Effective image segmentation of a digitized scene into a set of recognizable objects requires the development of sophisticated scene analysis algorithms. Progress in this area has been made through the development of a statistical-based deformable model that improves upon existing point distribution models (PDMs) for boundary-based object segmentation. Existing PDM boundary finding techniques often suffer from the shortcoming that global shape and gray-level information are treated independently during boundary optimization. A new deformable model algorithm is under development in which the objective function used during optimization of the boundary encompasses several important characteristics. Most importantly the objective function includes both shape and gray-level characteristics, so optimization occurs with respect to both pieces of information simultaneously. This new algorithm has been applied to geometric test images and a simple industrial-type scene for which results are presented.
|Number of pages||6|
|Journal||Proceedings - IEEE International Conference on Robotics and Automation|
|State||Published - Jan 1 2002|
|Event||2002 IEEE International Conference on Robotics adn Automation - Washington, DC, United States|
Duration: May 11 2002 → May 15 2002