The capabilities for representing and reasoning about three-dimensional (3-D) objects are essential for knowledge-based, 3-D photointerpretation systems that combine domain knowledge with image processing, as demonstrated by 3- D Mosaic and ACRONYM. A practical framework for geometric representation and reasoning must incorporate projections between a two-dimensional (2-D) image and a 3-D scene, shape and surface properties of objects, and geometric and topological relationships between objects. In addition, it should allow easy modification and extension of the system's domain knowledge and be flexible enough to organize its reasoning efficiently to take advantage of the current available knowledge. This system uses frames to represent objects such as buildings and walls, geometric features such as lines and planes, and geometric relationships such as parallel lines.

Kvetoslav "Slava" Prazdny, who died September 19, 1987 in a hang-gliding accident in the California mountains, was recognized internationally as an expert in many aspects of human and machine perception. He had published over 60 articles reporting research in human perception, stereo vision, image processing, robotics, perceptual reasoning and learning, adaptive neural networks, and psychophysics. A redwood tree in Big Basin State Park is dedicated in his memory.

An important task in postal automation technology is determining the position and orientation of the destination address block in the image of a mail piece such as a letter, magazine, or parcel. The corresponding subimage is then presented to a human operator or a machine reader (optical character reader) that can read the zip code and, if necessary, other address information and direct the mail piece to the appropriate sorting bin. Analysis of physical characteristics of mail pieces indicates that in order to automate the address finding task, several different image analysis operations are necessary. Some examples are locating a rectangular white address label on a multicolor background, progressively grouping characters into text lines and text lines into text blocks, eliminating candidate regions by specialized detectors (for example, detecting regions such as postage stamps), and identifying handwritten regions. Described here are several operations, their utility as predicted by statistics of mail piece characteristics, and the results of applying the operations to a task set of mail piece images. A problem-solving architecture based on the blackboard model of problem solving for appropriately invoking the tools and combining their results is described.

The interpretation of images of postal mail pieces is The Vision Group the domain of this investigation. Our efforts have included It is becoming increasingly important for vision researchers the development of various operators for visual data processing in diverse fields to interact, and the Vision Group at SUNY and image segmentation. The invocation of these Buffalo was formed to facilitate that interaction Current routines and the interpretation of the information they return membership includes 25 faculty and 25 students from 10 is determined by a control structure that uses a variant departments (computer science, electrical and computer of relaxation combined with a rule-based methodology.

In A Hanson and E. Riseman (eds.), Computer Vision Systems, pp. 3-26, New York: Academic Press, 1978

Computer Graphics and Image Processing 6:221-276

Hierarchies are observed in the levels of description used in image understanding along a few dimensions: processing unit, detail, composition and scene/view distinction. Emphasis is placed on the importance of explicitly handling the hierarchies both in representing knowledge and in using it. A scheme of "knowledge block" representation which is structured along the processing-unit hierarchy is also presented. I. INTRODUCTION Image Understanding System(IUS) constructs a description of the scene being viewed from an array of image sensory data: intensity, color, and sometimes range data. Image understanding is best characterized by description, whereas pattern recognition by classification, and image processing by image output.

IEEE Transactions on Systems, Man, and Cybernetics SMC-3:610-621