Energy matching based on deformable templates

Snakes, or active contours, have been previously used in computer vision applications to locate and identify objects. However, problems associated with initialization, poor convergence to boundary concavities and high computational complexity, have limited their utility. We present a scheme for object localization and classification based on inexact description of the shape. The approach is based on deformable templates and was tested and proven be very robust with respect to changes in object scale, position and orientation as well as noise and local deformations of shape. A coarse-to-fine algorithm was developed to reduce the computational complexity and achieve efficient implementation. Results of applying the algorithm for automatic identification and localization of industrial parts are presented.