EELE 5310: Digital Image Processing Lecture 1 Eng. Ruba A. Salamah Rsalamah @ iugaza.Edu

1. EELE 5310: Digital Image ProcessingLecture 1Eng. Ruba A. SalamahRsalamah @ iugaza.Edu

2. To Cover the basic theory and algorithms that are widely used in digital image processing. • To Expose students to current technologies and issues that are specific to image processing systems. • To Develop hands-on experience in using computers to process images. • Familiarize with MATLAB Image Processing Toolbox. Course Objectives:

3. • “Digital Image Processing” by R.C. Gonzalez and R.E. Woods, 3rd edition, Pearson Prentice Hall, 2008 • • Additional readings on the class website Recommended Textbook

4. Knowledge of the following three areas: -Linear Algebra. -Elementary Probability Theory. -Signals and Systems. Prerequisites:

5. Quizzes 15% • H.W 10% • Attendance 10% • Projects 20% • FinalExam45% Grading Policy

6. Introduction • Digital Image Fundamentals • Image Enhancement in the Spatial Domain • Image Enhancement in the Frequency Domain • Image Restoration • Image Compression • Image Segmentation • Representation and Description Course outline

7. Fundamentals of Digital Image Processing

8. A finite array of data values What is a Digital Image?

9. Processing digital images by means of a digital computer. • Image processing typically attempts to accomplish one of three things: • Restoring Images • Enhancing Images • Understanding Images • Restoration takes a corrupted image and attempts to recreate a clean original • Enhancement alters an image to makes its meaning clearer to human observers • Understanding usually attempts to mimic the human visual system in extracting meaning from an image What is Image Processing

10. Low-level Processes : • Involve primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. • A low-level process is characterized by the fact that both its inputs and outputs are images. • Mid-level Processes: • Involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for machine learning , and classification(recognition) of individual objects. • Its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Three Types of Processes

11. High-level Processes : • Processing involves "making sense“ of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision. Three Types of Processes

12. Processing of remote-sensed images via satellite. • Radar, MRI, Ultrasonic image processing. • Noise Reduction. • Character recognition. • Automatic inspection of industrial parts. • Content based image retrieval. • Biometrics. • Target tracking. Applications

13. The principle energy source for images is the EM spectrum Other sources include ultrasonic, electronic, and synthetic images. Sources of Energy for Image Formation

14. Some Applications -- Medical Diagnostics

15. Some Applications -- MRI

16. Some Applications -- Microscopy

17. Some Applications -- Industrial Inspection

18. Some Applications -- Remote Sensing

19. Some Applications -- Transmitting Images

20. Key Stages in Digital Image Processing

21. Image Acquisition

22. Image Enhancement

23. Image Restoration

24. Morphological Processing

25. Segmentation

26. Object Recognition

27. Image Representation & Description

28. Image Compression

29. Colour Image Processing