Recognition and Learning with Polymorphic Structural Components

We address the problem of describing, recognizing, and learning generic, freeform objects in real-world scenes. For this purpose, we have developed a hybrid appearance-based approach where objects are encoded as loose collections of parts and relations between neighboring parts. The key features of this approach are: part decomposition based on local structure segmentation derived from multi-scale wavelet filters, flexible and efficient recognition by combining weak structural constraints, and learning and generalization of generic object categories (with possibly large intra-class variability) from real examples. 1 Recognizing and Classifying 3D Objects Recognizing three-dimensional objects under different viewing and lighting conditions is a traditional problem in computer vision. The difficulty of the problem depends upon many factors including: types of objects, number of classes, inter- and intraclass variability, number of objects in a scene, background complexity, ammou..

Recognition and learning with polymorphic structural components

Generally all such recognition approaches assume a noiseless, pre-segmented image with all parts of the object visible in every view, i.e. no self-occlusion, and that models of all objects have been given a priori. Even with these restrictions there has been only limited success for small datasets e.g. Bergevin and Levine's PARVO system[1] which uses volumetric geons to successfully discriminate among 23 objects. The subgraph isomorphism problem forces them to compare only those models which contain the same geons as the view, preventing its use in cases of views with missing parts