After successful completion of the course, students are able to identify, explain, and compare the most important theories, principles, concepts, and algorithms relating to the application of Computer Vision. Their knowledge and understanding will be in line with the current specialized literature in computer vision for applications. They will be able to utilize formal-mathematical methods for modeling, abstraction, solution finding, and evaluation, and develop problem-formulation and problem-solving skills.
Furthermore, students will have the ability to gather, organize, evaluate, and interpret information relevant to computer vision. They will be able to identify requirements and constraints in various aspects of computer vision, apply their acquired knowledge to complex computer vision tasks, and devise solutions and arguments.
Through this course, students will gain the skills to identify various aspects of computer vision, formulate solutions to issues, and effectively communicate and exchange ideas with others. They will also learn to assess their own capabilities and limitations and acquire the ability to provide constructive criticism for their own work and that of others. Additionally, the students will cultivate self-organization and self-responsibility in order to independently tackle tasks.
The course explores the fundamental concepts of Computer Vision and their various applications. It covers the creation of digital images using digital cameras and the subsequent steps to automatically derive information from these images. The course begins with an overview of digital image creation, then delves into classical image processing techniques such as image enhancement and compression. It progresses to the development of digital filters and segmentation techniques for extracting specific information from images. Through real-world examples, the course aims to illustrate basic concepts, common challenges, simple solutions, and typical applications of image processing. While no prior knowledge of image processing is required, a basic understanding of mathematics is necessary. In the exercise component of the course, students may partake in an excursion and describe the problems and solutions in an image processing application, or engage in solving a specific image processing problem in a lab exercise. At the end of the semester, students are expected to deliver an oral presentation and submit a written report on the group's work.
The course will focus on frontal presentations, written exams, and independent problem-solving related to computer vision. It consists of lectures and excursions, where real-life computer vision applications in industrial settings will be explained and examined in groups. The course will cover concepts and contents, which will be explained during the lectures and demonstrated in practice during the excursions. The groups will discuss and present findings from the excursions in both written and oral forms.