Developing a quantitative model of human preattentive vision
Richard W. Conners, Colin Ng · IEEE Transactions on Systems Man and Cybernetics · 1989
To develop robust computer vision system methodologies, ones that have a range of applicability, early vision operators capable of matching a level of human perceptual performance are required. This implies the need to develop operators that can perform a variety of image analysis tasks in a unified and consistent fashion. These image analysis tasks include finding boundaries between regions of uniform but different gray levels and textures. They also include gauging Gestalt grouping concepts such as uniformity and proximity, so that these concepts are incorporated into the segmentation process. Lastly, the operators should be able to gauge information that allows the characteristics of surfaces to be made explicit, e.g., so-called shape-from-shading and shape-from-texture methods. Developing robust methods for performing these tasks corresponds to developing a quantitative model of human preattentive vision. A basis for such a quantitative model is proposed that is contrary to current theories in computer vision but that seemingly provides a unified method for image analysis tasks and to explain a number of perceptual phenomena.>