Abstract
Texture segmentation and classification are major issues in computer vision that have not yet been fully explored in the framework of irregularly sample data. Unlike well known image restoration techniques, many analysis methods are mainly concerned with obtaining data representation in a feature space and developing effective distance measures for image discrimination, with no interest in reconstructing back the image from the feature space. On avoiding the later, simpler approaches to image analysis may be developed. This thesis constitutes a research on texture analysis for feature extraction, classification and segmentation of irregularly sampled images. In a real scenario, irregularity in the sampling pattern may be a matter of either an inherent problem property, such as in gathering data in geosciences, or a deliberate design, such as retinomorphic sampling. To extend our results to either case, we introduced irregular sampling by investigating the spatial distributions of three sampling patterns. The first pattern is generated from the uniform distribution. The other two sampling patterns consist of inhomogeniously distributed data, with denser concentration towards the middle, to imitate the biological vision paradigm. One follows the Gaussian distribution and the other the log-pollar distribution. In addition, we extend two of the major approaches in image analysis to irregularly sampled data. The first, co-occurrence matrices, is a statistical approach, which is applied to texture classification. The second approach, Gabor analysis, is extended for unsupervised texture segmentation by using the Fourier transform for non-uniformly sampled data. Following a new trend which looks to enhance computer vision with the functionality of human vision, biologically inspired processing was progressively incorporated into our algorithms to the point of proposing a biological paradigm for image segmentation. Finally, we investigate the use of Gabor analysis for 3D irregularly sampled data, and in particular for the segmentation of volumetric seismic data obtained by the oil industry. The results, however, of this study are rather disappointing.