Neural networks for appearance-based 3-D object recognition

This paper presents a neural network based system for 3-D object recognition and localization. A new appearance-based approach is developed for recognition and pose estimation of 3-D objects from a single 2-D perspective view. Three-layer perceptrons are widely used in the whole image analysis process. A feature vector derived by a nonlinear principal component network is used to model object appearance. A neural classifier which receives the feature vector is then configured for recognition purpose. Object pose parameters are obtained by neural estimators trained on the same feature vector. Performance is tested on a data set consisting of more than 70 000 images of 14 objects. Comparative study with statistical approach is carried out.