page
Point Operations	Monadic image operations:  add constant, subtract constant, multiply constant, divide into constant, divide by constant, or constant, and constant, xor constant Diadic image operations:  add, subtract, multiply, divide, min,max, or, and, xor
Radon Transform	http://eivind.imm.dtu.dk/staff/ptoft/Radon/Radon.html
Recognition	Recognizing Flexible Objects www.cs.cornell.edu/vision/pff/boom99 Point Pattern Matching http://web.archive.org/web/*/http://www.eso.org/~ndevilla/point2  (Using Wayback Machine) Chris Russ’ UseNet Post about fixing broken lines in image recognition Pattern Classification and Scene Analysis (book) “Moiré Methods Make Shape Recognition Easier” Vision Systems Design, March 1997, pp. 32-37 Chapter 9, Recognition and Interpretation, pp. 571-661 Digital Image Processing See also Skeletonization
Recognition, Faces	The Face Detection Home Page www.facedetection.com Face Recognition Home Page www.cs.rug.nl/~peterkr/FACE/face.html Multi-Modal System for Locating Heads and Faces www.research.att.com/library/trs/TRs/96/96.5/96.5.1/96.5.1.abs.html Locating Faces and Facial Parts www.research.att.com/library/trs/TRs/96/96.4/96.4.1/96.4.1.body.htm Bayesian Modeling of Facial Similarity www.merl.com/reports/TR99-13/index.html Computer Vision Face Tracking For Use in a Perceptual User Interface http://developer.intel.com/technology/itj/q21998/articles/art_2.htm The Biometric Consortium, Research and Databases Face, Fingerprints, Handwriting, Voice www.biometrics.org/html/research.html Face detection, recognition and analysis www.cvc.uab.es/~jordi/cares.html Intelligent Biometric Techniques in Fingerprint and Face Recognition  US  UK
Recognition, Fingerprints	Biometrics http://dmoz.org/Computers/Security/Biometrics The Biometric Consortium, Reseat and Databases Face, Fingerprints, Handwriting, Voice www.biometrics.org/html/research.html Fingerprint Enhancement, www.kttech.com/fingerpr.html FBI Fingerprint Image Compression Standard www.c3.lanl.gov/~brislawn/FBI/FBI.html FAQ about FBI’s Wavelet/Scalar Quantization Specification for compression of digitized gray-scale fingerprint images. ftp://ftp.c3.lanl.gov/pub/misc/WSQ/FBI_WSQ_FAQ Intelligent Biometric Techniques in Fingerprint and Face Recognition    US  UK
Recognition, Handwriting	The Biometric Consortium, Reseat and Databases Face, Fingerprints, Handwriting, Voice www.biometrics.org/html/research.html Fingerprints and handwriting www.vision1.com/imagedb.html NIST Form-Based Handprint Recognition System www.nist.gov/itl/div894/894.03/databases/defs/nist_ocr.html
Recognition, Iris	Iris Recognition Homepage www.iris-recognition.org
Recognition, License Plates	Vehicle Number Plate Recognition Home Page Universidade de Trás-os-Montes e Alto Douro www.utad.pt/numberplate A Neural Network Based Artificial Vision System for Licence Plate Recognition www.wspc.com/journals/ijns/81/drag.html
Recognition, Optical Character (OCR)	Character Recognition http://web.mit.edu/stanrost/www/cs585p1/p1.html Optical Character Recognition:  Journal and Conference Papers http://ipml.ee.duth.gr/~papamark/OCR.html Character Recognition http://www.cedar.buffalo.edu/Publications/TechReps/OCR/ocr.html OCR/ICR Documents www.alumni.caltech.edu/~dave/patothr.html Character Recognition by Feature Point Extraction www.ccs.neu.edu/home/feneric/charrec.html Document Understanding and  Character Recognition WWW Server http://documents.cfar.umd.edu Chapter 8, Optical Character Recognition, pp. 275-304 Chapter 9, Symbol Recognition, pp. 305-356 Algorithms for Image Processing and Computer Vision Geometry in Action www.ics.uci.edu/~eppstein/gina/char.html
