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Title: Colour texture analysis in machine vision
Author: Tan, Tele Seng Chu
ISNI:       0000 0001 3497 8347
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 1993
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Texture is an important cue in vision and has been analysed in its own right for the last three decades by researchers in psychophysics as well as computer vision and image processing. The three important early vision roles that texture analysis can play include texture classification, texture description and texture segmentation, all of which are pre-requisites for higher levels of analysis namely image interpretation and understanding. It is well known that colour can aid the human vision system in the analysis of many visual phenomena like shape, motion and texture. This notion coupled with the recent advent of fast computing hardware and the widespread availability of good quality colour cameras, digitizers, and monitors had created a new pathway for improving the performance of traditional grey level texture analysis schemes by incorporating colour information. In this thesis, the problem of statistical colour texture analysis is addressed. As a pre-requisite to analysing colour textures a review of the main texture analysis techniques available in the open literature is presented. The local linear transform technique is singled out as the main texture analysis scheme to be used throughout the course of the work. This technique boasts of several advantages; compactness in texture measurement, implementation simplicity, and suitability for stochastic or random texture representation. It is found that the structural property of the local linear transform for texture measurement resembles that of the energy measures based on Gabor functions. This has resulted in the possibility of emulating the latter texture extraction process by a set of quadrature filters like in the case of Gabor filtering. The motivation here is the speed improvement in the computation of the texture representation as the filtering process can be accelerated by Fast Fourier Transform. But unfortunately the number of quadrature filters needed to successfully emulate the local linear transform measures has been found to be unexpectedly large making the FFT implementation very uneconomical to realise. Two colour texture analysis schemes are developed. The first method advocates the dual transformation of the colour input image which requires the initial transformation of the tristimulus values into several colour co-ordinate systems and tlien extracting texture attributes from these transformed component images. The performance of these features is measured as the percentage of correct classification. Feature behaviour under illumination intensity variation will be investigated. The second approach harnesses the texture and colour information separately in an attempt to eliminate redundant or highly correlated features that are usually associated with the first approach. The colour histogram is used as an image model from which a colour representation scheme of this method can be derived. An efficient and fast way of coding the colour histogram by approximate principal component analysis is developed here. This reduces both the memory requirement for histogram storage and computation time for colour features by a factor N82/9, where Ng is the total number of grey level of each channel. It is shown that features derived from the latter approach perform better in experiments involving colour granite classification. These colour features are shown to be more robust to illumination intensity changes than colour texture features computed from the individual transformed channels. Further to this, the overall size of the colour texture feature dimension of the second approach is considerable lower than the first approach. The encouraging results gathered here indicate the usefulness of a hybrid form of multi-variate feature measurement of colour texture using separately, colour and texture attributes.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Pattern recognition & image processing