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Digital Image Processing Lecture 1 (Introduction) Bu-Ali Sina University Computer Engineering Dep. Fall 2011

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  • Digital Image Processing

    Lecture 1(Introduction)

    Bu-Ali Sina UniversityComputer Engineering Dep.

    Fall 2011

  • Introduction

    One picture is worth more than ten thousandpwords

  • Outline

    Syllabus References Course Plane Grading and policies Introduction to image processing

  • Introduction

  • Syllabus

  • MSRT References

  • References

    Digital Image Processing, Rafael C.Gonzalez & Richard E. Woods,Addison-Wesley, 2002

  • Digital Image Processing Using matlabmuya

  • Recommended journals and conferences

    IEEE tran. On Image processingJournal of Graphics, Vision and Image Processing(GVIP)Image and Vision ComputingComputer Vision and Image UnderstandingJournal of Visual Communication and ImageRepresentationInternational Journal of Computer VisionMachine Vision and ApplicationsJournal of Mathematical Imaging and VisionGraphical Models and Image Processing

  • Course planImage fundamentalImage enhancement (Spatial domain)Image transform (Fourier, DCT)Image enhancement (Frequency domain)Image restorationColor image processingImage compressionMorphological image processingImage segmentation

  • Grading and PoliciesExams 50%

    Midterm 50% (25% of total) about 15/8/90 Final 50% (25% of total)

    Final Project (25%) One project (deadline is about 31/1/90)

    Seminar (15%)Seminar (15%) Every body presents a seminar (select a subject until

    15/8/90)Home works (10%)

    5 home works

  • Hours: 8-10 Sun. and Tue. (every two week)

    Site: http://Profs.basu.ac.ir/khotanlou

    Email:[email protected]@basu.ac.ir

    Contact: 8257410, 11 (324 )

  • ContentsThis lecture will cover:

    What is a digital image? What is digital image processing? History of digital image processing State of the art examples of digital imageState of the art examples of digital image

    processing

  • What is a Digital Image?A digital image is a representation of a two-dimensional image as a finite set of digitalvalues, called picture elements or pixels

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  • What is a Digital Image? (contH)Pixel values typically represent gray levels,colours, heights, etcRemember digitization implies that a digitalimage is an approximation of a real scene

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    1 pixel

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  • What is digital image?An image: 2-d function

    I=f(x,y) I: intensity(or color) (x,y): coordinate When (x y) and I are finite and discrete quantitiesWhen (x,y) and I are finite and discrete quantities

    -> digital image pixels, picture elements, image elements, pels

  • Representing digital images

  • Pixels

  • Side story of Lena

    1972 playboy: Miss Nov. 1997 Lena

  • Representing digital images

    Matrix form

    f(0,0) f(0,1) f(0,N-1)f(1,0) f(0,1) f(1,N-1)

    f(M-1,0) f(M-1,1) f(M-1,N-1)MxN

    bits to store the image = M x N x kgray level = 2k

  • Sources of digital images

    Electromagnetic(EM) energyAcoustic imagingSynthetic (computer generated) imagingSynthetic (computer-generated) imaging

  • EM images (cont.)The same objects in different EM spectrum

  • Ultrasound images

  • Synthetic images

  • What is a Digital Image? (contH)Common image formats include:

    1 sample per point (B&W or Grayscale) 3 samples per point (Red, Green, and Blue) 4 samples per point (Red, Green, Blue, and

    Alpha, Opacity)

    For most of this course we will focus on grey-scale images

  • What is Digital Image Processing?Digital image processing focuses on two majortasks

    Improvement of pictorial information for humaninterpretation

    Processing of image data for storage,g g gtransmission and representation for autonomousmachine perception

    Some argument about where image processingends and fields such as image analysis andcomputer vision start

  • What is DIP? (contH)The continuum from image processing tocomputer vision can be broken up into low-,mid- and high-level processes

    Low Level Process Mid Level Process High Level ProcessLow Level ProcessInput: ImageOutput: Image

    Examples: Noiseremoval, imagesharpening

    Mid Level ProcessInput: ImageOutput: Attributes

    Examples: Objectrecognition,segmentation

    High Level ProcessInput: AttributesOutput: Understanding

    Examples: Sceneunderstanding,autonomous navigation

    In this course we willstop here

  • Research fields

    Low-level processing

    ImageEarly vision

    Mid-level processing

    High-level processing

    processing

    Computervision

    Early vision

    Brain processing

  • Related fields

    Image processing Inputs and outputs are images Extract attributes from images

    Image analysisComputer vision

    Use computers to emulate human vision Related to artificial intelligence (AI)

    Pattern Recognition

  • History of Digital Image ProcessingEarly 1920s: One of the first applications ofdigital imaging was in the news-paper industry

    The Bartlane cable picturetransmission serviceIma

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    Images were transferred by submarine cablebetween London and New York