Recognition, Pattern	Pattern Recognition Links http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html Understanding Pattern Recognition, Visions Systems Design, July 1999. Understanding More Pattern-Recognition Techniques, Visions Systems Design, Aug 1999, pp. 21-25 Pattern Recognition Resources, www.dcs.ex.ac.uk/paa/resources.htm Pattern Recognition Information, www.ph.tn.tudelft.nl/PRInfo.html Statistical Pattern Recognition & Artificial Neural Network Library SPRLIB & ANNLIB.  www.ph.tn.tudelft.nl/~sprlib Optimizing Vision Applications: Which is Better, Blob-Centroid or Grayscale Search? www.imaging.com/techinfo/tutorials/00000016/tutorial.html
“Red Eye”	See efg’s  Color and Computers page
Representation	Chapter 8, Representation and Description, pp. 483-569 Digital Image Processing
Resampling	Advanced Image Processing:  Image Interpolation and Filtering www.engineering.uiowa.edu/~gec/248_s00_students/blake_carlson/hw2 Interpolation for Scaling, Rotation, Perspective and Morphing www.fho-emden.de/~hoffmann/interpol181100.pdf Interpolation (Nearest Neighbor, Bilinear, Cubic Convolution, B-Spline), pp. 110-123 Simplified Approach to Image Processing Digital Image Processing:  Principles and Applications, pp. 117-122 Image Processing By Interpolation and Extrapolation www.sgi.com/grafica/interp/index.html Efficient Image Magnification by Bicubic Spline Interploation http://members.bellatlantic.net/~vze2vrva/design.html Mathematical Morphology and Image Interpolation http://cmm.ensmp.fr/~beucher/interpol/interpol.html Section 8.3.2, “Interpolation,” in Practical Handbook on Image Processing for Scientific Applications  p. 269 Paul Heckbert’s “zoom” program www.cs.cmu.edu/~ph/src/zoom Non-Linear Magnification Home Page http://www.cs.indiana.edu/hyplan/tkeahey/research/nlm/nlm.html Note from Lazikas o Pontios abut Resampling to zoom. Testing Interpolator Quality www.fh-furtwangen.de/~dersch/interpolator/interpolator.html
Restoration, Reconstruction	Electronic Imaging, a Tool for the Reconstruction of Faded Color Photographs http://www.foto.unibas.ch/research/paper1/restor.html Image Restoration www.dca.fee.unicamp.br/dipcourse/html-dip/c7/c7.html “Novel Blind Deconvolution Techniques Restore Blurred Images” Researchers are using blind-image deconvolution to automatically deblur telescope and microscope images. Vision Systems Design, Nov. 98, pp. 35-41 Chapter 3, Correcting Image Defects, pp. 161-226 The Image Processing Handbook Chapter 5, Image Restoration, pp. 253-306 Digital Image Processing Chapter 6, Image Restoration, pp. 220-249 Algorithms for Image Processing and Computer Vision Chapter 9, Restoration and Reconstruction, pp. 287-306 Practical Handbook on Image Processing for Scientific Applications
Rotation	Turn, Turn, Turn:  Using the Graphics Class to Rotate Images http://msdn.microsoft.com/library/periodic/period99/turn.htm HOWTO: Display a Bitmap into a Rotated or Non-rectangular Area (Using Windows 2000 “WarpBlt” API call) http://support.microsoft.com/support/kb/articles/Q186/5/89.ASP “High Accuracy Rotation of Images,” in Computer Vision, Graphics and Image Processing, Vol. 54, No. 4, July 1992, pp. 340-344. One-pass and multipass rotation, Section 8.5 High Performance Computer Imaging. 2-pass and 3-pass rotations Practical Handbook on Image Processing for Scientific Applications  p. 107 comp.graphics.algorithms FAQ, Section 3.01, www.exaflop.org/docs/cgafaq In Windows NT the plgblt API call can be used for bitmap rotation if the RC_BITBLT is supported by a device. See efg’s RotateScanline, RotatePixels and FlipReverseRotate Lab Reports.
