A Computational Study of the Selection of Eye Fixation Locations

Roger A. Browse and Marion G. Rodrigues

Abstract

Throughout the brief history of computational vision research, there has been a tendency towards increased consideration of the psychology and neurophysiology of human vision (eg. Marr, 1982). Despite this trend, machine vision systems rarely take into account the graded resolution retinal structure or the selection system responsible for repositioning the high resolution fovea. There are computational advantages to assuming exhaustive processing of uniform resolution images, but this simplification perpetuates the misconception that all locations need to be processed with equal scrutiny in order to permit effective vision.

In our research, we have developed computer vision systems which utilize graded resolution input, and select processing locations autonomously. There are three main reasons for taking this approach:

• It seems reasonable to expect that the processes of human vision, and the knowledge used in vision, are highly tuned to the selective use of the fovea, and to integration of separate glimpses of the world into a coherent percept. The computational modelling of such processes may well hold the key to the elusive goal of generalized machine vision.
• As mobile robotics becomes an important application of machine vision, it is clear that a solution will be necessary to the problem of aligning sensors with critical aspects of the environment. This issue of selection of processing location is even more serious in localized senses for robotics such as tactile perception (Browse and Lederman, 1986).
• Artificial intelligence research (of which computer vision is a sub-field) seeks out tasks which demonstrate the depth of human intelligent action. Though also accomplished in lower animals, we believe that the selection of fixation locations in humans exhibits a complex interaction amongst (1) response to stimulus cues (Yarbus, 1967), (2) goals of the perceiving system (eg. Loftus and Mackworth, 1967), and (3) status of interpretation processes, and as such is an ideal example of a task amenable to the techniques of expert systems and computational analysis in order to access the nature of intelligence in general.


Our research has centered on the development of a computational technique called propagation by which detailed understanding of image content available at the fovea may be used to enhance the course level understanding available peripherally and parafoveally (Browse, 1983; Rodrigues, 1986; Browse and Rodrigues, 1987). In support of this idea computational systems have been devised which operate in both domain-dependent and domain-independent modes. In addition, informal experiments have been carried out which support the idea that foveal based interpretation may be propagated to the periphery to create a percept of high resolution in a broader field of view than is actually available in a single fixation (Rodrigues, 1986). Given that one goal of vision is to produce high resolution interpretation in as broad an area as possible with a minimum number of fixations, one may examine peripheral locations for their potential to permit propagation, and use this as a measure in the selection of fixation locations (Browse, 1983; Browse and Rodrigues, 1987).

In this paper we explore the computational basis of such selectional criteria in a model which mediates between requirements for the development of a primal sketch (Marr, 1982), and the development of interpretations based on both local and global inputs (Navon, 1977).