Amin Gheibi
Papers
1
Total Citations
5
H-Index
1
About
Amin Gheibi is a researcher in computational geometry, with a primary focus on shape analysis, curve similarity, and algorithmic optimization. His most notable contribution is the introduction of the **minimum backward Fréchet distance**, a novel measure for comparing polygonal curves that refines the classic weak Fréchet distance. This work, published in 2014, addresses a natural optimization problem: for a given threshold, it seeks the optimal pair of traversals that minimize backward movement, offering a more nuanced tool for matching trajectories, handwriting, or biological shapes. While his citation count (5 for this key paper) reflects a specialized niche, the concept has been cited in subsequent geometric matching and computational topology studies, underscoring its theoretical significance. Gheibi’s research sits at the intersection of algorithm design and practical applications, where precise curve comparison is critical—such as in GIS, computer graphics, and motion analysis. His work exemplifies how subtle refinements to foundational metrics can open new avenues for robust shape similarity, making him a contributor to the ongoing evolution of geometric data analysis.
Research Focus
Key Achievements
Top Papers
- 1Minimum backward fréchet distance5 citations · 2014