Date of Award

Fall 1996

Document Type

Thesis

Degree Name

Master of Science in Computer Science - (M.S.)

Department

Computer and Information Science

First Advisor

Jason T. L. Wang

Second Advisor

Peter A. Ng

Third Advisor

Marvin K. Nakayama

Abstract

An approach to designing very fast algorithms for tackling the problem of approximate object matching in very large databases of high-dimensional objects is proposed. Given are a target object C and a database D containing information about a set of high-dimensional objects each of which is represented as a set of points. Our algorithms have an off-line object preprocessing (shape representation) phase and a recognition phase. The described algorithms determine those objects from D which are the closest to object C, according to delete or insert some points, move and rotation. All of these can be achieved very efficiently with the help of geometric hashing techniques. This scheme has been successfully applied to a real scientific database.

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