We present a framework for incorporating prior information about high-probability
shapes in the process of contour extraction and object recognition in images. Here one studies shapes
as elements of an infinite-dimensional, non-linear, quotient space, and statistics of shapes are defined
and computed intrinsically using differential geometry of this shape space. Prior probability models
are constructed on the tangent bundle of shape space. Past work on boundary extraction has used
active curves driven by vector fields that were based on image gradients and roughness penalties.
The proposed method incorporates a priori knowledge of shapes in the form of gradient fields in
addition to the previously used image vector fields. Through experimental results, we demonstrate
the use of prior shape models in estimation of object boundaries, and their success in handling partial
obscuration and missing data. Furthermore, we describe the use of this framework in shape-based
object recognition or classification.