40896374
Qualitative description of camera motion from histograms of normal flow
If we histogram the normal flow vectors in images of a scene viewed by a moving observer, we can use the time-varying histogram to derive qualitative information about the observer's motion-for example, whether it is (primarily) translational or rotational, and whether the direction of translation or axis of rotation is (roughly) parallel or perpendicular to the camera axis. This is illustrated using flow histogram obtained from a variety of real image sequences.
20372308
Navigational Functionalities
11867822
Landmarks in Graphs
Navigation can be studied in a graph-structured framework in which the navigating agent (which we shall assume to be a point robot) moves from node to node of a “graph space”. The robot can locate itself by the presence of distinctively labeled “landmark” nodes in the graph space. For a robot navigating in Euclidean space, visual detection of a distinctive landmark provides information about the direction to the landmark, and allows the robot to determine its position by triangulation. On a graph, however, there is neither the concept of direction nor that of visibility. Instead, we shall assume that a robot navigating on a graph can sense the distances to a set of landmarks. 
 
Evidently, if the robot knows its distances to a sufficiently large set of landmarks, its position on the graph is uniquely determined. This suggests the following problem: given a graph, what are the fewest number of landmarks needed, and where should they be located, so that the distances to the landmarks uniquely determine the robot's position on the graph? This is actually a classical problem about metric spaces. A minimum set of landmarks which uniquely determine the robot's position is called a “metric basis”, and the minimum number of landmarks is called the “metric dimension” of the graph. In this paper we present some results about this problem. Our main new results are that the metric dimension of a graph with n nodes can be approximated in polynomial time within a factor of O(log n), and some properties of graphs with metric dimension two.
206614039
Array Grammars
This tutorial briefly introduces array grammars, in which rewriting rules involve replacement of subarrays by subarrays. Strings can be regarded as one-dimensional arrays; but as we shall see, in higher dimensions special problems arise. In this tutorial we consider only the twodimensional case, but the extension to arbitrary dimensions is straightforward. A more detailed review of array grammars of various types, as well as the corresponding types of array acceptors, can be found in [1]. Array grammars (like string grammars) can be regarded as a class of graph grammars, but it seems more natural to regard them as rewriting systems of a special type.
1024289
Strong arcs in fuzzy graphs
An arc of a fuzzy graph is called strong if its weight is at least as great as the strength of connectedness of its end nodes when it is deleted. An arc is strong iff its weight is equal to the strength of connectedness of its end nodes. A bridge is strong, but a strong arc need not be a bridge. An arc of maximum weight is strong, but a strong arc need not have maximum weight. In a connected graph, there is a strong path (a path consisting of strong arcs) between any two nodes. A fuzzy graph is a fuzzy tree iff there is a unique strong path between any two of its nodes. In a fuzzy tree, an arc is strong iff it is a bridge, and a strong path between two nodes is a path of maximum strength between them.
441278
Computer Vision
The field of computer vision is devoted to discovering algorithms, data representations, and computer architectures that embody the principles underlying visual capabilities. This article describes how the field of computer (and robot) vision has evolved, particularly over the past 20 years, and introduces its central methodological paradigms.
16184629
Digital straightness
A digital arc is called ‘straight’ if it is the digitization of a straight line segment. Since the concept of digital straightness was introduced in the mid-1970’s, dozens of papers on the subject have appeared; many characterizations of digital straight lines have been formulated, and many algorithms for determining whether a digital arc is straight have been defined. This paper reviews the literature on digital straightness and discusses its relationship to other concepts of geometry, the theory of words, and number theory.