Report: 1988 NSF Range Image Understanding Workshop

Abstract

Most computer vision research has concentrated on using digitized grayscale intensity images as sensor data. It has proven to be extraordinarily difficult to program computers to understand and describe these images in a general purpose way. One important problem is that digitized intensity images are rectangular arrays of numbers which indicate the brightness at individual points on a regularly spaced rectangular grid and contain no explicit information that is directly usable in depth perception. Yet human beings are able to correctly infer depth relationships quickly and easily among intensity image regions whereas automatic inference of such depth relationships has proven to be remarkably complex. In fact, many famous visual illusions, such as Kanizsa’s triangle, vividly demonstrate that humans impose 3-D surface structure on images to interpret them. Computer vision researchers recognized the importance of surfaces in the understanding of images. The popularity of shape from … approaches in the last decade is the result of this recognition.