The Design & Control of Visual Routines for the Computation of Simple Geometric Properties and Relations
The present work is based on the Visual Routine theory of Shimon Ullman. This theory holds that efficient visual perception is managed by first applying spatially parallel methods to an initial input image in order to construct the basic representation-maps of features within the image. Then, this phase is followed by the application of serial methods --- visual routines --- which are applied to the most salient items in these and other subsequently created maps.
Recent work in the visual routine tradition is reviewed, as well as relevant psychological work on preattentive and attentive vision. An analysis is made of the problem of devising a visual routine language for computing geometric properties and relations. The most useful basic representations to compute directly from a world of 2-D geometric shapes are determined. An argument is made for the case that an experimental program is required to establish which basic operations and which methods for controlling them will lead to the efficient computation of geometric properties and relations.
A description is given of an implemented computer system which can correctly compute, in images of simple 2-D geometric shapes, the properties vertical, horizontal, closed, and convex, and the relations inside, outside, touching, centred-in, connected, parallel, and being-part-of. The visual routines which compute these, the basic operations out of which the visual routines are composed, and the important logic which controls the goal-directed application of the routines to the image are all described in detail. The entire system is embedded in a Question-and-Answer system which is capable of answering questions of an image, such as "Find all the squares inside triangles" or "Find all the vertical bars outside of closed convex shapes." By asking many such questions about various test images, the effectiveness of the visual routines and their controlling logic is demonstrated.