{"title":"使用全景拼接创建可视场景","authors":"Saja Alferidah, Nora Alkhaldi","doi":"10.15439/2019F282","DOIUrl":null,"url":null,"abstract":"Image stitching refers to the process of combining multiple images of the same scene to produce a single high-resolution image, known as panorama stitching. The aim of this paper is to produce a high-quality stitched panorama image with less computation time. This is achieved by proposing four combinations of algorithms. First combination includes FAST corner detector, Brute Force K-Nearest Neighbor (KNN) and Random Sample Consensus (RANSAC). Second combination includes FAST, Brute Force (KNN) and Progressive Sample Consensus (PROSAC). Third combination includes ORB, Brute Force (KNN) and RANSAC. Fourth combination contains ORB, Brute Force (KNN) and PROSAC. Next, each combination involves a calculation of Transformation Matrix. The results demonstrated that the fourth combination produced a panoramic image with the highest performance and better quality compared to other combinations. The processing time is reduced by 67% for the third combination and by 68% for the fourth combination compared to stat-of-the-art.","PeriodicalId":168208,"journal":{"name":"2019 Federated Conference on Computer Science and Information Systems (FedCSIS)","volume":"412 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Creating See-Around Scenes using Panorama Stitching\",\"authors\":\"Saja Alferidah, Nora Alkhaldi\",\"doi\":\"10.15439/2019F282\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image stitching refers to the process of combining multiple images of the same scene to produce a single high-resolution image, known as panorama stitching. The aim of this paper is to produce a high-quality stitched panorama image with less computation time. This is achieved by proposing four combinations of algorithms. First combination includes FAST corner detector, Brute Force K-Nearest Neighbor (KNN) and Random Sample Consensus (RANSAC). Second combination includes FAST, Brute Force (KNN) and Progressive Sample Consensus (PROSAC). Third combination includes ORB, Brute Force (KNN) and RANSAC. Fourth combination contains ORB, Brute Force (KNN) and PROSAC. Next, each combination involves a calculation of Transformation Matrix. The results demonstrated that the fourth combination produced a panoramic image with the highest performance and better quality compared to other combinations. The processing time is reduced by 67% for the third combination and by 68% for the fourth combination compared to stat-of-the-art.\",\"PeriodicalId\":168208,\"journal\":{\"name\":\"2019 Federated Conference on Computer Science and Information Systems (FedCSIS)\",\"volume\":\"412 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Federated Conference on Computer Science and Information Systems (FedCSIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15439/2019F282\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Federated Conference on Computer Science and Information Systems (FedCSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15439/2019F282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
图像拼接是指将同一场景的多幅图像组合成一张高分辨率图像的过程,称为全景拼接。本文的目标是用更少的计算时间生成高质量的拼接全景图像。这是通过提出四种算法组合来实现的。第一种组合包括FAST角检测器、KNN (Brute Force K-Nearest Neighbor)和RANSAC (Random Sample Consensus)。第二种组合包括FAST, Brute Force (KNN)和Progressive Sample Consensus (PROSAC)。第三种组合包括ORB、Brute Force (KNN)和RANSAC。第四种组合包含ORB、Brute Force (KNN)和PROSAC。接下来,每个组合都涉及到变换矩阵的计算。结果表明,与其他组合相比,第四种组合产生的全景图像具有最高的性能和更好的质量。与最先进的组合相比,第三种组合的处理时间减少了67%,第四种组合的处理时间减少了68%。
Creating See-Around Scenes using Panorama Stitching
Image stitching refers to the process of combining multiple images of the same scene to produce a single high-resolution image, known as panorama stitching. The aim of this paper is to produce a high-quality stitched panorama image with less computation time. This is achieved by proposing four combinations of algorithms. First combination includes FAST corner detector, Brute Force K-Nearest Neighbor (KNN) and Random Sample Consensus (RANSAC). Second combination includes FAST, Brute Force (KNN) and Progressive Sample Consensus (PROSAC). Third combination includes ORB, Brute Force (KNN) and RANSAC. Fourth combination contains ORB, Brute Force (KNN) and PROSAC. Next, each combination involves a calculation of Transformation Matrix. The results demonstrated that the fourth combination produced a panoramic image with the highest performance and better quality compared to other combinations. The processing time is reduced by 67% for the third combination and by 68% for the fourth combination compared to stat-of-the-art.