dc.contributor.author | Jia, Zhuohao | |
dc.date.accessioned | 2023-07-10T19:55:10Z | |
dc.date.available | 2023-07-10T19:55:10Z | |
dc.date.issued | 2023-07 | |
dc.identifier.citation | Jia, 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.uri | https://hdl.handle.net/10680/2086 | |
dc.description.abstract | The 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.iso | en | en_US |
dc.publisher | University of Winnipeg | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Zernike moments | en_US |
dc.subject | Fast computation | en_US |
dc.subject | Graphics Processing Units (GPUs) | en_US |
dc.subject | Image reconstruction | en_US |
dc.title | An Efficient Approach to Compute Zernike Moments with GPU-Accelerated Algorithm | en_US |
dc.type | Thesis | en_US |
dc.description.degree | Master of Science in Applied Computer Science | en_US |
dc.publisher.grantor | University of Winnipeg | en_US |
dc.identifier.doi | 10.36939/ir.202307101450 | en_US |
thesis.degree.discipline | Applied Computer Science | |
thesis.degree.level | masters | |
thesis.degree.name | Master of Science in Applied Computer Science | |
thesis.degree.grantor | University of Winnipeg | |