J. J. Edwards

Bio: J. J. Edwards is an academic researcher from University of Tennessee. The author has contributed to research in topics: Context-sensitive grammar & Tree-adjoining grammar. The author has an hindex of 1, co-authored 1 publications receiving 32 citations.

An algorithm for the inference of tree grammars

Abstract: An algorithm for the inference of tree grammars from sample trees is presented. The procedure, which is based on the properties of self-embedding and regularity, produces a reduced tree grammar capable of generating all the samples used in the inference process as well as other trees similar in structure. The characteristics of the algorithm are illustrated by experimental results.

Inductive Inference: Theory and Methods

Abstract: There has been a great deal of theoretical and experimental work in computer science on inductive inference systems, that is, systems that try to infer general rules from examples. However, a complete and applicable theory of such systems is still a distant goal. This survey highlights and explains the main ideas that have been developed in the study of inductive inference, with special emphasis on the relations between the general theory and the specific algorithms and implementations. 154 references.

Tree automata

Abstract: This is a reissue of the book Tree Automata by F G\'ecseg and M Steinby originally published in 1984 by Akad\'emiai Kiad\'o, Budapest Some mistakes have been corrected and a few obscure passages have been clarified Moreover, some more recent contributions and current lines of research are reviewed in an appendix that also contains several new references

Syntactic Methods in Pattern Recognition

Abstract: This paper reviews concepts of syntactic pattern recognition with emphasis on syntax-directed translations. Examples of recent work on hybrid and hierarchical systems are cited. There is a brief discussion of active research areas which include methods of grammatical inference, probabilistic systems, approaches to error correction, and techniques of combining syntax with semantics.

Computer Vision Techniques for Industrial Applications and Robot Control

Abstract: robots that \"see\" and \"feel\" can perform more complex tasks, industry has employed various computer vision techniques to enhance the abilities of intelligent machines. The recent widespread interest in robotics and automation in the US originates from American industry's most fundamental problem: a staggering drop in productivity. From 1947 to 1965, US productivity increased at an average rate of 3.4 percent a year. The growth rate decreased to 2.3 percent in the following decade, then dropped to below one percent in the late 1970's and down to-0.9 percent in 1980. Japan's productivity growth, the contrasting example most often cited in the literature, has been climbing at an average annual rate of about 7.3 percent. ' Although there are many ways to influence manufacturing productivity and product quality-regulatory, fiscal, and social-the emphasis in the following discussion is technological. In particular, we are interested in the computer vision aspects of industrial inspection and robot control. The principal motivation behind computer vision is increased flexibility and lower cost. The use of sensing technology to endow-a machine with a greater degree of \"intelligence\" in dealing with its environment is receiving increased attention. A robot that can \"see\" and \"feel\" should be easier to train in the performance of complex tasks while at the same time requiring less stringent control mechanisms than preprogrammed machines. A sensory, trainable system is also adaptable to a much larger variety of tasks, thus achieving a degree of universality that ultimately translates into lower production and maintenance costs. The computer vision process can be divided into five principal areas: sensing, segmentation, description, recognition , and interpretation. These categories are suggested to a large extent by the way computer vision systems are generally implemented. It is not implied that human vision and reasoning can be so neatly subdivided nor that these processes are carried out independently of each other. For instance, we can logically assume that recognition and interpretation are highly interrelated functions in a human. These relationships, however, are not yet understood to the point where they can be mod-eled analytically. Thus, the subdivision of functions discussed below can be viewed as a practical (albeit limited) approach for implementing state-of-the-art computer vision systems, given our level of understanding and the analytical tools currently available in this field. Visual sensing Imaging devices. Visual information is converted to electrical signals by visual sensors. The most commonly used visual sensors are vidicon cameras …