ABSTRACT
Optimizing Harris Corner Detection on GPGPUs Using CUDA
The objective of this thesis is to optimize the Harris corner detection algorithm implementation on NVIDIA GPGPUs using the CUDA software platform and measure the performance benefit. The Harris corner detection algorithm—developed by C. Harris and M. Stephens—discovers well defined corner points within an image. The corner detection implementation has been proven to be computationally intensive, thus realtime performance is difficult with a sequential software implementation. This thesis decomposes the Harris corner detection algorithm into a set of parallel stages, each of which are implemented and optimized on the CUDA platform. The performance results show that by applying strategic CUDA optimizations to the Harris corner detection implementation, realtime performance is feasible. The optimized CUDA implementation of the Harris corner detection algorithm showed significant speedup over several platforms: standard C, MATLAB, and OpenCV. The optimized CUDA implementation of the Harris corner detection algorithm was then applied to a feature matching computer vision system, which showed significant speedup over the other platforms.
","abstract_html":"<p><strong>ABSTRACT</strong></p> <p>Optimizing Harris Corner Detection on GPGPUs Using CUDA</p> <p>The objective of this thesis is to optimize the Harris corner detection algorithm implementation on NVIDIA GPGPUs using the CUDA software platform and measure the performance benefit. The Harris corner detection algorithm—developed by C. Harris and M. Stephens—discovers well defined corner points within an image. The corner detection implementation has been proven to be computationally intensive, thus realtime performance is difficult with a sequential software implementation. This thesis decomposes the Harris corner detection algorithm into a set of parallel stages, each of which are implemented and optimized on the CUDA platform. The performance results show that by applying strategic CUDA optimizations to the Harris corner detection implementation, realtime performance is feasible. The optimized CUDA implementation of the Harris corner detection algorithm showed significant speedup over several platforms: standard C, MATLAB, and OpenCV. The optimized CUDA implementation of the Harris corner detection algorithm was then applied to a feature matching computer vision system, which showed significant speedup over the other platforms.</p>","abstract_has_math":false,"creators":["Loundagin, Justin"],"institution":null,"degree_name":"MS in Electrical Engineering","degree_level":null,"degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Jane Zhang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-03-01T08:00:00Z","date_published":"2015-03-01T08:00:00Z","updated_at":"2026-07-24T01:31:35Z","subjects":["CUDA","GPGPU","NVIDIA","Harris Corner Detection","Computational Engineering","Electrical and Computer Engineering","Signal Processing"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2015.11"],"render_values":[{"text":"10.15368/theses.2015.11","href":"https://doi.org/10.15368/theses.2015.11","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1348","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jane Zhang"]},{"key":"dc:creator","label":"Author","values":["Loundagin, Justin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-03-19T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Electrical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["CUDA","GPGPU","NVIDIA","Harris Corner Detection","Computational Engineering","Electrical and Computer Engineering","Signal Processing"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1348","10.15368/theses.2015.11"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["ABSTRACT
Optimizing Harris Corner Detection on GPGPUs Using CUDA
The objective of this thesis is to optimize the Harris corner detection algorithm implementation on NVIDIA GPGPUs using the CUDA software platform and measure the performance benefit. The Harris corner detection algorithm—developed by C. Harris and M. Stephens—discovers well defined corner points within an image. The corner detection implementation has been proven to be computationally intensive, thus realtime performance is difficult with a sequential software implementation. This thesis decomposes the Harris corner detection algorithm into a set of parallel stages, each of which are implemented and optimized on the CUDA platform. The performance results show that by applying strategic CUDA optimizations to the Harris corner detection implementation, realtime performance is feasible. The optimized CUDA implementation of the Harris corner detection algorithm showed significant speedup over several platforms: standard C, MATLAB, and OpenCV. The optimized CUDA implementation of the Harris corner detection algorithm was then applied to a feature matching computer vision system, which showed significant speedup over the other platforms.
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Optimizing Harris Corner Detection on GPGPUs Using CUDA
The objective of this thesis is to optimize the Harris corner detection algorithm implementation on NVIDIA GPGPUs using the CUDA software platform and measure the performance benefit. The Harris corner detection algorithm—developed by C. Harris and M. Stephens—discovers well defined corner points within an image. The corner detection implementation has been proven to be computationally intensive, thus realtime performance is difficult with a sequential software implementation. This thesis decomposes the Harris corner detection algorithm into a set of parallel stages, each of which are implemented and optimized on the CUDA platform. The performance results show that by applying strategic CUDA optimizations to the Harris corner detection implementation, realtime performance is feasible. The optimized CUDA implementation of the Harris corner detection algorithm showed significant speedup over several platforms: standard C, MATLAB, and OpenCV. The optimized CUDA implementation of the Harris corner detection algorithm was then applied to a feature matching computer vision system, which showed significant speedup over the other platforms.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1348","10.15368/theses.2015.11"],"dc:subject":["CUDA","GPGPU","NVIDIA","Harris Corner Detection","Computational Engineering","Electrical and Computer Engineering","Signal Processing"],"dc:title":["Optimizing Harris Corner Detection on GPGPUs Using CUDA"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_name":["MS in Electrical Engineering"]},"updated_at":"2026-07-24T01:31:35Z"}