Scaling	Bitmap Scaling www.ddj.com/ftp/1997/1997.04/asc.zip/2ddda.asc Section 8.4, High Performance Computer Imaging, Image Scaling
Segmentation	Digital Image Processing:  Principles and Applications, pp. 124-152 Image Segmentation and Mathematical Morphology http://cmm.ensmp.fr/~beucher/wtshed.html Image Sequence Segmentation www.uwm.edu/~gjb/thesis.html Many image-analysis tasks must first separate the image into clearly defined regions. Lee’s algorithm performs such a separation and presents the results in a fashion amenable to further study. Additional resources include aa798.txt (listings) and aa798.zip (source code).   July 1998, Dr. Dobb’s Journal. www.ddj.com/articles/1998/9807/9807toc.htm Skin Cancer Segmentation program www.cs.wright.edu/people/faculty/agoshtas/skinseg.html Efficiently Computing a Good Segmentation www.cs.cornell.edu/Vision/segmentation Understanding Image Segmentation Basics Vision Systems Design, Sept. 98; Oct. 98, pp. 20-22 Understanding Region-Based Segmentation Vision Systems Design, Nov. 98, pp. 21-23 Understanding Oversegmentation and Region Merging Vision Systems Design, Dec. 98, pp. 21-23 Understanding Undersegmentation and Region Splitting Vision Systems Design, Feb. 99, pp. 16-19 Understanding Edge- and Line-Based Segmentation Vision Systems Design, Mar. 99, pp. 27-32 Understanding other edge- and line-based segmentation techniques  Vision Systems Design, Apr. 99 Segmentation http://axon.physik.uni-bremen.de/research/segmentation Color Image Segmentation (with C++ code) www.caip.rutgers.edu/~comanici/segm_images.html Unsupervised Segmentation http://www-sigproc.eng.cam.ac.uk/~sab Chapter 6, Segmentation and Thresholding, pp. 371-430 The Image Processing Handbook Chapter 7, Image Segmentation, pp. 413-482 Digital Image Processing Chapter 15, Segmentation, pp. 474-484 Practical Handbook on Image Processing for Scientific Applications Also see Segmentation on efg’s Color Reference Library page
Shape from Shading	A method for determining the shape of a surface from its image www.psrw.com/image/ShapeFromShading/shape.html
Sharpness	see links under MTF
Skeletonization	Digital Image Processing:  Principles and Applications, pp. 137-139 The Scale Space Skeletonization Page http://cyvision.if.sc.usp.br/msskeletons Hilditch’s Algorithm for Skeletonization http://jeff.cs.mcgill.ca/~godfried/teaching/projects97/azar/skeleton.html#algorithm Skeletonization in 2D, 3D and 4D images www.ph.tn.tudelft.nl/~pieter/clop.html Comparison of Skeletonization Methods www.cs.wisc.edu/~cs766-1/spring99/hw/hw1-result.html
Skew	Skew Correction http://ipml.ee.duth.gr/~papamark/free_software2.htm
Snakes	Gradient Vector Flow (GVF) snake.  Active contours — or snakes — are computer-generated curves that move within images to find object boundaries. http://iacl.ece.jhu.edu/projects/gvf GVF snake for *nix boxes source code ftp://ftp.cs.wisc.edu/computer-vision/gsnake.tar Snakes www.tu-dresden.de/fghgipg/gen3.html Active Snakes www.crl.research.digital.com/projects/vision/snakes/snakes.html Active Contours (Snakes) www.persci.com/~schulze/snakes Active contour models of shape or “snakes” www.cogs.susx.ac.uk/users/davidy/teachvision/vision7.html Snakes:  Active Contour Models www.ee.oulu.fi/~jth/monitor/snakes.html GSNAKE API www.cs.wisc.edu/computer-vision/projects/gsnake.html
Special Effects	Beyond Photography — The Digital Darkroom (Out of Print, 1995) Publisher Web Site:  http://www.phptr.com/ptrbooks/ptr_0130744107.html
Spectroscopy	USGS Imaging spectroscopy analysis: identify and map materials through spectroscopic remote sensing, on the earth and throughout the solar system. http://speclab.cr.usgs.gov About Imaging Spectroscopy, http://speclab.cr.usgs.gov/aboutimsp.html Multispectral Scanner Landsat Data http://edcwww.cr.usgs.gov/glis/hyper/guide/landsat
Steganography	The information hiding homepage – digital watermarking & steganography www.cl.cam.ac.uk/%7Efapp2/steganography Steganography www.jjtc.com/stegdoc Steganography & Digital Watermarking www.jjtc.com/Steganography Steganography/Watermarking Information www.tml.hut.fi/~helger/crypto/link/steganography Stenography and Digital Watermarks www.infosyssec.org/infosyssec/stendig1.htm Also see watermarking
