Automatic processing of shape information requires the selection of a representation form of shape. Shape modeling is based on a choice of shape type, which is the joint specification of representation form and a set of operations. In shape applications, such as shape design and shape optimization, it is not sufficient to maintain a static shape type. Depending on the specific needs during the application, i.e. depending on the modeling context, the appropriate shape type might be continuously varying. Programmed systems can handle static shape types relatively well. However, to support dynamic shape typing a number of fundamental problems need to be understood and solved. An approach to dynamic typing of freeform shapes is presented. Theoretical issues will be described and some concrete examples and initial experimental results will be presented.
Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling
