3D Object Recognition and Visualization on the Web

This research deals with state-of-the-art novel ideas in high level visualization, understanding and interpretation of line-drawing images of 3D patterns, including articulated objects. A new structural approach using linear combination and fast two-pass parallel matching techniques is presented. It is aimed at learning, representing, visualizing, and interpreting 2D line drawings as 3D objects, with only very few learning samples. It solves one of the basic concerns in diffusion tomography complexities, i.e. patterns can be reconstructed through fewer projections, and 3D objects can be recognized by a few learning samples views. It can also strengthen advantages of current key methods while overcome their drawbacks. Furthermore, it will be able to distinguish objects with very similar properties and is more accurate than other methods in the literature. In addition, an expsrimental system using JAVA and user-friendly interative platform has been established for testing large volume of image data in virtual environment on the web, for learning, and recognition. Several illustrative examples are demonstrated, including learning, recognizing, visualization and interpretation of 3D line drawing polyhedral objects. Finally, future research topics are outlined.