Image Analysis With The Septree Data Structure
Gregory A. Baraghimian · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
This paper surveys hierarchical algorithms used in the analysis of image data under hexagonal planar decomposition. Part of the motivation for these algorithms is that practical parallel image processing devices have been based on hexagonal decomposition. The advantages of the hexagonal covering are based on the 'uniform adjacency property: each element is adjacent to exactly six others, shares with each exactly one-sixth of its boundary, and has identical distance between its centroid and those of its neighbors. We describe the septree: a seven-descendant hierarchical data structure based on decomposing a roughly-hexagonal planar region into a central hexagon and its six neighbors. Septree algorithms used for low-level image pre-processing, image segmentation, and feature extraction are surveyed. The results presented here for static two-dimensional scenes can be extended to three-dimensional analogies. These can be used in computer vision models and in time-sequences of images for robots.