The influence of complexity on the detection of contours

Detecting objects in visual scenes is an important function of the visual system. Studies of contour detection and contour integration have answered many questions about the human visual system, but the role of contour geometry is not well understood. This thesis considers the problem of contour detection as a Bayesian decision problem. I begin by describing a generative model for natural contours. Bayesian arguments predict that simple contours (high probability under the generative model) should be easier to detect than more complex (lower probability) ones. In the case of open contours (Experiments 1 and 2), a complexity measure follows from a well-established contour-generating model. For closed contours, which have been far less studied, complexity measures require a more novel model that involves the shape of the region enclosed by the contour. The results of closed contours (Experiments 3 and 4) show that the complexity of the contour and the complexity of the shape of the bounded region jointly affect the ability of human observers to detect the contour in a noise field. In summary, contour integration has been treated mainly as a local grouping problem, but these results suggest that there is an important role for global factors in detection. Additionally, while the mathematical framework for measuring complexity was used here to study contour detection, it is also general enough to be useful in all areas of pattern detection where an explicit generative model is defined.Ph. D.Includes bibliographical referencesby John Wilde