Connectionist approach to associative recall of 3D objects

A connectionist model that can store a sequence of various views of a three-dimensional object is presented. The model consists of four components, each of which is realized as a network or neuronlike elements. The model can store several 3-D objects in the same network in an overlapped manner. If the model is shown one view of a stored 3-D object, then a sequence of other views is recalled one by one. Since the storage is based on an associative matrix, the recall is possible even from an incomplete view. Instead of raw image data, a set of extracted features is stored for efficient storage