The colorimetric segmentation of textured digital images
This study approaches the problem of colour image segmentation as a pattern recognition task. This leads to the problem being broken down into two component parts: feature extraction and classification algorithms. Measures to enable the objective assessment of segmentation algorithms are considered. In keeping with this pattern-recognition based philosophy, the issue of texture is approached by a consideration of features, follwed by experimentation based on classification. Techniques based on Gabor filters and fractal dimension are compared. Also colour is considered in terms of its features, and a systematic exploration of colour features in undertaken. The technique for assessing colour features is also used as the basis for a segmentation algorithm that can be used for combining colour and texture. In this study, several novel techniques are presented and discussed. Firstly a methodology for the judgement of image segmentation algorithms. Secondly a technique for segmenting images using fractal dimension is presented, including a novel application of information dimension. thirdly an objective assessment of colour spaces using the techniques discussed as the first point of this study. Finally strategies for combining colour and texture in the segmentation process are discussed and techniques presented.