A model-based method for object recognition

A method is presented for using the high-level descriptions of objects (i.e. their models) to recognize them in an image. A complex object is viewed as a congregation of a set of component parts with simple shapes. The model of an object, therefore, describes the shapes of its component parts and states the geometrical relationships among those parts. This method also includes a recognition strategy which is a simple high-level description of how that object must be recognized. The shape descriptions of the parts are first used to extract a set of candidates for each part from the image. An object candidate is formed whenever a group of part candidates satisfy the model´s geometrical relationships. A model-based prediction and verification scheme is used to verify (or refute) the existence of the object candidates with low certainty. The scheme not only substantially increases the accuracy of recognition, but also makes it possible to detect and recognize partially occluded and camouflaged objects. Another advantage of the approach is that to recognize a new object, one only needs to define its model, and thus no programming is required. The user´s task is further simplified by the fact that each newly defined model is sufficient for recognizing a new category of objects