View-based Virtual Learning and Recognition of 3D Object Using View Model Obtained by Motion-Stereo

摘要

View-based approach for learning and recognition of 3D object and its pose detection was proved to be affective and efficient, except its high learning cost. In this research, we propose a virtual learning ap- proach which generates learning samples of views of an object from its 3D view model obtained by motion-stereo method. From the generated learning sample views, features o high-order autocorrelation are extracted, and dis- criminant feature spaces for object recognition and pose detection are built. Recognition experiments on real objects are carried out to show the effective- Ness of the proposed method.