Image Segmentation
Segmentace obrazových dat
dizertační práce (OBHÁJENO)
Zobrazit/ otevřít
Trvalý odkaz
http://hdl.handle.net/20.500.11956/26218Identifikátory
SIS: 39803
Kolekce
- Kvalifikační práce [10690]
Autor
Vedoucí práce
Oponent práce
Scarpa, Giuseppe
Jan, Jiří
Fakulta / součást
Matematicko-fyzikální fakulta
Obor
Softwarové systémy
Katedra / ústav / klinika (externí)
Informace není k dispozici
Datum obhajoby
28. 4. 2010
Nakladatel
Univerzita Karlova, Matematicko-fyzikální fakultaJazyk
Angličtina
Známka
Prospěl/a
Klíčová slova (česky)
image segmentation, texture, benchmarkImage segmentation is a fundamental part in low level computer vision processing. It has an essential influence on the subsequent higher level visual scene interpretation for a wide range of applications. Unsupervised image segmentation is an ill-defined problem and thus cannot be optimally solved in general. Several novel unsupervised multispectral image segmentation methods based on the underlaying random field texture models (GMRF, 2D/3D CAR) were developed. These segmenters use efficient data representations that allow an analytical solutions and thus the segmentation algorithm is much faster in comparison to methods based on MCMC. All segmenters were extensively compared with the alternative state- of-the-art segmenters with very good results. The MW3AR segmenter scored as one of the best available. The cluster validation problem was solved by a modified EM algorithm. Two multiple resolution segmenters were designed as a combination of a set of single segmenters. To tackle a realistic variable lighting in images, the illumination invariant features were derived and the illumination invariant segmenter was developed. For the proper evaluation of segmentation results and ranking of algorithms, a unique web-based texture segmentation benchmark was proposed and implemented. It was used for comprehensive...