Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11531
Title: Depth-adaptive methodologies for 3D image caregorization.
Authors: Kounalakis, Tsampikos
Advisors: Boulgouris, NV
Triantafyllidis, GA
Keywords: 3D imaging;Image classification;Object recognition;Image representation;Deep learning
Issue Date: 2015
Publisher: Brunel University London.
Abstract: Image classification is an active topic of computer vision research. This topic deals with the learning of patterns in order to allow efficient classification of visual information. However, most research efforts have focused on 2D image classification. In recent years, advances of 3D imaging enabled the development of applications and provided new research directions. In this thesis, we present methodologies and techniques for image classification using 3D image data. We conducted our research focusing on the attributes and limitations of depth information regarding possible uses. This research led us to the development of depth feature extraction methodologies that contribute to the representation of images thus enhancing the recognition efficiency. We proposed a new classification algorithm that adapts to the need of image representations by implementing a scale-based decision that exploits discriminant parts of representations. Learning from the design of image representation methods, we introduced our own which describes each image by its depicting content providing more discriminative image representation. We also propose a dictionary learning method that exploits the relation of training features by assessing the similarity of features originating from similar context regions. Finally, we present our research on deep learning algorithms combined with data and techniques used in 3D imaging. Our novel methods provide state-of-the-art results, thus contributing to the research of 3D image classification
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.
URI: http://bura.brunel.ac.uk/handle/2438/11531
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Theses

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