Computer Vision

This course IDATG2206 provides an introduction to the fundamental concepts and techniques of image processing and computer vision, a rapidly growing field that enables computers to interpret and understand the visual world. Students will gain a comprehensive understanding of image formation, image processing, feature extraction, image compression, different application areas of computer vision. The course will also enable the students to explore and understand real-world applications of computer vision in various domains.

• Image formation and low level processing
• Image acquisition
• Camera and optics
• Light and color
• Color imaging
• Image filtering
• Morphological image processing
• Image enhancement and restoration
• Feature detection and matching
• Image segmentation
• Image registration
• Image and video compression
• Image quality
• Introduction to spectral imaging, basic workflow and processing
• Introduction to machine learning applications.
• Application areas of computer vision

The above mentions topics will be covered through lectures, lab sessions, assignments, and projects.

Knowledge:

On successful completion of this course, students should have the knowledge to:

• Understand basic concepts, terminology, theories, 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.
• Able to design and implement algorithms for computer vision applications in different application areas

General competence:

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

• Lectures
• Project
• Assignments
• Lab exercises