{"title":"基于特征三角的电气设备多模态图像配准","authors":"Jianhua Zhu;Changjiang Liu","doi":"10.1109/TAES.2024.3514602","DOIUrl":null,"url":null,"abstract":"Infrared and visible imaging are crucial for detecting faults in electrical equipment, but their differences in resolution, field of view, and spectrum pose challenges for accurate image registration. Current methods often suffer from poor accuracy, lengthy processing times, or failing to register properly. This article proposes a multimodal image registration method for electrical equipment using feature triangles. It categorizes contours into open and closed contours, computes point curvatures to identify features, constructs feature triangles based on curvature radii, and determines principal directions using triangle centroids. The method demonstrates invariant properties under scaling, translation, and rotation. We evaluated the proposed method using both publicly available datasets and our own datasets, conducting both subjective and objective comparisons with state-of-the-art techniques. The proposed method achieved an average registration accuracy of 0.1117 and an average processing time of 8.834 s. For the infrared and visible images, the significant curvature contour ratios were measured at 0.251 and 0.193, respectively, while the feature point ratios were found to be 0.610 and 0.527. The experimental results demonstrate superior registration accuracy and reduced computational overhead compared to existing methods.","PeriodicalId":13157,"journal":{"name":"IEEE Transactions on Aerospace and Electronic Systems","volume":"61 2","pages":"5104-5115"},"PeriodicalIF":5.1000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multimodal Image Registration of Electrical Equipment Based on Feature Triangle\",\"authors\":\"Jianhua Zhu;Changjiang Liu\",\"doi\":\"10.1109/TAES.2024.3514602\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Infrared and visible imaging are crucial for detecting faults in electrical equipment, but their differences in resolution, field of view, and spectrum pose challenges for accurate image registration. Current methods often suffer from poor accuracy, lengthy processing times, or failing to register properly. This article proposes a multimodal image registration method for electrical equipment using feature triangles. It categorizes contours into open and closed contours, computes point curvatures to identify features, constructs feature triangles based on curvature radii, and determines principal directions using triangle centroids. The method demonstrates invariant properties under scaling, translation, and rotation. We evaluated the proposed method using both publicly available datasets and our own datasets, conducting both subjective and objective comparisons with state-of-the-art techniques. The proposed method achieved an average registration accuracy of 0.1117 and an average processing time of 8.834 s. For the infrared and visible images, the significant curvature contour ratios were measured at 0.251 and 0.193, respectively, while the feature point ratios were found to be 0.610 and 0.527. The experimental results demonstrate superior registration accuracy and reduced computational overhead compared to existing methods.\",\"PeriodicalId\":13157,\"journal\":{\"name\":\"IEEE Transactions on Aerospace and Electronic Systems\",\"volume\":\"61 2\",\"pages\":\"5104-5115\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2024-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Aerospace and Electronic Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10789189/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Aerospace and Electronic Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10789189/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
Multimodal Image Registration of Electrical Equipment Based on Feature Triangle
Infrared and visible imaging are crucial for detecting faults in electrical equipment, but their differences in resolution, field of view, and spectrum pose challenges for accurate image registration. Current methods often suffer from poor accuracy, lengthy processing times, or failing to register properly. This article proposes a multimodal image registration method for electrical equipment using feature triangles. It categorizes contours into open and closed contours, computes point curvatures to identify features, constructs feature triangles based on curvature radii, and determines principal directions using triangle centroids. The method demonstrates invariant properties under scaling, translation, and rotation. We evaluated the proposed method using both publicly available datasets and our own datasets, conducting both subjective and objective comparisons with state-of-the-art techniques. The proposed method achieved an average registration accuracy of 0.1117 and an average processing time of 8.834 s. For the infrared and visible images, the significant curvature contour ratios were measured at 0.251 and 0.193, respectively, while the feature point ratios were found to be 0.610 and 0.527. The experimental results demonstrate superior registration accuracy and reduced computational overhead compared to existing methods.
期刊介绍:
IEEE Transactions on Aerospace and Electronic Systems focuses on the organization, design, development, integration, and operation of complex systems for space, air, ocean, or ground environment. These systems include, but are not limited to, navigation, avionics, spacecraft, aerospace power, radar, sonar, telemetry, defense, transportation, automated testing, and command and control.