A Study on the Performance of Magnetic Material Identification System by SIFT-BRISK and Neural Network Methods

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Date

2015

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Ieee-inst Electrical Electronics Engineers inc

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Computer Engineering
(1998)
The Atılım University Department of Computer Engineering was founded in 1998. The department curriculum is prepared in a way that meets the demands for knowledge and skills after graduation, and is subject to periodical reviews and updates in line with international standards. Our Department offers education in many fields of expertise, such as software development, hardware systems, data structures, computer networks, artificial intelligence, machine learning, image processing, natural language processing, object based design, information security, and cloud computing. The education offered by our department is based on practical approaches, with modern laboratories, projects and internship programs. The undergraduate program at our department was accredited in 2014 by the Association of Evaluation and Accreditation of Engineering Programs (MÜDEK) and was granted the label EUR-ACE, valid through Europe. In addition to the undergraduate program, our department offers thesis or non-thesis graduate degree programs (MS).
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Department of Mechatronics Engineering
Our purpose in the program is to educate our students for contributing to universal knowledge by doing research on contemporary mechatronics engineering problems and provide them with design, production and publication skills. To reach this goal our post graduate students are offered courses in various areas of mechatronics engineering, encouraged to do research to develop their expertise and their creative side, as well as develop analysis and design skills.

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Abstract

Industry 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.

Description

Karacor, Deniz/0000-0001-6961-8966; ERTÜRK, Korhan/0000-0002-1162-2580; Kakilli, Adnan/0000-0003-2432-4424; Sengul, Gokhan/0000-0003-2273-4411

Keywords

Binary robust invariant scalable keypoint (BRISK), identification, mine detection, neural networks, principal component analysis (PCA), scale-invariant feature transform (SIFT)

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5

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Volume

51

Issue

8

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URI

https://doi.org/10.1109/TMAG.2015.2408572
 https://hdl.handle.net/20.500.14411/863

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