Advanced Search

Show simple item record

dc.contributor.authorEge, Yavuz
dc.contributor.authorNazlıbilek, Sedat
dc.contributor.authorKakilli, Adnan
dc.contributor.authorÇıtak, Hakan
dc.contributor.authorKalender, Osman
dc.contributor.authorKaracor, Deniz
dc.contributor.authorErtürk, Korhan Levent
dc.contributor.authorŞengül, Gökhan
dc.date.accessioned2019-10-17T10:24:14Z
dc.date.available2019-10-17T10:24:14Z
dc.date.issued2015en_US
dc.identifier.issn0018-9464
dc.identifier.issn1941-0069
dc.identifier.urihttps://doi.org/10.1109/TMAG.2015.2408572
dc.identifier.urihttps://hdl.handle.net/20.500.12462/8041
dc.descriptionEge, Yavuz (Balikesir Author)en_US
dc.description.abstractIndustry requires low-cost, low-power consumption, and autonomous remote sensing systems for detecting and identifying magnetic materials. Magnetic anomaly detection is one of the methods that meet these requirements. This paper aims to detect and identify magnetic materials by the use of magnetic anomalies of the Earth's magnetic field created by some buried materials. A new measurement system that can determine the images of the upper surfaces of buried magnetic materials is developed. The system consists of a platform whose position is automatically controlled in x-axis and y-axis and a KMZ51 anisotropic magneto-resistive sensor assembly with 24 sensors mounted on the platform. A new identification system based on scale-invariant feature transform (SIFT)-binary robust invariant scalable keypoints (BRISKs) as keypoint and descriptor, respectively, is developed for identification by matching the similar images of magnetic anomalies. The results are compared by the conventional principal component analysis and neural net algorithms. On the six selected samples and the combinations of these samples, 100% correct classification rates were obtained.en_US
dc.language.isoengen_US
dc.publisherLeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.isversionof10.1109/TMAG.2015.2408572en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectBinary Robust Invariant Scalable Keypoint (BRISK)en_US
dc.subjectIdentificationen_US
dc.subjectMine Detectionen_US
dc.subjectNeural Networksen_US
dc.subjectPrincipal Component Analysis (PCA)en_US
dc.subjectScale-Invariant Feature Transform (SIFT)en_US
dc.titleA study on the performance of magnetic material ıdentification system by SIFT-BRISK and neural network methodsen_US
dc.typearticleen_US
dc.relation.journalLeee Transactions on Magneticsen_US
dc.contributor.departmentNecatibey Eğitim Fakültesien_US
dc.contributor.authorID0000-0002-1162-2580en_US
dc.contributor.authorID0000-0003-2273-4411en_US
dc.contributor.authorID0000-0002-5627-3601en_US
dc.identifier.volume51en_US
dc.identifier.issue8en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record