Title: Piece-Wise Linear Line Simplification by Self-Organization using the Second Order Auto Regression

Year of Publication: Aug - 2019
Page Numbers: 86-89
Authors: Akira Wake, Isamu Shioya
Conference Name: The Fifth International Conference on Electronics and Software Science (ICESS2019)
- Japan


This paper describes a line simplification algorithm combining self-organization and auto-regression, and the algorithm keeps a quality for drawing simplified lines even when scaled. Self-organization enables us a microscopic autonomous behavior to produce macro structures, and plays a coordination which reflects to every two neighbor vertices. On the other hand, auto-regression maintains the shape of original lines, so the regression tries to track the original features as much as possible by considering lines locally as time series. Our contributions are to show that the two models work well together and give us positive results by complementing each other without losing the original essential features. Finally, we present some experimental results showing our usefulness.