@conference {648,
	title = {Clustering Moment Invariants to Identify Similarity within 2D Flow Fields},
	booktitle = {EG / VGTC Conference on Visualization, EuroVis 2015 in Cagliari, Italy},
	year = {2015},
	url = {http://www.informatik.uni-leipzig.de/~bujack/2015EuroVis.pdf},
	author = {Roxana Bujack and Jens Kasten and Vijay Natarajan and Gerik Scheuermann and Kenneth I. Joy}
}
@conference {647,
	title = {Moment Invariants for 3D Flow Fields via Normalization},
	booktitle = {IEEE Pacific Visualization Symposium, PacificVis 2015 in Hangzhou, China},
	year = {2015},
	url = {http://www.informatik.uni-leipzig.de/~bujack/PacificVis2015.pdf},
	author = {Roxana Bujack and Jens Kasten and Ingrid Hotz and Gerik Scheuermann and Eckhard Hitzer}
}
@proceedings {685,
	title = {Detecting Topologically Relevant Structures in Flows by Surface Integrals},
	volume = {22},
	year = {2014},
	pages = {39-48},
	author = {Wieland Reich and Jens Kasten and Gerik Scheuermann}
}
@proceedings {645,
	title = {Moment Invariants for 3D Flow Fields},
	year = {2014},
	url = {http://www.informatik.uni-leipzig.de/~bujack/meinPosterSmall.pdf},
	author = {Roxana Bujack and Jens Kasten and Ingrid Hotz and Gerik Scheuermann and Eckhard Hitzer}
}
@article {527,
	title = {dPSO-Vis: Topology-based Visualization of Discrete Particle Swarm Optimization},
	journal = {Computer Graphics Forum},
	volume = {32},
	year = {2013},
	pages = {351-360},
	abstract = {Particle swarm optimization (PSO) is a metaheuristic that has been applied successfully to many continuous and combinatorial optimization problems, e.g., in the fields of economics, engineering, and natural sciences. In PSO a swarm of particles moves within a search space in order to find an optimal solution. Unfortunately, it is hard to understand in detail why and how changes in the design of PSO algorithms affect the optimization behavior. Visualizing the particle states could provide substantially better insight into PSO algorithms, but in case of combinatorial optimization problems, it raises the problem of illustrating the discrete states that cannot easily be embedded spatially. We propose a visualization approach to analyze the optimization problem topologically using a landscape metaphor. Therefore, we transform the configuration space of the optimization problem into a barrier landscape that is topologically equivalent. This visualization is augmented by an illustration of the time-dependent states of the particles. The user of our tool {\textemdash} called dPSO-Vis {\textemdash} is able to analyze the swarm{\textquoteright}s behavior within the search space. We illustrate our approach with a brief analysis of a PSO algorithm that predicts the secondary structure of RNA molecules.},
	author = {Sebastian Volke and Martin Middendorf and Mario Hlawitschka and Jens Kasten and Dirk Zeckzer and Gerik Scheuermann}
}
@conference {510,
	title = {Towards the Extraction of Saddle Periodic Orbits},
	booktitle = {TopoInVis 2013},
	year = {2013},
	author = {Jens Kasten and Jan Reininghaus and Wieland Reich and Gerik Scheuermann}
}
@conference {536,
	title = {Visualizing Linear Neighborhoods in Non-Linear Vector Fields},
	booktitle = {PacificVis},
	year = {2013},
	doi = {target},
	author = {Stefan Koch and Alexander Wiebel and Jens Kasten and Mario Hlawitschka}
}
@booklet {eichelbaum2012c,
	title = {Leading edge vortices of flow over a delta wing.},
	year = {2012},
	month = {November},
	author = {Eichelbaum, Sebastian and Jens Kasten and Mario Hlawitschka and Gerik Scheuermann and Bernd R. Noack}
}
