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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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Border Ownership (BO) signifies which side of an image border owns the border. This information is very important for many computer vision problems. There are various approaches for estimating BO in the literature. One approach is to use methods that rely on low-level local features; combinations of local visual cues such as convexity or junctions and using spectral features have been proven to be quite effective. Alternatively, global constraints or cues can be imposed on top of local cue predictions to ma...
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Border Ownership is the problem of identifying which image regions own the image border. This information is essential and important for large variety of high-level vision problems such as object segmentation, object recognition, depth perception, motion perception etc. Current computational approaches to Border Ownership (BO) estimation either use artificial or limited number of real images. In this thesis, we propose a new comprehensive BO database, including 500 indoor and 500 outdoor images whose BO inf...
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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.
