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 |