Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

Final Evaluation of the Heart of Teesdale Landscape Partnership

A report on behalf of Durham County Council to the Heritage Lottery Funded in relation to the £2.7m Heart of Teesdale Landscape Partnership. The evaluation provides an independent assessment of what the Partnership has delivered (its outputs), what the benefits (outcomes) have been and what lasting impact it will have made (its legacy) for natural and cultural heritage and for local communities and visitors. This Report also considers what may not have worked so well and the lessons that may be drawn for the future.