Authors:
Martin Radolko
and
Enrico Gutzeit
Affiliation:
Fraunhofer Institute for Computer Research IGD, Germany
Keyword(s):
Image Segmentation, Background Substraction, Belief Propagation, Otsu’s Method, Markov Random Fields.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
Foreground-background segmentation in videos is an important low-level task needed for many different applications in computer vision. Therefore, a great variety of different algorithms have been proposed to deal with this problem, however none can deliver satisfactory results in all circumstances. Our approach combines an efficent novel Background Substraction algorithm with a higher order Markov Random Field (MRF) which can model the spatial relations between the pixels of an image far better than a simple pairwise MRF used in most of the state of the art methods. Afterwards, a runtime optimized Belief Propagation algorithm is used to compute an enhanced segmentation based on this model. Lastly, a local between Class Variance method is combined with this to enrich the data from the Background Substraction. To evaluate the results the difficult Wallflower data set is used.