A new feature space trajectory (FST) description of 3-D distorted versions of an object or object part is considered in which different distorted object views are different vertices on the FST. Adjacent vertices are connected by straight lines. Different objects or object parts have different FSTs. For input data (an object or an object part), the FST closest to the input data denotes the class of the object and the closest line segment on that FST denotes the pose of the object or part. A neural net processor performs all needed distance calculations. Fundamental issues associated with this new representation of distorted objects are addressed. Processing a time sequence of input data and its pose estimation ability will be used in active vision systems. Its good generalization and performance on small training sets will be studied. Its ability to reject false (non object) inputs and to learn new inputs will be studied.
