Title

Identifying Robust SIFT Features for Improved Image Alignment

Date of Graduation

5-2013

Document Type

Thesis

Degree Name

Master of Science in Computer Science (MS)

Degree Level

Graduate

Department

Computer Science & Computer Engineering

Advisor

John Gauch

Committee Member

Christophe Bobda

Second Committee Member

Craig Thompson

Keywords

Applied sciences; Computer vision; Digital image processing; Image alignment; Image matching; Object recognition

Abstract

In this thesis, we will study different ways to improve feature matching by increasing the quality and reducing the number of SIFT features. We created an algorithm to identify robust SIFT features by evaluating how invariant individual feature points are to changes in scale. This allows us to exclude poor SIFT feature points from the matching process and obtain better matching results in reduced time. We also developed techniques consider scale ratios and changes in object orientation when performing feature matching. This allows us to exclude false-positive feature matches and obtain better image alignment results.

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