Jung Leng Foo
Iowa State University
14 Papers
67 Citations
Jung Leng Foo is an academic researcher from Iowa State University. The author has contributed to research in topics: Particle swarm optimization & Motion planning. The author has an hindex of 7, co-authored 14 publications.
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Papers
Three-Dimensional Path Planning of Unmanned Aerial Vehicles Using Particle Swarm Optimization
Jung Leng Foo,Jared S. Knutzon,James H. Oliver,Eliot H. Winer +3 more
- 06 Sep 2006
TL;DR: This paper presents a unique three-dimensional path planning problem formulation and solution approach using Particle Swarm Optimization (PSO), designed to minimize risk due to enemy threats while simultaneously minimizing fuel consumption.
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Evaluating mental workload of two-dimensional and three-dimensional visualization for anatomical structure localization
Jung Leng Foo,Marisol Martinez-Escobar,Bethany Juhnke,Keely Cassidy,Kenneth Hisley,Thom E Lobe,Eliot H. Winer +6 more
TL;DR: Evaluating the mental workload associated with both 2D and 3D views in first-year medical students showed that participants viewing in 3D had higher localization accuracy and a lower subjective measure of mental workload, specifically, the mental demand component of the NASA-TLX.
50
A framework for interactive visualization of digital medical images.
TL;DR: This research is directed toward improving the capabilities of medical professionals in the tasks of preoperative planning, surgical training, diagnostic assistance, and patient education by creating a software and hardware framework that not only make use of advanced visualization techniques, but also feature powerful, yet simple-to-use, interfaces.
14
Three-dimensional multi-objective path planning of unmanned aerial vehicles using particle swarm optimization
Jung Leng Foo,Jared S. Knutzon,James H. Oliver,Eliot H. Winer +3 more
- 01 Jan 2007
TL;DR: This paper presents a unique three-dimensional path planning problem formulation and solution approach using Particle Swarm Optimization (PSO), designed to minimize risk due to enemy threats and also to minimize fuel consumption incurred by deviating from the original path.
10
Colorization of CT images to improve tissue contrast for tumor segmentation
TL;DR: It is shown that colorization significantly decreases segmentation time and allows the method to be performed on commodity hardware, thereby allowing for easier identification of the tumor tissue(s).
10