Image processing systems have typically exhibited a high degree of application specificity. This makes it unlikely that any of the processing tasks for one application can be applied to another. Research has also found that such systems had not made use of knowledge about human perception although, it can be argued, such knowledge is independent of the application. This thesis takes the view that the structures which people perceive in pictures are of considerable importance to supporting picture-based human computer interaction. However, the design of systems which process pictures based on knowledge of human perception presents two major challenges. The first relates to determining the appropriate psychological knowledge that the computational modelling effort will be based upon. The second relates to the testing and evaluation of systems that aim to generate perceptually-valid structures, which poses significant problems because such systems are concerned with perceptual structures rather than semantic interpretations. An approach to developing picture processing systems is proposed that overcomes these problems. The approach represents a development strategy that exploits psychological theory in constructing image processing algorithms. The aim of these algorithms is to generate structures that match those perceived by humans. The approach enables new algorithms to be developed as additional psychological knowledge becomes available. Given appropriate training, users of such systems are able to see in images the structures that are generated by the system. The thesis presents the results of a study of perceptual and computational approaches to the processing of pictures. Motivations for and the development of a computational model based on perceptual grouping are described. A thorough evaluation of the computational model is performed which highlights both its value and limitations. Specific applications of the computational model in areas such as shape emergence in design, and pictorial databases, are then considered.