|M.Sc Student||Pechuk Michael|
|Subject||Function-based Object Recognition|
|Department||Department of Computer Science||Supervisor||Professor Ehud Rivlin|
|Full Thesis text|
We propose a novel scheme for using supervised learning for function based classification of objects in 3D images. During the learning process, a generic multi-level hierarchical description of object classes is constructed. The object classes are described in terms of functional components. The multi-level hierarchy is designed and constructed using a large set of signature-based reasoning and grading mechanism. This set employs likelihood functions that are built as radial-based functions from the histograms of the object instances. During classification, a probabilistic matching measure is used to search through a finite graph to find the best assignment of geometric parts to the functional structures of each class. An object is assigned to a class providing the highest matching value. Reuse of functional primitives in different classes enables easy expansion to new categories. We tested the proposed scheme on a database of about one thousand different 3D objects. The proposed scheme achieved high classification accuracy while using small training sets.