1. Digital Image Processing
introduction

2. About Digital Images
This course is about digital images and what can be done to digital images.
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 spectral band) and greylevel.
For example, in the 2D case the image data contains information of the graylevel at each position in the image.
A digital image of a rat.
A magnification of the rat’s nose.

3. Digital Images
A digital image can be thought of as a matrix of graylevels, or intensity values.
The magnification of the rat’s nose.
Intensity values of the rat’s nose.

4. Images
row
column x y
f(x, y)
Sample
Quantize

5. Why put the image into a computer ?
What are computers good at compared to people?
Human Computer
+ identify objects + measure absolute values
+ describe relationships + perform complicated
+ interpret images using calculations
experience + does not get tired / cheaper
- difficulties with fast
normalizing intensity + objective
- subjective

6. Digital Images: Applications
Environmental and agricultural applications
Multi spectral satellite image
Aerial image of a forest
Microscopy image of wood

7. Digital Images: Applications
Hydrography and weather
Multi spectral aerial image of the Stockholm archipelago
Satellite image

8. Medical applications Diagnosis MR (Magnetic Resonance)
PET (Positron Emission Tomography)
X-ray image

9. Medical Applications Research and Development AIDS-virus particles
(Electron microscopy)
cultured and stained celles
stained cell nuclei in cancer tumor
(Fluorescence microscopy)

10. { { Other applications Quality control
Biometry (face recognition, fingerprint…)
Handwriting recognition
Automatic surveillance
Forensics
Astronomy {

11. Course Contents Some of the topics dicussed during the course
Filtering in the spatial domain
The Fourier transform and its use in image analysis
Image restoration
Color
Segmentation
Binary image operations, morphology and feature extraction
Classification and decision
etc

12. DIP: Course Logistics

13. The Fundamental Steps in Digital Image Processing
Problem
Solution
Recognition and interpretation
Image acquisition
Lara Croft in a room
Problem = get out of the room
Image acquisition = visual system
Preprocessing = get used to the brightness level
Segmentation = differentiation between all the objects
Representation = build a “model” of objects
Recognition = what are these objects?
Solution = this is the door. Open it!
Representation and description
Preprocessing
Segmentation

14. Fundamental Steps* Knowledge Base Representation & Description
Preprocessing
Segmentation
Problem
Domain
Knowledge
Base
Image
Acquisition
Recognition &
Interpretation
Result
*Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Addison-Wesley, 1992

15. DIP: Details

16. The Fourier transform
Image after reverse transform of filtered power spectra.
Original image
The power spectra after Fourier transformation

17. Filtering in the spatial domain
“Lena” with noice
After median filtering
Edge detection

18. Image restoration
Restoration of images degraded by bad focusing, motion etc.
Blur caused by motion
After restoration

19. Color Color representation and use RGB-space
CIE’s chromaticity diagram

20. Segmentation
Segmentation means to divide an image into objects and background. This is a necessary step prior to feature extraction.
Gray level image with binary overlay
Gray level image

21. Binary image operations, morphology and feature extraction
Gray scale image
… the same image after segmentation.
… after morphological closing...
… after skeletonization...

22. Classification and decision
Classification can either be made on the object level (based on object features such as size and shape) or on the pixel level (based on intensity in spectral or texture information)
Original image
Result of classification

23. What do you need to do Image Processing?
Mathematics
Physics
Statistics
Computer Science
Artificial intelligence
“area” knowledge …

24. Image Analysis (bildanalys) vs Image Processing ( bildbehandling)
world
data
image
Image Analysis
Computer Graphics
Image Processing
Imaging
Visualisation
“knowledge”
Image understanding
Computer vision

25. Course goals
After the course you will know a bunch of 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…..

26. Digital 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

27. Image sampling (x,y)

28. Image sampling (x,y) 32 64
256
512
128

29. 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.

30. Grey-level quantization8 32
256 2 16

31. 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)
image intensity
image intensity
object intensity
object intensity

32. Common 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 common
n=16 [ ] common in research
n=24 [0 16.2*106] common in color images (i.e. 3*8 for RGB)

33. 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 want to do any image analysis?
What information is relevant for the analysis (i.e. color, spatial and/or graylevel resolution)?