Course content

• Image formation and low level processing
• Camera and optics
• Camera calibration
• Light and color
• Color imaging
• Image quality
• Image processing
• Image filtering
• 2D transformations
• Feature detection and matching
• Pattern Analysis
• Image registration
• Image and video compression
• Segmentation
• Classification, Introduction to spectral imaging
• Introduction to machine learning applications.

Learning outcome

Knowledge:

On successful completion of this course, students should have the knowledge to: - understand basic concepts, terminology, theories, models, and methods in the field of image processing and computer vision -describe basic methods of computer vision. -assess which methods to use for solving a given problem, and analyze the accuracy of the methods in image processing and computer vision.

Skills:

Upon completion of the course, the students will acquire skills to: -develop and apply image processing, computer vision techniques for solving practical problems -choose appropriate image processing methods for image filtering, image restoration, image reconstruction, segmentation, classification and representation.

General competence:

-Apply knowledge and skills to new areas to understand and conduct complex tasks and projects. -Analysis relevant professional and research problems.

Learning methods and activities

• Lectures
• Project
• Assignments
• Exercises

Compulsory assignments

• Oblig

Specific conditions

Recommended previous knowledge

IMAG1001 Mathematical Methods 1 or BMA1021 Mathematics for Programming

Course materials

Digital Image Processing by Rafael C. Gonzalez and Richard Eugene Woods

Lecture notes and supplementary material relevant to the course will be provided