Computational Investigation of Feature Extraction and Image Organization

This dissertation investigates computational issues of feature extraction and image organization at different levels. Boundary detection and segmentation are studied extensively for range, intensity, and texture images. We developed a range image segmentation system using a LEGION network based on a similarity measure consisting of estimated surface properties. We propose a nonlinear smoothing algorithm through local coupling structures, which exhibits distinctive temporal properties such as quick convergence. We propose spectral histograms, consisting of marginal distributions of a chosen bank of filters, as a generic feature vector based on that early steps of human visual processing can be modeled using local spatial/frequency representations. Spectral histograms are studied extensively in texture modeling, classification, and segmentation. Experiments in texture synthesis and classification demonstrate that spectral histograms provide a sufficient and unified feature in capturing perceptual appearance of textures. Spectral histograms improve significantly the classification performance for challenging texture images. We also propose a model for texture discrimination based on spectral histograms which matches existing psychophysical data. A new energy functional for image segmentation is proposed. With given regional features, an iterative and deterministic algorithm for segmentation is derived. Satisfactory results