PLANAR OBJECT RECOGNITION USING PROJECTIVE SHAPE REPRESENTATION

We describe a model based recognition system, called LEWIS, for the id entification of planar objects based on a projectively invariant repre sentation of shape. The advantages of this shape description include s imple model acquisition (direct from images), no need for camera calib ration or object pose computation, and the use of index functions. We describe the feature construction and recognition algorithms in detail and provide an analysis of the combinatorial advantages of using inde x functions. Index functions are used to select models from a model ba se and are constructed from projective invariants based on algebraic c urves and a canonical projective coordinate frame. Examples are given of object recognition from images of real scenes, with extensive objec t libraries. Successful recognition is demonstrated despite partial oc clusion by unmodelled objects, and realistic lighting conditions.