Stereoscopic Vision	Stereo pair displays of surface range images http://members.AOL.com/DrJohnRuss/stereo.pdf Single-Image Stereograms, July 1995, Dr. Dobb’s Journal. www.ddj.com/articles/1995/9507/9507toc.htm Stereoscopic, or true 3-D, images take into account depth information that’s lost when conventional 3-D images are projected onto a PC’s 2-D screen. In addition to discussing hardware and software stereoscopic requirements, our authors present and implement algorithms for generating left- and right-eye views fundamental to stereoscopic viewing.   April 1994, Dr. Dobb’s Journal. www.ddj.com/articles/1993/9304/9304toc.htm Stereoscopic Vision and Perspective Projection www.cogs.susx.ac.uk/users/davidy/teachvision/vision5.html Stereo Vision http://axon.physik.uni-bremen.de/research/stereo
Texture	Fast Marble Texture Algorithm www.cubic.org/~submissive/sourcerer/marble.htm pp. 278-282, The Image Processing Handbook Chapter 4, Texture, pp 150-175 Algorithms for Image Processing and Computer Vision Chapter 14, Scale and Texture, pp. 441-469 Practical Handbook on Image Processing for Scientific Applications
Thermal Imaging	“Thermal Imaging Is Gaining Acceptance as a Diagnostic Tool” Biophotonics International, Nov/Dec 1998, pp. 48-53
Thresholding	Chapter 6, Segmentation and Thresholding, pp. 371-430 The Image Processing Handbook Section 7.3, Thresholding, pp. 443-457 Digital Image Processing
Transformation	Image Transformations (Chapter 7) Frequency Domain; Discrete Fourier Transform; FFT; Discrete Cosine Transform Simplified Approach to Image Processing
Transformation, Affine	Affine transformation software http://bigwww.epfl.ch/thevenaz/affine/index.html Affine texture mapping is fundamental to many forms of 3D rendering, including light interpolation and other sampling type operations. Additional resources include tmapper.txt (listings) and tmapsrc.zip(source code).  Dr. Dobb’s Journal, July 1998.   www.ddj.com/articles/1998/9807/9807toc.htm Section 8.6, High Performance Computer Imaging, Affine transformation Curvature Scale Space image under affine transforms www.ee.surrey.ac.uk/Research/VSSP/imagedb/affine.html
Transformation, Special effects	Section 8.8, High Performance Computer Imaging, Special-effects filters. Shows radial transformation Spatial Transformations (affine, perspective, bilinear, meshwarp) Simplified Approach to Image Processing
Transparency	Sean Dockery’s UseNet Post about Transparency including links to Microsoft Technical Reports
Triangle Intersection	Triangle Intersection Tests Dr. Dobb’s Journal, August 2000
Unsharp Masking	“Real” Digital Unsharp Masking:  A Digital Equivalent to a Film Technique www.astropix.com/HTML/J_DIGIT/USM.HTM
Warping	Digital Image Processing:  Principles and Applications, pp. 115-117 Fields-Based Warping, pp. 234-243 Simplified Approach to Image Processing
Watermarking	nformation Hiding Techniques for Steganography and Digital Watermarking by Fabien Petitcolas and Stefan Katzenbeisser  US  UK Author’s Web Site The information hiding homepage – digital watermarking & steganography www.cl.cam.ac.uk/%7Efapp2/steganography Digital Watermarking World www.watermarkingworld.org Invisible Watermarking:  Protecting Digital Pictures www.kodak.com/US/en/corp/researchDevelopment/technologyFeatures/digitalWatermarking.shtml References on Multimedia Watermarking and Data Hiding Research & Technology www.nt.e-technik.uni-erlangen.de/~hartung/watermarkinglinks.html www.lnt.de/~hartung/watermarkinglinks.html Watermarking of Video and Multimedia Data www.lnt.de/~hartung/watermarking.html Digital watermarking: perfecting the art of security www.spie.org/web/oer/november/nov99/cover1.html Digital Watermarking www.acm.org/~hlb/publications/dig_wtr/dig_watr.html Digital Watermarking: a solution to Electronic Copyright Management Systems Requirements www.medicif.org/dig_library/StateArt/Ipr/Piva/piva_doc.html Watermarking of Digital Images www.csee.usf.edu/~smohanty/personal/project.html Also see Steganography
