Showing papers by "David G. Lowe published in 1985"

Co-operative structuring of information: the representation of reasoning and debate

Abstract: Interactive computer networks create new opportunities for the co-operative structuring of information which would be impossible to implement within a paper-based medium. Methods are described for co-operatively indexing, evaluating and synthesizing information through well-specified interactions by many users with a common database. These methods are based on the use of a structured representation for reasoning and debate, in which conclusions are explicitly justified or negated by individual items of evidence. Through debates on the accuracy of information and on aspects of the structures themselves, a large number of users can co-operatively rank all available items of information in terms of significance and relevance to each topic. Individual users can then choose the depth to which they wish to examine these structures for the purposes at hand. The function of this debate is not to arrive at specific conclusions, but rather to collect and order the best available evidence on each topic. By representing the basic structure of each field of knowledge, the system would function at one level as an information retrieval system in which documents are indexed, evaluated and ranked in the context of each topic of inquiry. At a deeper level, the system would encode knowledge in the argument structures themselves. This use of an interactive system for structuring information offers further opportunities for improving the accuracy, integration and accessibility of information.

The Recovery of Three-Dimensional Structure from Image Curves

Abstract: A class of inferences is described which allows the recovery of three-dimensional structures from the two-dimensional curves in an image. Unlike most previous methods, these inferences do not require restrictive assumptions or prior knowledge regarding the scene. They are based on the assumption that the camera viewpoint and the positions of the illumination sources are independent of the objects in the scene. From these independence assumptions, it can be shown that many potential interpretations of image curves are highly improbable. By eliminating these improbable interpretations it is possible to segment the image into sets of related image features and derive many three-space relations.

Visual recognition from spatial correspondence and perceptual organization

Abstract: Depth reconstruction from the two-dimensional image plays an important role in certain visual tasks and has been a major focus of computer vision research. However, in this paper we argue that most instances of recognition in human and machine vision can best be performed without the preliminary reconstruction of depth. Three other mechanisms are described that can be used to bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization can be used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Secondly, evidential reasoning can be used to combine evidence from these groupings and other sources of information to reduce the size of the search-space during model-based matching. Finally, a process of spatial correspondence can be used to bring the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. These methods have been combined in an experimental computer vision system named SCERPO. This system has demonstrated the use of these methods for the recognition of objects from unknown viewpoints in single gray-scale images.

Measuring the Significance of Image Relations

Abstract: PERCEPTUAL ORGANIZATION can be viewed as a process that assigns a degree of significance to each potential grouping of image features. Our goal in this chapter will be to take a unified view of the many grouping phenomena by examining the underlying principles for measuring the significance of each grouping. As was described in Chapter 1, perceptual groupings are useful to the extent that they are unlikely to have arisen by accident of viewpoint or position, and therefore are likely to reflect meaningful structure of the scene. Our basic argument will be that certain image relations are carriers of statistical information indicating that they are non-accidental in origin, and that this degree of non-accidentalness forms the basis for assigning degrees of significance. Note that there are an infinite number of different types of relations that could be considered (e.g., “all pairs of straight line segments at N degrees relative orientation,” for any given N), and a combinatorial number of sets of elements to consider in any given image. Only a small subset of these possible relations are likely to be of any significance or are worth the effort required for detection.