Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293331
Title: Segmentation of textured images
Author: Li, Zhongqiang
ISNI:       0000 0001 3609 5935
Awarding Body: Lancashire Polytechnic
Current Institution: University of Central Lancashire
Date of Award: 1991
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Abstract:
This study is dedicated to the problem of segmenting monochrome images into distinct homogeneous regions by texture properties. The principle of the approaches to texture segmentation adopted in this thesis is mapping a textured image into a grey level image so that conventional segmentation techniques by intensity can be applied. Three novel approaches to texture segmentation have been developed in this thesis. They are called the Local Feature Statistics Approach (LFS), the Local Spectral Mapping Approach (LSM) and the Multichannel Spatial Filtering Approach (MSF). In the LFS approach, a multiresolution scheme for extracting texture features is introduced. This scheme produces features which can describe texture characteristics at different resolution levels. The gradient vector at each resolution level is used as the local texture feature. Based on the population statistics of gradient magnitude and direction in a local observation window, two novel texture measures, named as the Linear Gradient Magnitude Enhancement Measure (LGME) and the Linear Gradient Direction Enhancement Measure (LGDE), are developed to enhance different texture characteristics. In the LSM approach, the new scheme for the extraction of local texture features is based on performing transformations on the power spectra of local regions. The power spectrum of a local region is divided into a number of rings or wedges, and local spectral vectors are formed by summing the energy in these rings or wedges as vector elements. Two new texture measures, named as the Linear Radial Feature Enhancement Measure (LRFE) and the Linear Angular Feature Enhancement Measure (LAFE), are developed to highlight different texture characteristics. The MSF approach is based on the Multichannel Spatial Filtering Model (MSFM) for the human visual cortex. It is assumed in this approach that a texture can be characterised by its principal spatial frequency components, and that these components can be captured by a number of narrowband spatial filters. A new class of filters, called the Gaussian-Smoothed Fan (GSF) filters, is developed to perform channel filtering operations. The passband characteristic of these GSF filters is flatter than that of the Gabor filters, thus their bandwidths are inherently better defined. Computational algorithms based on these three new approaches are implemented and applied to a set of textured images. Good segmentation results are obtained, with more than 92% of the pixel population of each of the test images (derived from Brodatzs texture album) being correctly classified by all the three approaches. By comparison, the newly-developed GSF filters used in the MSF approach have an important advantage over the Gabor filters in that they can produce better defined boundaries between texture regions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.293331  DOI: Not available
Keywords: Computer-aided engineering
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