Show simple item record

dc.contributor.authorJia, Zhuohao
dc.date.accessioned2023-07-10T19:55:10Z
dc.date.available2023-07-10T19:55:10Z
dc.date.issued2023-07
dc.identifier.citationJia, Zhuohao. An Efficient Approach to Compute Zernike Moments with GPU-Accelerated Algorithm; A thesis submitted to the Faculty of Graduate Studies in partial fulfillment of the requirements for the Master of Science degree, Department of Applied Computer Science, The University of Winnipeg. Winnipeg, Manitoba, Canada: The University of Winnipeg, July 2023. DOI: 10.36939/ir.202307101450.en_US
dc.identifier.urihttps://hdl.handle.net/10680/2086
dc.description.abstractThe utilization of Zernike moments has been extensive in various fields, including image processing and pattern recognition, owing to their desirable characteristics. However, the application of Zernike moments is hindered by two significant obstacles: computational efficiency and accuracy. These issues become particularly noticeable when computing high-order moments. This study presents a novel GPU-based method for efficiently computing Zernike moments by leveraging the computational power of the Single Instruction Multiple Data(SIMD) architecture. The experimental results demonstrate that the proposed method can compute Zernike moments up to order 500 within 0.5 seconds for an image of size 512 * 512. To achieve greater accuracy in Zernike moments computation, a k * k sub-region scheme was incorporated into the approach. The results show that the PSNR value of the Lena image reconstructed from 500-order Zernike moments computed using the 9 * 9 scheme can reach 39.20 dB. Furthermore, a method for leaf recognition that leverages Zernike moments as image features, with the k-Nearest Neighbors (k-NN) algorithm serving as the classifier is proposed. The proposed method is evaluated on the Flavia leaf dataset, and the results affirm the effectiveness of the approach.en_US
dc.language.isoenen_US
dc.publisherUniversity of Winnipegen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectZernike momentsen_US
dc.subjectFast computationen_US
dc.subjectGraphics Processing Units (GPUs)en_US
dc.subjectImage reconstructionen_US
dc.titleAn Efficient Approach to Compute Zernike Moments with GPU-Accelerated Algorithmen_US
dc.typeThesisen_US
dc.description.degreeMaster of Science in Applied Computer Scienceen_US
dc.publisher.grantorUniversity of Winnipegen_US
dc.identifier.doi10.36939/ir.202307101450en_US
thesis.degree.disciplineApplied Computer Science
thesis.degree.levelmasters
thesis.degree.nameMaster of Science in Applied Computer Science
thesis.degree.grantorUniversity of Winnipeg


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record