Data Driven Refinement of Active Shape Model Search
T.F. Cootes, Chris Taylor · 1996
Active Shape Models (ASMs) provide an efficient means of locating objects in images. By statistically modelling the shape variations in a class of objects they can rapidly and robustly fit to new examples. However, if an ASM does not represent all the shape variation exhibited by the object, the model may not be able to locate new examples accurately. This paper describes two complementary approaches to allowing additional freedom to the points which compromise the model, enabling them to fit to the image data more accurately. We present results for synthetic and real images and discuss how the methods can be used in an interactive 'bootstrap ' training scheme where problems with over-constrained models are particularly important.