Watershed Transformation	Watershet Edge Detection, pp. 148-153 Digital Image Processing:  Principles and Applications Several papers on Watershed algorithms www.rug.nl/hpc/people/arnold SDC Morphology Toolbox for MATLAB:   includes fast queue-based algorithms for distance transform, watershed, reconstruction, labeling, area-opening, etc. www.mmorph.com Watershed Transform www.geocities.com/kimdesok/#Watershed Image segmentation problems in mathematical morphology http://cmm.ensmp.fr/~beucher/wtshed.html
Zoom	See ResamplingImage Registration

Automated Image Registration	http://bishopw.loni.ucla.edu/AIR5
Automatic Panoramic Image Merging	www.sgi.com/grafica/merge/index.html
Automatic Registration of SAR Images and Digitized Maps	www.engr.mun.ca/~charlesr/summary.html
Bibliography	Michael Jacobs’ UseNet Post with many references to journal articles
CISG Registration Toolkit	www-ipg.umds.ac.uk/cisg/vtk-software
Cross-Correlation	Fast Normalized Cross-Correlation www.idiom.com/~zilla/Papers/nvisionInterface/nip.html
Elastic Image Registration	www.loni.ucla.edu/~thompson/ElasChpt.html Elastic Imaging Registration and Pathology Detection http://www.loni.ucla.edu/~thompson/PDF/no_pics_ElasChpt.pdf
FFT	Reddy, B. Srinivasa and B. N. Chatterji, “An FFT-Based Technique for Translation, Rotation, and Scale-Invariant Image Registration,” IEEE Trans. Image Processing vol. 5, no. 8 (1996 August) pp. 1266-1271.
FLIRT	FLIRT is software for linear image registration, which is part of FSL www.fmrib.ox.ac.uk/fsl
Image Matching and Registration	www.dra.hmg.gb/cis5/ipi13b.html
Image Matching by Maximisation of Mutual Information	www.robots.ox.ac.uk:5000/~seb/matching/mutual_info.html
Image Registration	Special Issue of Pattern Recognition on Image Registration www.cs.wright.edu/people/faculty/agoshtas/ImgReg.html
Image Registration and Mosaicking	www.prettyview.com/mtch/mtch.shtml
Image Registration Technology	KT-Tech, Inc. www.kttech.com/top/reg.htm
Matching Algorithms for Medical Image Processing	http://mbi.dkfz-heidelberg.de/mbi/research/matching.html
Medical Image Registration	New Book (June 2001)  US  UK  DE  FR
Medical Image Registration using Geometric Hashing	www.computer.org/cse/cs1997/c4029abs.htm
Multimodality Medical Image Registration	www.seas.gwu.edu/seas/institutes/medimage/registration
Raw Image Registration	NewSips System www.vilspa.esa.es/iue/IUEFA/manual/chap00/node26.html Cross-Corr http://archive.stsci.edu/iue/manual/newsips/node36.html
Retrospective Registration Evaluation Project	www.vuse.vanderbilt.edu/~jayw
Role of Image Registration in Brain Mapping	www.loni.ucla.edu/~thompson/IVCJ_99.html www.loni.ucla.edu/~thompson/PDF/IVCJ_99.pdf
Survey of image registration techniques	Abstract from ACM Computing Surveys, Vol 24., No. 4, Dec 92, pp. 325-376
Survey of Medical Image Registration	J.B.A. Maintz, Medical Image Analysis, 2(1):1-36, 1998. www.cs.uu.nl/people/twan/personal/media97.pdf An overview of medical image registration methods. In Symposium of the Belgian hospital physicists association (SBPH/BVZF), volume 12, pages V:1-22, 1996/1997. www.cs.uu.nl/people/twan/personal/brussel_bvz.pdf Author’s Bibliographical Information:    www.cs.uu.nl/people/twan/personal/list.html
3D Image Registration of CT Angiography	www.ee.princeton.edu/~mingy/cta
3D Image Registration for Sculptors	www.sculptor.org/register.htm

Mathematical Techniques
Also see Fourier Analysis and Wavelets Sections of efg’s Mathematics Page

DCTs	Implementing Fast DCTs (Discrete Cosine Transforms) Dr. Dobb’s Journal, March 1999, pp. 115-119
Fast Hartley Transform	Hartley Transform www.treasure-troves.com/math/HartleyTransform.html Note:  According to a letter to the editor in the July 1999 “Embedded Systems Programming” (p. 7) the Fast Hartley Transform is covered under U.S. Patent Number 4,646,256.  Use of this algorithm for noncommercial research must be negotiated with the Office of Technology Licensing at Stanford University.
