Paper Info
Title
GAMEs: growing and adaptive meshes for fully automatic shape modeling and analysis.
Abstract
This paper presents a new framework for shape modeling and analysis, rooted in the pattern recognition theory and based on artificial neural networks. Growing and adaptive meshes (GAMEs) are introduced: GAMEs combine the self-organizing networks which grow when require (SONGWR) algorithm and the Kohonen’s self-organizing maps (SOMs) in order to build a mesh representation of a given shape and adapt it to instances of similar shapes. The modeling of a surface is seen as an unsupervised clustering problem, and tackled by using SONGWR (topology-learning phase). The point correspondence between point distribution models is granted by adapting the original model to other instances: the adaptation is seen as a classification task and performed accordingly to SOMs (topology-preserving phase). We thoroughly evaluated our method on challenging synthetic datasets, with different levels of noise and shape variations. Finally, we describe its application to the analysis of a challenging medical dataset. Our method proved to be reproducible, robust to noise, and capable of capturing real variations within and between groups of shapes.
Year
DOI
Venue
2007
10.1016/j.media.2007.03.006
Medical Image Analysis
Keywords
Field
DocType
Shape analysis,Automatic modeling,Pattern recognition,Artificial neural networks,Brain ventricles
Point distribution model,Polygon mesh,Similarity (geometry),Computer science,Self-organizing map,Artificial intelligence,Artificial neural network,Cluster analysis,Computer vision,Point correspondence,Pattern recognition,Machine learning,Shape analysis (digital geometry)
Journal
Volume
Issue
ISSN
11
3
1361-8415
Citations 
PageRank 
References 
16
0.98
13
Authors
6
Name
Order
Citations
PageRank
L. Ferrarini117419.00
Hans Olofsen2202.64
Walter M Palm3160.98
M A van Buchem418027.48
Johan H C Reiber5949.49
Faiza Admiraal-Behloul61239.04