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Computer Vision (Lecture + Practice)
DozentIn: Prof. Dr.-Ing. Gunther Heidemann , Ulf Krumnack, Ph.D. , Lukas Niehaus
Veranstaltungstyp: Vorlesung
Ort: 32/102: Di. 14:00 - 16:00 (13x), 93/E01: Mi. 10:00 - 12:00 (5x), 35/E23-E24: Mi. 10:00 - 12:00 (5x) Do. 10:00 - 12:00 (12x), 66/E34: Mi. 10:00 - 12:00 (3x) Do. 10:00 - 12:00 (1x), 35/E21: Mi. 10:00 - 12:00 (1x)
Zeiten: Di. 14:00 - 16:00 (wöchentlich), Ort: 32/102, Mi. 10:00 - 12:00 (wöchentlich), Ort: 93/E01, 35/E23-E24, 66/E34 (+1 weitere), Do. 10:00 - 12:00 (wöchentlich), Ort: 35/E23-E24, 66/E34
Beschreibung: Both the rapid growth of image and video data and new applications such as robotics require automated image processing. This course introduces the basic concepts of artificial vision.
Topics: Image acquisition and representation; mathematical background; basic point operations; linear and nonlinear filtering; morphological pattern recognition; color (perceptual aspects and technical representation); gray-, color- and texture-segmentation; image reconstruction and enhancement; object recognition; compression; applications (e.g., image search in databases). A focus is on object recognition, where topics range from simple edge based methods and template matching over traditional approaches like PCA over Boosting, SIFT and SURF to (deep) neural networks.
