computer assisted image analysis - uppsala university · project experiment with matlab (including...
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Computer Assisted Image AnalysisLecture 1 - Introduction
Amin [email protected]
Centre for Image AnalysisUppsala University
2009-03-23
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Course Homepage
Course homepage:www.it.uu.se/edu/course/homepage/bild1/vt08/
ScheduleTeacher contact informationComputer exercise instructionsProject instructions...
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Course Litterature
Digital Image Processing 3rd Edition, by Gonzales and Woods(Digital Image Processing Using Matlab, by Gonzales and Woods)
Book homepage:www.imageprocessingplace.com
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Teachers
Pollacksbacken, House 2, floor 1www.cb.uu.se
Amin Allalou (lectures and projects)[email protected]
Milan Gavrilovic(lectures and projects)[email protected]
Hamid Sarve (lecture)[email protected]
Gustaf Kylberg (lectures and labs)[email protected]
Patrik Malm (lectures and labs)[email protected]
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Organisations
Swedish Society for Automated Image Analysiswww.ssba.org.se
Free membership for students
Newsletter (PDF)
Annual symposium
Annual summer school (3-4 days)
Member of IAPR
International Association for Pattern Recognitionwww.iapr.org
Newsletter
Conferences
Journals
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Course Arrangement
18 lectures3 computer exercises (1-2 students per group)
I Introduction to digital image analysisI SegmentationI Classification
1 project (2 students per group)I Small “real world” problem
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Computer Exercise 1Introduction to Digital Image Analysis
Learn how to work with an image in Matlab.
Histogram, filtering, arithmetics, FFT etc.Report done orally during lab sessionOtherwise hand in a written report
Good to idéa to start before the scheduled time.
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Computer Exercise 2Segmentation
Segmentation and analysis of circular objects using Matlab ImageProcessing Toolbox.
Combine functions to segment and analyse image of coinsNo “perfect solution”Similar to the projects, but with more guidanceWritten lab report
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Computer Exercise 3Classification
Segment real image regions using classification in Matlab.
Different statistical methods will be triedThresholding vs Maximum-Likelihood classificationUsing colour imagesWritten lab report
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Project
Experiment with Matlab (including the Image Processing Toolbox) tosolve a real image analysis problem.
10 different projects availableGroups of 2 students per groupNo time scheduled for projectExtensive reportOral presentation at the end of the course
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IntroductionWhat is a digital image?
rat.png Rat nose
>> I = imread (’rat.png’ ) ;>> A = I (26 :35 , 125:134)A =
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Why Digital Image Analysis?
HumanIdentificationRecognitionSee and describe relationsInterpretation usingexperience
ComputerMeasuring absolute valuesPerform calculationsNever gets tiredCheapFastObjective
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Perception and ObjectivenessWhich inner square is the brightest?
How much is dark and bright respectively?
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Perception and ObjectivenessWhich inner square is the brightest?
How much is dark and bright respectively?
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Application ExamplesAgricultural and Environmental
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Application ExamplesMedical and Biomedical
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Problem Solving Using Image AnalysisFundamental steps
Problem
Image acquisition
Preprocessing
Segmentation Representation
Classification
Knowledge base
Result
Low level
Intermediate level
High level
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Image Processing vs Image AnalysisDifference in Terminology
Image Processing
=⇒
Image Analysis
=⇒
Blue eyes8cm earsGreek flag
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Course Contents
DigitizationPointwise operationsLocal neighbourhood operationsFourier transform (FFT)SegmentationMathematical morphologyObject description and representationWaveletsImage restorationColourClassificationImage coding and compression
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Electromagnetic Spectrum
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Other Image Acquisition modalities
Acoustic ImagingElectron Microscopy (TEM and SEM)
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Creating a Digital Image
Illumination source
Scene elementImaging system
Imageplane
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Creating a Digital Image
Illumination source
Scene elementImaging system
Imageplane
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Creating a Digital Image
Illumination source
Scene elementImaging system
Imageplane
Output
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Sensors
Point scanner
Line scanner
Array scanner
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Digital Images
A 2D grayscale image f (x, y)The value of f (x, y) is the intensity, or graylevel, at position (x, y).When an image is digitized it is sampled in
SPACE (x, y): image samplingAMPLITUDE f (x, y): graylevel quantization
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Spatial Sampling
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Spatial Sampling
512 256 128
64 32
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Methods for Image Sampling (in Space)
Uniform - same sampling frequency everywhereAdaptive - higher sampling frequency in areas with greater detail(not very common)
The discrete sample is called a pixel (short for picture element) in 2Dand voxel (short for volume element) in 3D. It is usually square (cubic)but can also have other shapes, e.g. rectangular or hexagonal grids.
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Sampling Density and Resolution
Resolution is the smallest discernible detail in an image.The sampling density (together with the imaging system) limits theresolution.Sampling density at scanning is often measured in dots per inch(dpi) = pixels per 2.5 cm on the input object (e.g. paper). The“dot-size” may however be greater than the distance between twosamples, leading to a lower resolution. Always test!Sample twice as often as the smallest detail you need to resolve.
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Graylevel Quantization
256 32
8 2
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Graylevel Quantization
256 128 64 32
16 8 4 2
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Methods for Quantization (in Amplitude)
Uniform (linear) - the intensities of the object are mapped directlyto the graylevels of the image.Logarithmic - higher intensity resolution in darker areas (thehuman eye is logarithmic).
imag
ein
tens
ity
object intensityim
age
inte
nsity
object intensity
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Common Quantization Levels
Image values when using integers, f (x, y) ∈ [0, 2n − 1].
Bits Interval Commentn = 1 [0, 1] “binary image”n = 5 [0, 31] what the human can resolve locallyn = 8 [0, 255] 1 byte, very commonn = 16 [0, 65535] common in imaging systemsn = 24 [0, 16.2× 106] common in colour images (3× 8 bit for RGB)
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Choice of Sampling
What will the image be used for?What are the limitations in memory and speed?Will we only use the image for visual interpretation or do we wantto do any image analysis?What information is relevant for the analysis (i.e., color, spatialand/or graylevel resolution)?
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Resampling, Graylevel Interpolation
Nearest neighbour, NN.Bilinear, interpolation from four closest neighbours.
g(x, y)
T
f (x, y)
Resampling
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Resampling, Graylevel Interpolation
Original imageRotation with NN
interpolationRotation with bilinear
interpolation
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Aliasing when Sampling
The image information may be obscured if the sampling frequency isdifferent from “frequencies” in the image.
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Examples of Aliasing Effects
NN
Bilinear
The frequency of thin lines is too high tobe correctly represented when the imageis subsampled to 1/4 of its size.
This image was scannedfrom a magazine,resulting in a patterndue to the frequency ofthe raster in the printing.
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Review Questions
What is the difference between image processing and imageanalysis?Spatial sampling vs. graylevel quantization?What is the difference between nearest neighbour and bilinearinterpolation?How often should you sample with respect to the smallest detailthat needs to be resolved?What is aliasing?
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