Graphics and Computer Vision
General
Code: ΕΠ57
Language: Greek
Delivery: Face-to-face
Prerequisites: Basic concepts of linear algebra and signal processing
Workload
- Lectures: 39.0 hours
- Lab: 0.0 hours
- Study: 46.0 hours
- Project: 40.0 hours
Course Content
In the context of this course, an introduction to the basic principles of computer vision and modern methods that rely heavily on machine learning techniques will be provided. Specifically, the course material includes, in a first stage, the analysis of classical machine learning methods (e.g. K-nearest neighbor classifier, linear classifier, etc.) for visual content analysis applications. Then, the basic theory and structural elements related to the development of deep neural networks will be analyzed, as well as methodologies for their training. Then, convolutional neural network topologies for visual data analysis will be studied, where particular emphasis will be given on image classification, object detection and image segmentation methodologies. Additionally, the course will include the examination of more recent classes of neural networks, namely the so-called ‘transformers’, for the implementation of the aforementioned visual content analysis methodologies. The course contents also include an introduction to the basic concepts and applications in the field of computer graphics. Particular emphasis will be given on the study of computer vision techniques for the creation of graphic representations (e.g. 3D reconstruction, Simultaneous Localization and Mapping (SLAM), human pose estimation, etc.), as well as tools/techniques for their processing and representation (e.g. point cloud, rendering, etc.).
Learning Outcomes
- Understanding basic principles of image analysis and computer vision
- Understanding the use of machine and deep learning methods for computer vision applications
- Consolidation of different methods for image classification, object detection and image segmentation
- Understanding basic principles of computer graphics
- Familiarity with basic techniques for creating graphical representations, as well as tools/methods for processing and representing them
Skills
- Search, analysis and synthesis of data and information with the use of the assorted technologies
- Decision making
- Individual work
- Project design and management
- Promoting reasoning and self-improvement
- Promoting free, creative and deductive reasoning