Discovering unknown visual objects with novelty detection techniques
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Beschreibung
Humans are able to recognize objects of different categories like apple, bottle, or car without difficulty just by looking at them. Even if an unknown object occurs, they rapidly decide that they have never seen something like this before. However, for machines these tasks turn out to be much more complicated. Although recently developed algorithms of computer vision and machine learning achieve remarkably good performances in automatically recognizing objects in images and videos, their abilities are restricted to a fixed amount of known object categories that have been defined by annotated example images in a learning phase beforehand. The detection of unknown objects belonging to other categories is usually neglected by relying on the closed world assumption, which indicates that only objects from already observed categories appear in the application. This rarely holds for the real world in uncontrolled environments that are subject to change and where possibly occurring categories can not be determined in advance. For this reason, new methods and algorithmic concepts are introduced
in the present thesis, which allow for an automatic discovery of objects belonging to unknown and therefore novel categories.
Autor*in
Paul Bodesheim
Themen in »Discovering unknown visual objects with novelty detection techniques«
Computer Vision Machine Learning Novelty Detection