Nonlinear features for classification and pose estimation of machined parts from single views

A new nonlinear feature extraction method is presented for classification and pose estimation of objects from single views. The feature extraction method is called the maximum representation and discrimination feature (MRDF) method. The nonlinear MRDF transformations to use are obtained in closed form, and offer significant advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We consider MRDFs on image data, provide a new a-stage nonlinear MRDF solution, and show it specializes to well-known linear and nonlinear image processing transforms under certain conditions. We show the use of the MRDF in estimating the class and pose of images of rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show new results with better classification and pose estimation accuracy than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.