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Computer Vision (Lecture + Practice)




Prerequisites: Basic Math

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 fitering; 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 to modern algorithms such as Boosting, SIFT and SURF.

Weitere Angaben

Ort: 32/110: Di. 14:00 - 16:00 (12x), 93/E33: Di. 14:00 - 16:00 (1x), 93/E31: Mi. 10:00 - 12:00 (14x) Do. 10:00 - 12:00 (13x)
Zeiten: Di. 14:00 - 16:00 (wöchentlich), Ort: 32/110, 93/E33, Mi. 10:00 - 12:00 (wöchentlich), Ort: 93/E31, Do. 10:00 - 12:00 (wöchentlich), Ort: 93/E31
Erster Termin: Di , 24.10.2017 14:00 - 16:00, Ort: 32/110
Veranstaltungsart: Vorlesung (Offizielle Lehrveranstaltungen)


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