Texture vs contours : explorations in the fields of contour detection and image processing

In this thesis it is explored how far a computer can perform the task of preserving the content of an image while removing totally or partially its texture information. This problem is studied in two application fields: contour detection and artistic imaging. Concerning contour detection, the state of the art is first reviewed. Several classes of local algorithms are considered, which are mainly based on differential analysis, statistical approaches, phase congruency, rank order filters, and combinations thereof. More sophisticated global methods based on the computation of contour saliency, perceptual grouping, relaxation labeling and active contours are considered too. Then, algorithms for contour detector are developed, which are inspired by several aspects of both low and high level of the human visual system. In particular, a biologically motivated multiresolution contour detector with Bayesian denoising and surround inhibition is proposed, which takes into account several low level aspects of human vision. Subsequently, it is extended by including higher level vision aspects, such as contour integration and edge grouping. This is done by means of a new morphological operator, called adaptive pseudo-dilation (APD), which uses context dependent structuring elements in order to identify long curvilinear structure in an edge map. As to artistic imaging, algorithms for the automatic generation of painterly images are proposed by removing texture while preserving edges or by replacing the natural texture of the input image with a synthetic one. Specifically, a simple and mathematically well-posed edge and corner preserving smoothing operator is presented as a generalization of the Kuwahara filter. Unlike many existing approaches for edge preserving smoothing, the concerned operator is able to add an interesting artistic effect to an input image. Then, a simple texture manipulation algorithm based on the theory of Glass Patterns is introduced. It generates and adds to an input image synthetic texture which simulates long curvilinear brushstrokes.