ITD33514 Image Processing and Pattern Recognition (Spring 2015)

The course is connected to the following study programs

Elective course in:

• Bachelor Programme in Computer Engineering
• Bachelor Programme in Computer Engineering, Y-veien
• Bachelor Programme in Computer Engineering, Tress
• Bachelor Programme in Computer Science

Prerequisites

This course requires prerequisite knowledge in:

• mathematics equivalent to the course in Mathematics for computer science
• programming equivalent to the course in Object-oriented Programming

Lecture Semester

• Bachelor Programme in Computer Engineering: semester 4 (spring)
• Bachelor Programme in Computer Engineering, Y-veien: semester 4 (spring)
• Bachelor Programme in Computer Engineering, Tress: semester 4 (spring)
• Bachelor Programme in Computer Science: semester 6 (spring)

Total workload:

4 hours of lectures per week, as well as possible study groups and lab assignments/projects.

The student's learning outcomes after completing the course

Knowledge:

On completion of the course, the students:

• understand digital images and their properties
• understand colour models and perception
• are familiar with representation methods for digital images, including image coding and compression
• understand how images can be affected by noise
• are familiar with the key elements in optics
• understand how images can be filtered/processed for better quality
• understand the principles and methods in basic pattern recognition

Skills:

On completion of the course, the students can:

• use standard filters to improve image quality by filtering noise
• use standard filters to enhance the details in an image
• use standard techniques to detect edges, corners and objects in an image
• write programs for basic image processing and pattern recognition
• get a computer and/or computer system to recognize objects in an image by, for example, searching a database

Content

Digital images and their properties, colour models and perception, representation methods for digital images, optics, image coding and compression, noise reduction techniques, use of filters, detail enhancement, image transformations, handling shape in an image, pattern recognition, and classification. Emphasis is placed on the implementation of image processing and pattern recognition techniques.

Forms of teaching and learning

Lectures, exercises and lab assignments/projects.

Coursework requirements - conditions for taking the exam

The course entails a number of lab assignments/projects that must be done in groups. Students must write and submit a report and/or software program by the set deadlines. All submissions must be approved before students may sit the exam.

Examination

Written exam lasting 4 hours. Letter grading scale A?F.
All written support materials permitted.

Course evaluation

This course is evaluated as follows:

• Mid-semester evaluation (optional)
• Final evaluation (compulsory)

The course instructor prepares a course report on the basis of student feedback and on his/her own experiences of the course.
Course reports are discussed with the Committee for Study Quality at the Faculty of Computer Sciences.

Literature

To be announced prior to the course.