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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Vemuri, Sanjay Abhinav"],"institution":null,"degree_name":"Master of Science in Computer Science (MS)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Bobda, Christophe","Thompson, Craig W."],"advisors":["Gauch, John M."],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-01T07:00:00Z","date_published":"2013-05-01T07:00:00Z","updated_at":"2026-07-24T00:58:44Z","subjects":["Applied sciences","Computer vision","Digital image processing","Image alignment","Image matching","Object recognition","Graphics and Human Computer Interfaces"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/670","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bobda, Christophe","Thompson, Craig W."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Gauch, John M."]},{"key":"dc:creator","label":"Author","values":["Vemuri, Sanjay Abhinav"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-09-29T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Applied sciences","Computer vision","Digital image processing","Image alignment","Image matching","Object recognition","Graphics and Human Computer Interfaces"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/670"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Identifying Robust SIFT Features for Improved Image Alignment"]}]}],"canonical_facts":{"dc:contributor":["Bobda, Christophe","Thompson, Craig W."],"dc:contributor.advisor":["Gauch, John M."],"dc:creator":["Vemuri, Sanjay Abhinav"],"dc:date":["2013"],"dc:date.available":["2017-09-29T07:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://scholarworks.uark.edu/etd/670"],"dc:subject":["Applied sciences","Computer vision","Digital image processing","Image alignment","Image matching","Object recognition","Graphics and Human Computer Interfaces"],"dc:title":["Identifying Robust SIFT Features for Improved Image Alignment"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Computer Science (MS)"]},"updated_at":"2026-07-24T00:58:44Z"}