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Estimating border ownership using iterative vector voting and conditional random fields
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index.pdf
Date
2014
Author
Özkan, Buğra
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Border ownership is the information that signifies which side of a border owns the border. Estimating this information has recently become very popular for perceptual organization as it allows rectification of ambigious visual information. It is applied on many computer vision problems such as object detection, depth perception and optical flow. In this thesis, two different approaches are followed to solve the border ownership problem. For the supervised approach, conditional random fields are used as it is the most appropriate method for modelling con- textual relations between semantic classes. Tensor voting is the inspire of our second algorithm called Iterative Vector Voting, as it allows modelling different information sources and their interactions. It is an unsupervised voting frame- work, which is proper for the use of Gestalt visual cues. Experiments show that both two models show significant contribution to the border ownership problem with respect to the successful results gathered on our own large-scale dataset.
Subject Keywords
Computer graphics.
,
Figure-ground perception.
,
Pattern recognition systems.
,
Image processing
URI
http://etd.lib.metu.edu.tr/upload/12617119/index.pdf
https://hdl.handle.net/11511/23764
Collections
Graduate School of Natural and Applied Sciences, Thesis
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B. Özkan, “Estimating border ownership using iterative vector voting and conditional random fields,” M.S. - Master of Science, Middle East Technical University, 2014.
