Digital Image ProcessingDigital Image Processing

introductionintroduction

About Digital ImagesAbout Digital Images► This course is about digital images and what can be done to digital This course is about digital images and what can be done to digital

images. images. ► A digital image is simply an image that can be stored in a computer, A digital image is simply an image that can be stored in a computer,

i.e. a discrete function of position (in 2D or 3D space, time and i.e. a discrete function of position (in 2D or 3D space, time and spectral band) and greylevel. spectral band) and greylevel.

► For example, in the 2D case the image data contains information of For example, in the 2D case the image data contains information of the graylevel at each position in the image.the graylevel at each position in the image.

A digital image of a rat.

A magnification of the rat’s nose.

Digital ImagesDigital Images

A digital image can be thought of as a matrix of graylevels, or intensity values.

The magnification of the rat’s nose.

94 100 104 119 125 136 143 153 157 158

103 104 106 98 103 119 141 155 159 160

109 136 136 123 95 78 117 149 155 160

110 130 144 149 129 78 97 151 161 158

109 137 178 167 119 78 101 185 188 161

100 143 167 134 87 85 134 216 209 172

104 123 166 161 155 160 205 229 218 181

125 131 172 179 180 208 238 237 228 200

131 148 172 175 188 228 239 238 228 206

161 169 162 163 193 228 230 237 220 199

Intensity values of the rat’s nose.

ImagesImages

xy

f(x, y)row

column

Sample

Quantize

Why put the image into a computer ?Why put the image into a computer ?

What are computers good at compared to What are computers good at compared to people?people?

HumanHuman ComputerComputer

+ identify objects+ identify objects + measure absolute values+ measure absolute values

+ describe relationships+ describe relationships + perform complicated+ perform complicated

+ interpret images using+ interpret images using calculations calculations

experienceexperience + does not get tired / cheaper + does not get tired / cheaper

- difficulties with - difficulties with + fast+ fast

normalizing intensitynormalizing intensity + objective+ objective

- subjective - subjective

Digital Images: ApplicationsDigital Images: Applications► Environmental and agricultural applicationsEnvironmental and agricultural applications

Multi spectral satellite image

Aerial image of a forest

Microscopy image of wood

Digital Images: Applications

Hydrography and weather

Satellite image

Multi spectral aerial image of the Stockholm archipelago

Medical applicationsMedical applications► DiagnosisDiagnosis

X-ray image

MR (Magnetic Resonance)

PET (Positron Emission Tomography)

Medical ApplicationsMedical Applications► Research and DevelopmentResearch and Development

(Fluorescence microscopy)

cultured and stained celles

AIDS-virus particles(Electron microscopy)

stained cell nuclei in cancer tumor

Other applicationsOther applications Quality controlQuality control Biometry (face recognition, fingerprint…)Biometry (face recognition, fingerprint…) Handwriting recognitionHandwriting recognition Automatic surveillanceAutomatic surveillance

ForensicsForensics AstronomyAstronomy

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Course ContentsCourse Contents

► Some of the topics dicussed during the Some of the topics dicussed during the coursecourse Filtering in the spatial domain Filtering in the spatial domain The Fourier transform and its use in image The Fourier transform and its use in image

analysisanalysis Image restorationImage restoration ColorColor SegmentationSegmentation Binary image operations, morphology and Binary image operations, morphology and

feature extractionfeature extraction Classification and decisionClassification and decision etcetc

DIP: Course LogisticsDIP: Course Logistics

http://faculty.petra.ac.id/resmana

The Fundamental Steps in Digital Image ProcessingThe Fundamental Steps in Digital Image Processing

Image acquisition

Preprocessing Segmentation

Representation and description

Recognition and interpretation

Problem Solution

Fundamental Steps*Fundamental Steps*

*Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Addison-Wesley, 1992

ImageAcquisition

ImageAcquisition

Preprocessing Preprocessing SegmentationSegmentation Representation &Description

Representation &Description

Recognition &Interpretation

Recognition &Interpretation

ProblemDomain

KnowledgeBase

KnowledgeBase

Result

DIP: DetailsDIP: Details

G ray-level Histogram

Spatial

DF T DC T

Spectral

Digital Image Characteristics

Point Processing M asking Filtering

Enhancem ent

Degradation M odels Inverse Filtering W iener Filtering

Restoration

Pre-Processing

Inform ation Theory

LZW (gif)

Lossless

Transform -based (jpeg)

Lossy

Com pression

Edge Detection

Segm entation

Shape Descriptors Texture M orphology

Description

Digital Im age Processing

The Fourier transformThe Fourier transform

Original image The power spectra after Fourier transformation

Image after reverse transform of filtered power spectra.

