{"title":"Comparison of Selected Methods for Detecting a Reference Element using Key Points in Static Images","authors":"Joanna Kulawik","doi":"10.1515/ipc-2017-0008","DOIUrl":null,"url":null,"abstract":"Abstract The article presents a possible way to detect key points. The tests were carried out by the case of detection of a reference object in static images. For comparative purposes, Chris Harris & Mike Stephens [11] and Speeded-Up Robust Features (SURF) detectors [2, 3] were used. The descriptors were built based on the Fast Retina Key point (FREAK) [1, 16] and SURF algorithms [2, 3]. Six different configurations of key point detection methods with the above descriptors were implemented. The obtained results have been presented on exemplary images and in the table. They show that this type of detection of an element of interest can be successful and should be developed.","PeriodicalId":271906,"journal":{"name":"Image Processing & Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image Processing & Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/ipc-2017-0008","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 The article presents a possible way to detect key points. The tests were carried out by the case of detection of a reference object in static images. For comparative purposes, Chris Harris & Mike Stephens [11] and Speeded-Up Robust Features (SURF) detectors [2, 3] were used. The descriptors were built based on the Fast Retina Key point (FREAK) [1, 16] and SURF algorithms [2, 3]. Six different configurations of key point detection methods with the above descriptors were implemented. The obtained results have been presented on exemplary images and in the table. They show that this type of detection of an element of interest can be successful and should be developed.
摘要本文提出了一种可能的关键点检测方法。以静态图像中参考物体的检测为例进行了测试。为了比较,我们使用了Chris Harris & Mike Stephens[11]和accelerated - up Robust Features (SURF)检测器[2,3]。描述符是基于Fast Retina Key point (FREAK)[1,16]和SURF算法[2,3]构建的。利用上述描述符实现了6种不同配置的关键点检测方法。所得结果已在示例图像和表中给出。它们表明,这种对感兴趣的元素的检测是成功的,应该得到发展。