Identifying Robust SIFT Features for Improved Image Alignment

<p>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.</p>

Name thesis:degree_name

Master of Science in Computer Science (MS)

Level thesis:degree_level

Thesis

Year dc:date.available

2013

Author dc:creator

• Vemuri, Sanjay Abhinav

Advisor dc:contributor.advisor

• Gauch, John M.

Contributors dc:contributor

• Bobda, Christophe
• Thompson, Craig W.

• Applied sciences
• Computer vision
• Digital image processing
• Image alignment
• Image matching
• Object recognition
• Graphics and Human Computer Interfaces

Repository record dc:identifier

https://scholarworks.uark.edu/etd/670

OAI identifier oai:identifier

oai:scholarworks.uark.edu:etd-1669