    Pictures were coded for cable transfer andreconstructed at the receiving end on atelegraph printer

    Early digital image

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  • History of DIP (contH)Mid to late 1920s: Improvements to theBartlane system resulted in higher qualityimages

    New reproductionprocesses basedIma

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    on photographictechniques

    Increased numberof tones inreproduced images Improved

    digital image Early 15 tone digitalimage

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  • History of DIP (contH)1960s: Improvements in computing technologyand the onset of the space race led to a surgeof work in digital image processing

    1964: Computers used toimprove the quality ofIma

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    images of the moon takenby the Ranger 7 probe

    Such techniques were usedin other space missionsincluding the Apollo landings

    A picture of the moon takenby the Ranger 7 probeminutes before landing

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  • History of DIP (contH)1970s: Digital image processing begins to beused in medical applications

    1979: Sir Godfrey N.Hounsfield & Prof. Allan M.Cormack share the Nobel

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    Prize in medicine for theinvention of tomography,the technology behindComputerised AxialTomography (CAT) scans Typical head slice CAT

    image

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  • History of DIP (contH)1980s - Today: The use of digital imageprocessing techniques has exploded and theyare now used for all kinds of tasks in all kindsof areas

    Image enhancement/restorationg Artistic effects Medical visualisation Industrial inspection Law enforcement Human computer interfaces

  • Examples: Image EnhancementOne of the most common uses of DIPtechniques: improve quality, remove noise etc

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  • Examples: The Hubble TelescopeLaunched in 1990 the Hubbletelescope can take images ofvery distant objectsHowever, an incorrect mirrormade many of Hubblesmade many of Hubble simages uselessImage processingtechniques wereused to fix this

  • Examples: Artistic EffectsArtistic effects areused to makeimages more visuallyappealing, to addspecial effects and topmake compositeimages

  • Examples: MedicineTake slice from MRI scan of canine heart, andfind boundaries between types of tissue

    Image with gray levels representing tissuedensity

    Use a suitable filter to highlight edgesImage

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    Original MRI Image of a Dog Heart Edge Detection ImageImag

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  • Examples: GISGeographic Information Systems

    Digital image processing techniques are usedextensively to manipulate satellite imagery

    Terrain classification MeteorologyIma

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  • Examples: GIS (contH)Night-Time Lights of theWorld data set

    Global inventory ofhuman settlement

    Not hard to imagineImage

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    gthe kind of analysisthat might be doneusing this data

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  • Examples: Industrial InspectionHuman operators areexpensive, slow andunreliableMake machines do thejob insteadIma

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    job insteadIndustrial vision systemsare used in all kinds ofindustriesCan we trust them?

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  • Examples: PCB InspectionPrinted Circuit Board (PCB) inspection

    Machine inspection is used to determine that allcomponents are present and that all solder jointsare acceptable

    Both conventional imaging and x-ray imagingare used

  • Examples: Law EnforcementImage processingtechniques are usedextensively by lawenforcers

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    precognition for speedcameras/automated tollsystems

    Fingerprint recognition Enhancement of CCTV

    images

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  • Examples: HCI

    Try to make human computerinterfaces more natural

    Face recognition Gesture recognition

    Does anyone remember theuser interface from MinorityReport?These tasks can beextremely difficult

  • Key Stages in Digital Image Processing

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancement

    ImageAcquisition

    g

    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

  • Key Stages in Digital Image Processing:Image Aquisition

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancementImag

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    ImageAcquisition

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    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

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  • Key Stages in Digital Image Processing:Image Enhancement

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancementImag

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    ImageAcquisition

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    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

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  • Key Stages in Digital Image Processing:Image Restoration

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancementImag

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    ImageAcquisition

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    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

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  • Key Stages in Digital Image Processing:Morphological Processing

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancementImag

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    ImageAcquisition

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    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

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  • Key Stages in Digital Image Processing:Segmentation

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancementImag

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    ImageAcquisition

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    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

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  • Key Stages in Digital Image Processing:Object Recognition

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancementImag

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    ImageAcquisition

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    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

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  • Key Stages in Digital Image Processing:Representation & Description

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancementImag

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    ImageAcquisition

    g

    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

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  • Key Stages in Digital Image Processing:Image Compression

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancement

    ImageAcquisition

    g

    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

  • Key Stages in Digital Image Processing:Colour Image Processing

    ImageRestoration

    MorphologicalProcessing

    SegmentationImageEnhancement

    ImageAcquisition

    g

    Representation& Description

    Enhancement

    ObjectRecognition

    Problem Domain

    Colour ImageProcessing

    ImageCompression

  • Fundamental steps in DIP

  • SummaryWe have looked at:

    What is a digital image? What is digital image processing? History of digital image processing State of the art examples of digital imageState of the art examples of digital image

    processing Key stages in digital image processing