{"title":"Application of Wavelet Analysis in Image Matching","authors":"Linglong Tan, Fengzhi Wu, Xiaoyao Yin, Song Xue","doi":"10.1145/3480651.3480670","DOIUrl":null,"url":null,"abstract":"Abstract. Based on the study of traditional matching methods, this paper implements a low-frequency image matching system based on wavelet transform, which is composed of wavelet preprocessing, low-frequency image extraction, and image matching. The low-frequency image after wavelet decomposition is used for matching, which can reduce the calculation time of matching. The low-frequency image still contains most of the visual information of the original image, making the matching result stable and reliable.In this system, image wavelet decomposition and matching use mature and fast algorithms. The matching is performed on low-frequency images, which makes the amount of calculation for matching very small. Using the low-frequency components of the image to match also greatly removes the interference of noise on the image matching. Since the highest proportion of high-frequency noise in the noise has been removed before the algorithm is matched, all the matching algorithms have good anti-noise ability.The matching system in this paper adopts a matching method based on low-frequency components after wavelet transform, discusses and realizes the use of low-frequency images after image wavelet decomposition to perform image matching. The experimental results show that the matching algorithm used in the article has fast calculation speed, less matching time, and certain practicability.","PeriodicalId":305943,"journal":{"name":"Proceedings of the 2021 International Conference on Pattern Recognition and Intelligent Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 International Conference on Pattern Recognition and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3480651.3480670","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract. Based on the study of traditional matching methods, this paper implements a low-frequency image matching system based on wavelet transform, which is composed of wavelet preprocessing, low-frequency image extraction, and image matching. The low-frequency image after wavelet decomposition is used for matching, which can reduce the calculation time of matching. The low-frequency image still contains most of the visual information of the original image, making the matching result stable and reliable.In this system, image wavelet decomposition and matching use mature and fast algorithms. The matching is performed on low-frequency images, which makes the amount of calculation for matching very small. Using the low-frequency components of the image to match also greatly removes the interference of noise on the image matching. Since the highest proportion of high-frequency noise in the noise has been removed before the algorithm is matched, all the matching algorithms have good anti-noise ability.The matching system in this paper adopts a matching method based on low-frequency components after wavelet transform, discusses and realizes the use of low-frequency images after image wavelet decomposition to perform image matching. The experimental results show that the matching algorithm used in the article has fast calculation speed, less matching time, and certain practicability.