Ender Konukoglu: Catalogue data in Autumn Semester 2018 |
| Name | Prof. Dr. Ender Konukoglu |
| Field | Biomedical Image Computing |
| Address | Biomedizinische Bildverarbeitung ETH Zürich, ETF E 113 Sternwartstrasse 7 8092 Zürich SWITZERLAND |
| Telephone | +41 44 633 88 16 |
| kender@vision.ee.ethz.ch | |
| Department | Information Technology and Electrical Engineering |
| Relationship | Associate Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 227-0447-00L | Image Analysis and Computer Vision  | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
| Abstract | Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks. | ||||
| Learning objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | ||||
| Content | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | ||||
| Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | ||||
| Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | ||||