Geometry	Vision Geometry and Mathematics www.dai.ed.ac.uk/CVonline/geom.htm Section 2.5, Imaging Geometry, pp. 51-71 Digital Image Processing, 2nd edition Chapter 8, Geometry, pp. 263-286 Practical Handbook on Image Processing for Scientific Applications Chapter 8, Image geometric operations High Performance Computer Imaging
Image Quality	IQM Approach:  Obtain Quality from Image Power Spectrum www.mitre.org/technology/mtf/poster.pdf Objective Image Quality Measure Derived from Digital Image Power Spectra Norman B. Nill, Optical Engineering, April 1992, Vol. 31, No. 4, pp. 813-825 www.mitre.org/technology/mtf/opteng.pdf Also see Modulation Transfer Function below
Image Transforms	Chapter 3, pp. 81-159 Fourier Transform (3.1), Walsh Transform (3.5.1), Hadamard Transform (3.5.2), Discrete Cosine Transform (3.5.3), Haar Transform (3.5.4), Slant Transform (3.5.5), Hotelling Transform (3.6) Digital Image Processing, 2nd edition
Modulation Transfer Function	Random Test Patterns To Evaluate MTF www.precisionopticalimaging.com/notes/notes.asp Understanding image sharpness part 1:  resolution and MTF curves in film and lenses. www.normankoren.com/Tutorials/MTF.html Understanding image sharpness part 2:  resolution and MTF curves in scanners and sharpening www.normankoren.com/Tutorials/MTF2.html Understanding MTF Testing www.edmundoptics.com/techsup/tsb/mtftest.cfm How to interpret MTF Graphs www.photozone.de/mtf.htm Use of Sinusoidal Test Patterns for MTF Evaluation www.sinepatterns.com/MTF/EngNotes.htm Image Quality Evaluation:  Modulation Transfer Function http://www.mitre.org/tech/mtf What is a MTF Curve? http://perso.club-internet.fr/legault/mtf.html
Moments	Section 8.3.4, Moments, pp. 514-518 Digital Image Processing
Matrices	Matrix Operations for Image Processing www.sgi.com/grafica/matrix/index.html
Point Spread Functions	The point-spread function (PSF) model of blurring http://extreme.indiana.edu/~tveldhui/papers/MAScThesis/node10.html Point Spread Function of Imaging System (diagram) www.vislab.usyd.edu.au/CP3/Four5/node2.html Point spread function of the human eye obtained by a dual double-pass method www.iop.org/PEL/abstract/pa07001l2 Point and Line Spread Functions www.yorku.ca/eye/psf.htm ACIS/HRMA Point Spread Function www.astro.psu.edu/xray/docs/cal_report/node150.html
Statistics	Statistics and Image Processing www.ph.tn.tudelft.nl/Courses/FIP/noframes/fip-Statisti.html
White Balance	White Balance Patents www.frontiernet.net/~shyam/whitebalance.htm