Filtering in the spatial domainFiltering in the spatial domain

“Lena” with noice After median filtering Edge detection

Image restorationImage restoration► Restoration of images degraded by bad Restoration of images degraded by bad

focusing, motion etc.focusing, motion etc.

Blur caused by motion After restoration

ColorColor► Color representation and useColor representation and use

RGB-space CIE’s chromaticity diagram

SegmentationSegmentation► Segmentation means to divide an image into Segmentation means to divide an image into

objects and background. This is a necessary objects and background. This is a necessary step prior to feature extraction.step prior to feature extraction.

Gray level image Gray level image with binary overlay

Binary image operations, morphology and Binary image operations, morphology and feature extractionfeature extraction

Gray scale image… the same image after segmentation.

… after morphological closing... … after

skeletonization...

Classification and decisionClassification and decision► Classification can either be made on the object level Classification can either be made on the object level

(based on object features such as size and shape) or on (based on object features such as size and shape) or on the pixel level (based on intensity in spectral or texture the pixel level (based on intensity in spectral or texture information)information)

Original image Result of classification

What do you need to do Image What do you need to do Image Processing?Processing?

► MathematicsMathematics► PhysicsPhysics► StatisticsStatistics► Computer ScienceComputer Science► Artificial intelligenceArtificial intelligence► ““area” knowledgearea” knowledge► ……

Image Analysis (bildanalys) vs Image Analysis (bildanalys) vs Image Processing ( bildbehandling)Image Processing ( bildbehandling)

world

data image

Image Analysis

Computer Graphics

Image Processing

Imaging

Visualisation

“knowledge”

Image understanding Computer vision

Course goalsCourse goals

After the course you will know a bunch of After the course you will know a bunch of algorithms as well as ...algorithms as well as ...

•how a digital image works.

•when image analysis is a possible solution.

•when image analysis is not a possible solution.

•what the requirements on the equipment are.

•what the requirements on the image are.

•how to do some image processing and analysis yourself.

•what is true and false about imaging and analysis systems.

•that some images tell lies…..

Digital imagesDigital images

A 2D grayscale image f(x,y) the value of f(x,y) is the greylevel or intensity at position (x,y)

A digital image must be sampled (digitized):•in space (x,y): image sampling •in amplitude f(x,y): grey-level quantization

Image sampling (x,y)Image sampling (x,y)

Image sampling (x,y)Image sampling (x,y)

5122561286432

Methods for image sampling (in space)Methods for image sampling (in space)

•Uniform - same sampling frequency everywhere

•Adaptive - higher sampling frequency in areas with greater detail (not very common)

•The discrete sample is called a pixel (from picture element) in 2D and voxel (from volume element) in 3D and is usually square (cubic), but can also have other shapes.

256

Grey-level quantizationGrey-level quantization

16

3282

Methods for quantization (in amplitude)Methods for quantization (in amplitude)

•Uniform (linear) - intensity of object is lineary mapped to gray-levels of image •Logarithmic - higher intensity resolution in darker areas (the human eye is logarithmic)

object intensity

imag

e in

ten

sit

y

object intensity

imag

e in

ten

sit

y

Common quantization levelsCommon quantization levels

f(x,y) is given integer values [0-max], max=2n-1

n=1 [0 1] ”binary image”n=5 [0 31] maximum the human

eye can resolve (locally)n=8 [0 255] 1 byte, very commonn=16 [0 65535] common in researchn=24 [0 16.2*106] common in color images

(i.e. 3*8 for RGB)

Choice of samplingChoice 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 want to do any image analysis?

•What information is relevant for the analysis (i.e. color, spatial and/or graylevel resolution)?