Module Title: Advances in Image Processing and Computer Vision
Teaching hours: 39
Credits: 7,5
Semester: 2nd
Instructor:
Course Objectives
Image processing and artificial vision are two adjacent research areas with numerous applications in the wider scientific community. This intensive course focuses on introducing the basic concepts of image processing towards understanding both the theory and computational processes for addressing image processing problems and computer vision applications. The course is multidisciplinary, based on computer science, mathematics, signal processing and artificial intelligence, and addresses many potential applications, such as remote sensing, multimedia, industrial surveillance, robotics, medical imaging as well as human computer interaction.
Important topics include image representation, spatial and frequency filtering, feature extraction, and description and detection of objects. The emphasis is the learning of theoretical concepts, algorithms and techniques but more importantly on their application with high level programming environments (Matlab, Octave) and in object-oriented programming (Python), focusing on solving real vision and image processing problems.
A programming background is essential, as well as familiarity with linear algebra and basic knowledge of signal processing and mathematics.
After completing the course, students are expected to have acquired the necessary knowledge and skills to:
• Understand the basic concepts and modern algorithmic techniques of digital image processing and computer vision.
• Address computational problems related to image processing tasks in 2 and 3 dimensions.
Indicative Syllabus
- Introduction to image processing and computer vision
- Introduction to Image (2 and 3 Dimensions), Characteristics, Texture, Morphology, Image Transformations.
- Matlab, Octave and Python Laboratory work for image processing and computational vision
- Image processing basics: Image topology (Neighborhood and areas)
- Filter and Edge detection in the spatial and frequency domain
- Image restoration
- Detection of points / objects of interest
- Shape description- Morphology
- Applied image segmentation
- Geometric transformations and image registration
- Face Identification/Detection
- Medical image analysis: 3D and 4D processing principles
Bibliography:
- Richard Szeliski, Computer vision: algorithms and applications, Springer Science & Business Media, 2010/9/30.
- Rafael C. Gonzalez and Richard E. Woods, Digital Imaging Processing, Digital Image Processing, 4th edition, 2018, Pearson, ISBN number 9780133356724.
- N. Papamarkos, Ψηφιακή Επεξεργασία και Ανάλυση Εικόνας, 3rd edition, ISBN 978-960-92731-7-6