Award Date

May 2016

Degree Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science

First Committee Member

Laxmi P. Gewali

Second Committee Member

John Minor

Third Committee Member

Justin Zhan

Number of Pages

62

Abstract

Clustering a set of points in Euclidean space is a well-known problem having applications in pattern recognition, document image analysis, big-data analytics, and robotics. While there are a lot of research publications for clustering point objects, only a few articles have been reported for clustering a given distribution of obstacles. In this thesis we examine the development of efficient algorithms for clustering a given set of convex obstacles in the 2D plane. One of the methods presented in this work uses a Voronoi diagram to extract obstacle clusters. We also consider the implementation issues of point/obstacle clustering algorithms.

Keywords

clustering; k-means; plane sweep; polygonal obstacle; visibility graph; voronoi diagram

Disciplines

Artificial Intelligence and Robotics | Computer Sciences | Robotics

Language

English


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