Cellular Automata for 3D Morphing of Volume Data

Abstract

Morphing involves the smooth transformation of one model, called the source to another, called the target. Several methods have been employed in this field both for two and three dimensional morphing. This paper performs morphing through the usage of cellular automata. The goal was to develop morphing algorithms that would minimize the need for both the user input and correspondence specification between source and the target. Two algorithms, the bacterial growth model and the core increment model have been designed and implemented in C++. Both algorithms utilize simple automata rules and are stable over dissimilar data. Results are presented that display the efficiency of the approach.