We open our eyes and we "see" the world in all its color, brightness, and movement. Yet, we have great difficulties when trying to endow our machines with similar abilities. In this paper we shall describe recent developments in the theory of early vision which lead from the formulation of the motion problem as an illposed oneto its solution by minimizing certain "cost" functions. These cost or energy functions can be mapped onto simple analog and digital resistive networks. Thus, we shall see how the optical flow can be computed by injecting currents into resistive networks and recording the resulting stationary voltage distribution at each node. These networks can be implemented in cMOS VLSI circuits and represent plausible candidates for biological vision systems. APERTURE PROBLEM AND SMOOTHNESS ASSUMPTION In this study, we use intensity-based schemes for recovering motion.

This paper describes an approach to 2-dimensional object recognition. Complex-log conformal mapping is combined with a distributed associative memory to create a system which recognizes objects regardless of changes in rotation or scale. Recalled information from the memorized database is used to classify an object, reconstruct the memorized version of the object, and estimate the magnitude of changes in scale or rotation. The system response is resistant to moderate amounts of noise and occlusion. Several experiments, using real, gray scale images, are presented to show the feasibility of our approach. Introduction The challenge of the visual recognition problem stems from the fact that the projection of an object onto an image can be confounded by several dimensions of variability such as uncertain perspective, changing orientation and scale, sensor noise, occlusion, and nonuniform illumination.

AAAI is a society devoted to supporting the progress in science, technology and applications of AI. I thought I would use this occasion to share with you some of my thoughts on the recent advances in AI, the insights and theoretical foundations that have emerged out of the past thirty years of stable, sustained, systematic explorations in our field, and the grand challenges motivating the research in our field.

Review of Three-Dimensional Computer Vision

Subsequent chapters extend these techniques to the recognition of isolated curved objects using a graph model, interpretation of imperfect regions using a scene model, and recognition of multiple objects using Z-D object models. Shirai proceeds in a logical fashion two-dimensional pattern (2-D) recognition, pleted, the author concentrates on the from 2-D analysis, to line drawing such as character recognition 3-D world. The next two chapters deal interpretation, to the analysis of range or the recognition of silhouettes. The chapter on range data irregular objects using fractals and brief description of the field, the processing is a very strong one that superquadrics. This is followed by a reference source for researchers analysis, edge linking and following, chapter on three-dimensional description in 3-D vision.

A fundamental goal of computer vision is the development of systems capable of carrying out scene interpretation while taking into account all the available knowledge. In this article, we focus on how the interpretation task can be aided by the expected scene information (such as map knowledge), which, in most cases, would not be in registration with the perceived scene. The proposed approach is applicable to the interpretation of scenes with three-dimensional structures as long as it is possible to generate the equivalent two-dimensional orthogonal or perspective projections of the structures in the expected scene. The system is implemented as a two-panel, six-level blackboard and uses the Dempster-Shafer formalism to accomplish inexact reasoning in a hierarchical space. Inexact reasoning involves exploiting, at different levels of abstraction, any internal geometric consistencies in the data and between the data and the expected scene. As they are discovered, these consistencies are used to update the system's belief in associating a data element with a particular entity from the expected scene.

The Computing Research Laboratory (CRL) at New Mexico State University is a center for research in artificial intelligence and cognitive science. Specific areas of research include the human-computer interface, natural language understanding, connectionism, knowledge representation and reasoning, computer vision, robotics, and graph theory. This article describes the ongoing projects at CRL.

In R. Engelmore and T. Morgan (Eds.), Blackboard systems. Wokingham, England: Addison-Wesley

He then the Sempervirens Fund Anyone wishing Kvetoslav "Slava" Prazdny died in a completed a Ph.D. in Computer Science to make a tribute can do so in Slava's hang-gliding accident, Saturday, at the University of Essex. His name to The Sempervirens Fund, P 0 September 19th, 1987, in the California work reflected the marriage of these Drawer BE, Los Altos, California 94023 mountains. He is survived by his disciplines, as he strove to develop wife, Dagmar Dolan, and his 15 year formal computational models of old daughter Bronja Prazdny. During He was a member of American his prolific career, he had published Association for Advancement of Science, over 60 journal articles reporting American Association for Artificial research in human perception, Intelligence, The Cognitive Science stereo vision, image processing, Society, The Psychonomic Soci-robotics, perceptual reasoning and ety, International Society for Ecological learning, adaptive neural networks, Psychology, Society for Information and psychophysics. Display, SPIE, and a Fellow of the Slava derived his greatest pleasure New York Academy of Sciences.