29950481
Graphbots: Mobility in Discrete Spaces
Most previous theoretical work on motion planning has addressed the problem of path planning for geometrically simple robots in geometrically simple regions of Euclidean space (e.g., a planar region containing polygonal obstacles). In this paper we define a natural version of the motion planning problem in a graph theoretic setting. We establish conditions under which a “robot” or team of robots having a particular graph structure can move from any start location to any goal destination in a graph-structured space.
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.
17212106
Estimating relative vehicle motions in traffic scenes
Abstract Autonomous operation of a vehicle on a road calls for understanding of various events involving the motions of the vehicles in its vicinity. In this paper we show how a moving vehicle which is carrying a camera can estimate the relative motions of nearby vehicles. We show how to “smooth” the motion of the observing vehicle, i.e. to correct the image sequence so that transient motions (primarily rotations) resulting from bumps, etc. are removed and the sequence corresponds more closely to the sequence that would have been collected if the motion had been smooth. We also show how to detect the motions of nearby vehicles relative to the observing vehicle. We present results for several road image sequences which demonstrate the effectiveness of our approach.
20372308
Navigational Functionalities
203665251
Understanding Object Motion of Tools and Vehicles.
35830748
The function of documents
The purpose of a document is to facilitate the transfer of information from its author to its readers. It is the author's job to design the document so that the information it contains can be interpreted accurately and efficiently. To do this, the author can make use of a set of stylistic tools. In this paper, we introduce the concept of document functionality, which attempts to describe the roles of documents and their components in the process of transferring information. A functional description of a document provides insight into the type of the document, into its intended uses, and into strategies for automatic document interpretation and retrieval. To demonstrate these ideas, we define a taxonomy of functional document components and show how functional descriptions can be used to reverse-engineer the intentions of the author, to navigate in document space, and to provide important contextual information to aid in interpretation.
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.
46687960
Web Grammars
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.
1884675
Fuzzy end nodes in fuzzy graphs
We define a fuzzy end node in a fuzzy graph, and show that no node can be both a cut node and a fuzzy end node. In a fuzzy tree, every node is either a cut node or a fuzzy end node, but the converse is not true. We also show that any nontrivial fuzzy tree has at least two fuzzy end nodes, and we characterize fuzzy cycles that have no cut nodes or no fuzzy end nodes.
189885505
Digital geometry