Name
Title | ||
|---|---|---|
Performance evaluation of a statistical and a neural network model for nonrigid shape-based registration |
Abstract | ||
|---|---|---|
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 non-rigid 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IPTA.2016.7820990 | 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) |
Keywords | Field | DocType |
Shape registration,entropy,self-organising networks,probabilistic models | Computer vision,Active shape model,Pattern recognition,Computer science,Image processing,Probabilistic method,Hebbian theory,Solid modeling,Artificial intelligence,Probabilistic logic,Artificial neural network,Neural gas | Conference |
ISSN | ISBN | Citations |
2154-512X | 978-1-4673-8911-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandra Psarrou | 1 | 199 | 27.14 |
| Anastassia Angelopoulou | 2 | 102 | 21.29 |
| Markos Mentzelopoulos | 3 | 39 | 10.45 |
| José Garcia Rodriguez | 4 | 55 | 9.71 |