DM890 - Computer Vision

DM890: Computer Vision, 10 ETCS.

Course Introduction: Computer Vision is the study of enabling machines to “see” the visual world (i.e., understand images and videos). In this course, the students will learn fundamental computer vision algorithms and have opportunities to implement them. Further, we will be discussing more recent state-of-the-art visual representation learning approaches.

By successfully completing this course the students will be able to:

• Knowledge of a selection of basic image processing algorithms
• Knowledge of computer vision models and methods intended for applications
• Expert knowledge in a computer vision that is based on the highest international field of research within the field of computer science
• To be able to understand and on a scientific basis reflect on the knowledge of the field of computer vision and be able to identify scientific issues
• Describe, analyze and solve advanced computer science problems using the learned models (current deep neural network (DNN) based approaches to basic image processing and computer vision)
• Describe, analyze and solve problems in computer vision, demonstrate awareness of the current key research issues in computer vision; learn to implement and train their own DNN models
• Analyze advantages and disadvantages of different DNN architectures for image processing and computer vision
• Elucidate hypotheses made on a qualified theoretical background and be critical of one’s own and others’ research results and scientific models
• Plan and carry out scientific projects in computer vision at a high professional level
• Develop new variants of the learned methods where the specific problem requires it
• Disseminate research-based knowledge and discuss professional and scientific issues with both peers and non-specialists

[Full Course Description | ODIN]

I was responsible for the lecture in convolutional neural networks (CNNs). The slides can be accessed here: [Lecture: Convolutional Neural Networks]