1. Ch1: Introduction Prepared by: Hanan Hardan
Image Processing
Ch1: Introduction
Prepared by: Hanan Hardan

2. “One picture is worth more than ten thousand words”
Introduction
“One picture is worth more than ten thousand words”

3. References
“Digital Image Processing”, Rafael C. Gonzalez & Richard E. Woods, Addison-Wesley, 2002
Much of the material that follows is taken from this book
“Machine Vision: Automated Visual Inspection and Robot Vision”, David Vernon, Prentice Hall, 1991
Available online at: homepages.inf.ed.ac.uk/rbf/BOOKS/VERNON/

4. Contents This 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 image processing
Key stages in digital image processing

5. What is a Digital Image?
A digital image: is a representation of a two-dimensional image as a finite set of digital values, called picture elements or pixels
Real world is continuous – an image is simply a digital approximation of this.

6. What is a Digital Image? (cont…)
Pixels: Elements of the digital image , each has intensity.
Intensity of pixel: the amplitudeغزارة of gray level (in gray scale images)
1 pixel

7. digital image processing
What is digital Image?
An image can be defined as function of 2 variables , f(x,y), where x and y are spatial coordinates, and the amplitude of f at any pair of coordinates (x , y) is called the intensity of the image at that point
Digital image is composed of a finite number of elements, each one has a particular location and value.. These element are called picture elements, image elements or pixels.
Note: images can be: binary, grayscale, color.

8. What is digital Image?

9. digital image processing
The image consists of finite number of pixels ( f(x,y) )
What is digital image?
Every pixel Is an intersection تقاطع between a row and a column.
every pixel has intensity كثافة
Ex:
f(4,3)= 123
Refers to a pixel existing on the intersection between row 4 with column 3, and its intensity is 123.
Remember digitization implies that a digital image is an approximation of a real scene
pixel

10. digital image processing
Binary Images
Remember: images can be: binary, grayscale, color.
Binary images are images that have been quantized to two values, usually denoted 0 and 1, but often with pixel values 0 and 255, representing black and white.

11. Binary Images

12. Grayscale Images
A grayscale (or graylevel) image is simply one in which the only colors are shades of gray (0 – 255)

13. Grayscale Images

14. Color Images
Color image: A color image contains pixels each of which holds three intensity values corresponding to the red, green, and blue or( RGB)

15. Color Images

16. What is digital image processing?
Digital image processing focuses on two major tasks
Improve image quality(pictorial information) for human perception and interpretation
Processing of image data for storage, transmission and representation for autonomous machine perception

17. Computer Graphics: the creation of image
Image processing fields:
Computer Graphics: the creation of image
Image processing: enhancement or other manipulation of the image
Computer vision: analysis of the content

18. What are digital image processing levels?
low level processes:
Input and output are images
Tasks: Primitive operations, such as, image processing to reduce noise, contrast enhancement and image sharpening
مثال صورة قديمة نريد تحسينها

19. What are digital image processing levels?
Mid-Level Processes:
Inputs, generally, are images. Outputs are attributes extracted from those images (edges, contours, identity of individual objects)
Tasks:
Segmentation (partitioning an image into regions or objects)
Description of those objects to reduce them to a form suitable for computer processing
Classifications (recognition) of objects
مثال: صورة لكرسي نريد تعديلها حاسوبيا لنبرز حوافه

20. What are digital image processing levels?
High-Level Processes
Input: Attributes Output: Understanding
Tasks: recognizing objects
Image analysis and computer vision(Analysis of the image content)
Examples: Scene understanding
مثال: صورة لمشتبه فيه نريد الحاسوب ان يتعرف عليه

21. Uses of DIP Image enhancement/restoration Artistic effects
Medical visualisation
Law enforcement
Human computer interfaces

22. Examples: Image Enhancement
One of the most common uses of DIP techniques: improve quality, remove noise etc

23. Examples: The Hubble Telescope
Launched in 1990 the Hubble telescope can take images of very distant objects
However, an incorrect mirror made many of Hubble’s images useless
Image processing techniques were used to fix this

24. Examples: Artistic Effects
Artistic effects are used to make images more visually appealing, to add special effects and to make composite images

25. Examples: Medicine
Take slice from MRI (Magnetic Resounance Imaging) scan of a heart, and find boundaries between types of tissue
Image with gray levels representing tissue density
Use a suitable filter to highlight edges

26. Examples: GIS Geographic Information Systems
Digital image processing techniques are used extensively to manipulate satellite imagery
Terrain classification (التضاريس)
Meteorology (الأرصاد الجوية)

