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Identifier | 000342575 | |||
Title | Bayesian flooding for image and video segmentation | |||
Alternative Title | Αλγόριθμοι Πλημμυρίδας κατά Bayes για Τμηματοποίηση Εικόνων και Βίντεο | |||
Author | Γκρίνιας, Ηλίας | |||
Abstract |
Segmentation is based on a statistical framework. Visual feature description is unified under a statistical point of view. The first part of the framework proposes a new, block based clustering method for visual content classification. Starting with the computation of visual cues, statistics of blocks are estimated and a k-means algorithm is employed for classification of blocks in a number of classes, using statistical dissimilarity criteria. A novel distance metric between affine models of optical flow is also described. Towards the automation of clustering, a method for computing the number of visual classes as well as a feature selection procedure are also proposed. Second part of the framework explores region based segmentation, given the statistical description of classes. Initial regions of high confidence per class are determined and two new region growing algorithms are proposed to expand initial regions. The strong relation of region growing on statistical surfaces to the connectivity percolation process is also underlined. To test the effectiveness of these new techniques, extensive tests are conducted on the Berkeley segmentation data set and the associated ground truth, using colour and texture. Furthermore, segmentation of independently moving objects using interframe difference and colour as well optical flow based segmentation in image sequences is also described. Finally, an application is shown, in which the proposed framework is used for extracting left ventricle in medical cardiac images. |
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Language | English | |||
Issue date | 2009-04-02 | |||
Collection | School/Department--School of Sciences and Engineering--Department of Computer Science--Doctoral theses | |||
Type of Work--Doctoral theses | ||||
Views | 577 |
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