This paper describes a program for extracting an accurate outline of a man's head from a digital picture. The program accepts as input digital, grey scale pictures containing people standing in front of various backgrounds.

We have written a program which will recognize a range of objects including a cup, a wedge, a hammer, a pencil, and a pair of spectacles. A visual image, represented by a 64.x 64 array of light levels, is first partitioned into connected regions. These regions are chosen to have welldefined edges. Having chosen the regions, the program then computes properties of and relations between regions. Properties include shape as defined by Fourier analysis of the s--tfr equation of the bounding curve. A typical relation between regions is the degree of adjacency. Finally, the program matches the actual relational structure of the regions of the picture with ideal relational structures representing various objects, using a heuristic search procedure, and selects that object whose relational structure best matches the actual picture. INTRODUCTION In November 1969, a Mark i robot device (Barrow and Salter 1970) was connected on-line to the ICI, 4130 computer of the Department of Machine Intelligence and Perception, University of Edinburgh.

We describe the analysis of visual scenes consisting of black on white drawings formed with curved lines, depicting familiar objects and forms: houses, trees, persons, and so on; for instance, drawings found in coloring books. The goal of such analysis is to recognize (by computer) such forms and shapes when present in the input scene; that is, to name (correctly) as many parts of the scene as possible: finger, hand, girl, dance, and so on. Complications occur because each input scene contains several such objects, partially occluding each other and in varying degrees of orientation, size, and so on. The analysis of these line drawings is an instance of'the context problem', which can be stated as'given that a set (a scene) is formed by components that locally (by their shape) are ambiguous, because each shape allows a component to have one of several possible values (a circle can be sun, ball, eye, hole) or meanings, can we make use of context information stated in the form of models, in order to single out for each component a value in such manner that the whole set (scene) is consistent or makes global sense?' Thus, shape drastically limits the values that a component could have, and further disambiguation is possible only by using global information (derived from several components and their inter-relations or inter-connections) under the assumption that the scene as a whole is meaningful. This paper proposes a way to solve'the context problem' in the paradigm of coloring book drawings. We have not implemented this approach; indeed, a purpose of this paper is to collect criticisms and suggestions. INTRODUCTION 1.1 Statement of the problem An input picture is read into a computer. We would like to analyze it. The input picture The input picture consists of a line drawing (black curved thin lines on white paper) containing familiar objects [figure 1(a)]; one could think of drawings in coloring books for children. The objects forming the picture should be drawn correctly and accurately: no intentional distortions, caricatures, or humanizations of animals (figure 2) will be allowed. Figure 2. Line drawings containing distortions, caricatures, comic strips, humanized animals, and so on, will not be accepted. Thus, it can be said that the class of input pictures we want to analyze is that found in coloring books, except distortions. We could think of a person looking at the input data [figures 1(a) or 1(b)] and saying: there is a straight line from point (30, 40) to point (67, --18.5), The input picture [figure 1(a)] is stored initially in the memory of the computer as a collection of black points (specified by their two-dimensional coordinates) closely spaced along each black line [figure 1(b)]. We will assume that: (a) The points are uniformly spaced along the lines of the drawing.

As a minimum we should at least be better able to decide when sentences from a'polyhedral language' or'smooth language' are

The distinction between analytic and empirical statements obviously has some potential philosophical overtones. We hope to avoid most of them by formulating the distinction in terms of an assumption on the verbs believe, know, and so on, rather than in terms of philosophical considerations. The predicate'Holds' The connectedness' of our set of functions and relations requires that there should be some unary relation'Holds' such that Holds(m

The problem we consider in this paper is that of discovering formal rules which will enable us to decide when a question posed in English can be answered on the basis of one or more declarative English sentences. To illustrate how this may be done in very simple cases we give rules which translate certain declarative sentences and questions involving the quantifiers'some', 'every', 'any', and'no' into a modified first-order predicate calculus, and answer the questions by comparing their translated forms with those of the declaratives. We suggest that in order to capture the meanings of more complex sentences it will be necessary to go beyond the first-order predicate calculus, to a notation in which the scope of words other than quantifiers and negations is clearly indicated. We conclude by describing a notational form for connected sentences, which seems to be a natural extension of Chomsky's'deep structures'. INTRODUCTION In this paper we shall consider the problem of when an English sentence, or a series of sentences, provides enough information to answer a question, also posed in English.

The Genetics Counselor is a computer program, written in LISP, designed to handle problems of medical genetics counseling. It is an attempt to apply the methods of artificial intelligence research to medical diagnostic problems. The program attempts to map the data space of a family-tree structure into the hypothesis space of classical Mendelian genetics by use of a heuristic search. The input data are the family members along with their children (or parents), and phenotype. The program generates a family tree and searches for consanguinity.

Yet the attention given to the program as an application of artificial intelligence research has tended to obscure the more general concerns of the project investigators.

In order to be able to view the situation objectively we feel that it would be useful to preface this with a historical review of the development of ideas in this twenty-year-old field. By considering the most important ideas and techniques that are employed in the (currently) best program available, we hope to convince the reader that progress has been very slow despite the multiplicity of programs (and their associated literature) which have appeared since 1950. HISTORICAL REVIEW The most important paper that has appeared on the subject of computer chess is one written by Claude Shannon in 1948 and published two years later (Shannon 1950). Shannon's paper does not describe an actual program, but offers many suggestions for those who are interested in writing one. In this respect Shannon's paper may be compared with one by Jack Good which was also full of sound ideas which could well be included in a successful chess program (Good 1967). Shannon stressed the importance of having a good evaluation function. The features which he considers necessary for inclusion in the evaluation function included material, mobility, five aspects of pawn-structure, four of the positions of pieces, and four of commitments of pieces, attacks and options. He appreciated that such an evaluation function should be used only in the middle-game, and that different principles applied to the opening and endgame phases of chess. He suggested that the values of the coefficients of the function should be determined by'some experimental procedure', and the fact that this statement has never been followed in practice is very surprising.

K.A.PATON 411 24 Centromere finding: some shape descriptors for small chromosome outlines.