27. Examples: Law Enforcement
Image processing techniques are used extensively by law enforcers
Number plate recognition for speed cameras
Fingerprint recognition

28. Examples: HCI Try to make human computer interfaces more natural
Face recognition

29. Fundamental steps in digital image processing

30. 1.Image Acquisition: (capturing an image in digital form)
Image Restoration
Morphological Processing
Image Enhancement
Segmentation
Image Acquisition
Object Recognition
Representation & Description
Problem Domain
Colour Image Processing
Image Compression

31. Fundamental Steps in DIP
Step 1: Image Acquisition The image is captured by a sensor (eg. Camera), and digitized if the output of the camera or sensor is not already in digital form, using analogue-to-digital convertor

32. 2.Image Enhancement: making an image look better in a subjective way.
Image Restoration
Morphological Processing
Image Enhancement
Segmentation
Image Acquisition
Object Recognition
Representation & Description
Problem Domain
Colour Image Processing
Image Compression

33. Fundamental Steps in DIP
Step 2: Image Enhancement
The process of manipulating an image so that the result is more suitable than the original for specific applications.
Enhancement techniques are so varied, and use so many different image processing approaches
The idea behind enhancement techniques is to bring out details that are hidden, or simple to highlight certain features of interest in an image.

34. 3.Image Restoration: improving the appearance of any image objectively.
Morphological Processing
Image Enhancement
Segmentation
Image Acquisition
Object Recognition
Representation & Description
Problem Domain
Colour Image Processing
Image Compression

35. Fundamental Steps in DIP
Step 3: Image Restoration - Improving the appearance of an image (محاولة اعادة الصوره الى طبيعتها) - Tend to be based on mathematical or probabilistic models of image degradation.

36. 4.Morphological Processing: extracting image components that are useful in the representation and description of shape
Image Restoration
Morphological Processing
Image Enhancement
Segmentation
Image Acquisition
Object Recognition
Representation & Description
Problem Domain
Colour Image Processing
Image Compression

37. Fundamental Steps in DIP
Step4 : Morphological Processing
Tools for extracting image components that are useful in the representation and description of shape.

38. 5.Segmentation: partitioning an image into its constituent parts or objects.
Image Restoration
Morphological Processing
Image Enhancement
Segmentation
Image Acquisition
Object Recognition
Representation & Description
Problem Domain
Colour Image Processing
Image Compression

39. Fundamental Steps in DIP
Step 5: Image Segmentation Segmentation procedures partition an image into its parts or objects. Computer tries to separate objects from the image background. الهدف: الحصول على الاجزاء المهمه او لاعادة تشكيل الصورة لتعطي معنى مختلف

40. 6.Object Recognition: assigning a label to an object based on its descriptors
Image Restoration
Morphological Processing
Image Enhancement
Segmentation
Image Acquisition
Object Recognition
Representation & Description
Problem Domain
Colour Image Processing
Image Compression

41. Fundamental Steps in DIP
Step6 : Recognition and Interpretation Recognition: the process that assigns label to an object based on the information provided by its description. تميز محتوى الصوره

42. 7. Representation & Description: boundary representation vs
7.Representation & Description: boundary representation vs. region representation. Boundary descriptors vs. region descriptors
Image Restoration
Morphological Processing
Image Enhancement
Segmentation
Image Acquisition
Object Recognition
Representation & Description
Problem Domain
Colour Image Processing
Image Compression

43. Fundamental Steps in DIP
Step 7: Representation and Description
Make a decision whether the data should be represented as a boundary or as a complete region:
Boundary representation: focus on external shape characteristics, such as corners and inflections.
Region representation: focus on internal properties, such as texture or skeleton shape
تمثيل الصوره ووصفها

44. 8.Image Compression: reducing the stored and transmitted image data.
Image Restoration
Morphological Processing
Image Enhancement
Segmentation
Image Acquisition
Object Recognition
Representation & Description
Problem Domain
Colour Image Processing
Image Compression

45. Fundamental Steps in DIP
Step 8: Compression
Techniques for reducing the storage required to save an image or the bandwidth required to transmit it.

46. 9.Colour Image Processing: color models and basic color processing
Image Restoration
Morphological Processing
Image Enhancement
Segmentation
Image Acquisition
Object Recognition
Representation & Description
Problem Domain
Colour Image Processing
Image Compression

47. Fundamental Steps in DIP
Step 9: Colour Image Processing
Use the colour of the image to extract features of interest in an image