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/Mentor
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.
Citation
Vemuri, S. (2013). Identifying Robust SIFT Features for Improved Image Alignment. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/670