{"title":"基于投影几何的图像处理中的计算机视觉算法设计","authors":"YG Kang, Di Zhao","doi":"10.5750/ijme.v1i1.1385","DOIUrl":null,"url":null,"abstract":"Image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM's performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computer Vision Algorithm Design in Image Processing Based on Projective Geometry\",\"authors\":\"YG Kang, Di Zhao\",\"doi\":\"10.5750/ijme.v1i1.1385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM's performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain.\",\"PeriodicalId\":50313,\"journal\":{\"name\":\"International Journal of Maritime Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Maritime Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.5750/ijme.v1i1.1385\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Maritime Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5750/ijme.v1i1.1385","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
引用次数: 0
摘要
利用计算机视觉进行图像处理,特别是在投影几何领域,为各种应用提供了巨大的潜力。通过投影几何的视角,可以对图像进行精确的转换、增强和重建,从而为图像校正、三维重建和物体跟踪等任务提供便利。计算机视觉中的地标估算是一项重要任务,可广泛应用于各个领域。这一过程包括识别图像中的关键点或地标,从而完成面部识别、物体跟踪和手势识别等任务。本文提出了一种利用投影几何地标估算(PGLM)进行计算机视觉地标估算的新方法。该模型旨在通过投影几何模型估算地标特征。通过对几何特征的估计,可以计算出与面部、物体和医学图像相关的地标。PGLM 模型使用点特征来确定地标特征的位置。为了将 PGLM 的性能与随机森林、K-近邻(KNN)和支持向量机(SVM)等传统分类方法进行比较,我们进行了模拟分析。我们可以看到,在比较准确度、精确度、召回率和 F1 分数时,PGLM 完全胜过这些方法。研究结果表明,PGLM 是一种在图像处理任务中进行地标估计的有效方法,为这一领域的进一步发展铺平了道路。
Computer Vision Algorithm Design in Image Processing Based on Projective Geometry
Image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM's performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain.
期刊介绍:
The International Journal of Maritime Engineering (IJME) provides a forum for the reporting and discussion on technical and scientific issues associated with the design and construction of commercial marine vessels . Contributions in the form of papers and notes, together with discussion on published papers are welcomed.