@inproceedings{79ce9cad20254f70af4472ea3a1a0d76,
title = "SwarmCurves: Evolutionary Curve Reconstruction",
abstract = "The problem of recovering the shape of a curve given partial information about it is a fundamental problem in many applications in visual computing. Which types of curves are fitted to a given input data depends on the application and varies from piece-wise linear approximation to parametric splines. The choice of approximation method depends on the context of the problem, the nature of the data, and the desired level of accuracy and complexity. In this paper we introduce SwarmCurves, a curve reconstruction approach based on particle swarm optimization. For given input data SwarmCurves offers a range of solutions, from linear polygons to rational B-Splines with various degrees of freedom. The algorithm works on dense, sparse or noisy, 2D or 3D input data. We demonstrate the performance of SwarmCurves, on a number of examples.",
keywords = "B-Splines, Curve Reconstruction, Evolutionary Optimization",
author = "Alexander Komar and Ursula Augsd{\"o}rfer",
year = "2023",
month = dec,
day = "1",
doi = "10.1007/978-3-031-47969-4_27",
language = "English",
isbn = "9783031479687",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "343--354",
editor = "George Bebis and Golnaz Ghiasi and Yi Fang and Andrei Sharf and Yue Dong and Chris Weaver and Zhicheng Leo and {LaViola Jr.}, {Joseph J.} and Luv Kohli",
booktitle = "Advances in Visual Computing",
address = "Germany",
note = "18th International Symposium on Visual Computing : ISVC 2023 ; Conference date: 16-10-2023 Through 18-10-2023",
}