This paper examines the problem of shape-based object recognition, and proposes a new approach, the alignment of pictorial descriptions. The first part of the paper reviews general approaches to visual object recognition, and divides these approaches into three broad classes: invariant properties methods, object decomposition methods, and alignment methods.

The second part presents the alignment method. In this approach the recognition process is divided into two stages. The first determines the transformation in space that is necessary to bring the viewed object into alignment with possible object models. This stage can proceed on the basis of minimal information, such as the object's dominant orientation, or a small number of corresponding feature points in the object and model. The second stage determines the model that best matches the viewed object. At this stage, the search is over all the possible object models, but not over their possible views, since the transformation has already been determined uniquely in the alignment stage.

The proposed alignment method also uses abstract description, but unlike structural description methods it uses them pictorially, rather than in symbolic structural descriptions.