ITWT308 : Image Processing

Department

Internet Technologies Department

Academic Program

Bachelor in Internet Technologies

Type

Elective

Credits

03

Prerequisite

ITGS211

Overview

This course covers: introduction to image processing, image acquiring, point and discrete image transforms, linear image filtering, image distortions, types of noise, optimal image filtering, non-linear image filtering, watermarks, edge detection, segmentation, motion analysis, loseless and lossy image compression techniques.

Intended learning outcomes

Knowledge &understand

  • Have an idea on the importance and applications of image processing
  • Learn the basics, concepts and methodologies of image processing
  • Identify different transformations methodologies
  • learn image sampling and quantization
  • learn filtering in the spatial domain
  • learn filtering in the frequency domain
  • Understanding different methods of feature extraction using morphological operators.
  • Describe image segmentation approaches
  • learn anaconda distribution to implement all of the above

mental skills

  • Compare and analyze algorithms used to solve a problem
  • Compare between different image filtering methods
  • Analyze images in the frequency domain using various transforms
  • Evaluate the techniques for image enhancement, image restoration and Morphological Image Processing
  • Interpret image segmentation and representation techniques
  • Implement image processing algorithms
  • Devise algorithms to solve image processing problems

Practical & professional skills

  • Be able to design, code and test image processing applications
  • Apply Fourier transform
  • Apply different enhancement methods
  • Perform image representations and descriptions
  • Translate abstract ideas into practice
  • Implement and handle projects that use available datasets

General and transferable skills

  • . Using knowledge and sources effectively
  • Gain good searching capabilities
  • Group-working & presentation skills (real-life practical requirement).
  • Hand in assignments, reports and projects in time
  • Possess good image analysis concepts
  • Develop the basic image algorithms into complete project
  • Choose the appropriate image algorithms for a certain problem
  • Implement image processing techniques in anaconda such as histogram equalization, enhancement, filtering and segmentation.

Teaching and learning methods

  • Lectures.
  • Seminars.
  • Tutorials.
  • Problem-based/enquiry-based learning.
  • Laboratory and practical learning.
  • Projects.

Methods of assessments

  • Midterm Exam = 25
  • Home works (Practical, Presentation and Documentation) = 25
  • Practical Exam = 30
  • Final Exam = 20

Course contents

  • The importance and applications of image processing
  • Basics, concepts and methodologies of image processing.
  • Sampling, Fourier Transform,
  • and Convolution
  • Convolution and Frequency
  • Domain Filtering
  • Image Enhancement (pixel transformation, Histogram processing)
  • Image Enhancement (Linear (spatial) filtering, Nonlinear (spatial) filters)
  • Image Enhancement Using Derivatives (Image Derivatives-Gradient, Laplacian, Sharpening and unsharp masking)
  • Image Enhancement Using Derivatives (Edge detection using derivatives and filters, Image pyramids (Gaussian and Laplacian), Blending images)
  • Morphological Image Processing
  • Extracting Image Features and
  • Descriptors (extract features/descriptors from images, Harris Corner Detector)
  • Extracting Image Features and
  • Descriptors (Blob detectors with LoG, DoG, and DoH, Extraction of Histogram of Oriented Gradients features, Haar-like features Detector)
  • Image Segmentation (Hough transform-circle and line detection in an image, Thresholding and Otsu's segmentation)
  • Image Segmentation (Edges-based /region-based segmentation techniques, Felzenszwalb, SLIC, QuickShift, and Compact Watershed algorithms, Active contours, morphological snakes, and GrabCut algorithms)
  • Machine Learning
  • Methods in Image Processing

Data Mining/Business Intelligence (ITWT301)
Wide Area Networks (ITWT309)
Information Retrieval Systems (ITWT302)
Introduction to Computer Graphics (ITWT303)
Image Processing (ITWT308)
Cloud Computing (ITWT307)
Multimedia over IP Networks (ITWT306)
Principles of Games Developments (ITWT305)
e-commerce (ITWT304)
Mathematics I (ITMM111)
Physics (ITPH111)
Problem solving Technic (ITGS113)
Intro to Information Technology (ITGS111)
General English1 (ITEL111)
Arabic language 1 (ITAR111)
Mathematics II (ITMM122)
Arabic language 2 (ITAR122)
General English2 (ITEL122)
Introduction to Programming (ITGS122)
System Analysis and Design (ITGS124)
logic Circuit Design (ITGS126)
Object Oriented Programmin (ITGS211)
Introduction to Software Engineering (ITGS213)
Introduction to Networking (ITGS215)
Discrete Structures (ITGS217)
Numerical analysis (ITGS219)
Introduction to Statistics (ITST211)
Introduction to Internet Programming (ITGS226)
Foundation of Information Systems (ITGS222)
Computer Architucture & Organization (ITGS223)
Data Structure (ITGS220)
Introduction to Databases (ITGS228)
Information Security (ITGS224)
Human Computer Interaction (ITWT315)
Security Policies and Procedure (ITWT317)
Advanced Databases (ITWT313)
Advanced Internet Programming (ITWT311)
Design and Analysis algorithms (ITGS301)
Operating System (ITGS302)
IT Project Management (ITGS303)
Client server Programming (ITWT320)
Web Applications Development (ITWT413)
Ethical Hacking (ITWT420)
Scientific Writing (ITGS304)
Multimedia System development (ITWT324)
Web Services (ITWT411)
Integrated systems development (ITWT415)
Mobile Applications Development (ITWT422)