Koyuncu, Murat

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Name Variants
M.,Koyuncu
Koyuncu M.
Koyuncu, Murat
M., Koyuncu
K.,Murat
Koyuncu, M
Koyuncu,M.
K., Murat
Murat, Koyuncu
Job Title
Profesor Doktor
Email Address
murat.koyuncu@atilim.edu.tr
Main Affiliation
Information Systems Engineering
Status
Website
ORCID ID
0000-0003-1958-5945
Scopus Author ID
7004305370
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Google Scholar ID
WoS Researcher ID
Scholarly Output

86

Articles

24

Citation Count

332

Supervised Theses

23

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2014 - 20195
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Subject: multimedia

Scholarly Output Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Conference Object
    Citation - WoS: 0
    Part-Based Object Extraction for Complex Objects
    (Ieee, 2014) Koyuncu, Murat; Cetinkaya, Basar; Information Systems Engineering
    Indexing requirement for efficient accessing to visual data has been increased with the widespread use of multimedia applications. Satisfaction of this requirement mostly depends on the automatic extraction of objects in the visual data. In this study, component-based object extraction method is compared with object extraction in its entirety. Applied method, implemented system and conducted tests are presented in this paper. Test results show that, even in the case of a good segmentation is achieved for whole object, object components are classified more successfully compared to whole object.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 0
    Part-Based Object Extraction for Complex Objects;
    (IEEE Computer Society, 2014) Koyuncu,M.; Cetinkaya,B.; Information Systems Engineering
    Indexing requirement for efficient accessing to visual data has been increased with the widespread use of multimedia applications. Satisfaction of this requirement mostly depends on the automatic extraction of objects in the visual data. In this study, component-based object extraction method is compared with object extraction in its entirety. Applied method, implemented system and conducted tests are presented in this paper. Test results show that, even in the case of a good segmentation is achieved for whole object, object components are classified more successfully compared to whole object. © 2014 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 0
    A Component-Based Object Detection Method Extended With a Fuzzy Inference Engine
    (Institute of Electrical and Electronics Engineers Inc., 2015) Koyuncu,M.; Cetinkaya,B.; Information Systems Engineering
    In this paper, we propose a component-based object detection method extended with the fuzzy inference technique. The proposed method detects constituent components of a complex object instead of a whole object in images. For component detection, multiple multi-class support vector machines (SVM) are used in parallel. Each SVM classifies the candidate component using a different low-level image feature. The obtained results are fused to reach a decision about the component. Then, a fuzzy object extractor determines the whole object considering the detected components and their geometric configurations. The fuzzy object extractor is a fuzzy inference engine which tests various combinations of detected components and their fuzzified directions and distances. The initial tests yield promising results and encourage further studies to extend proposed method. © 2015 IEEE.
  • Thumbnail Image
    Article
    Citation - WoS: 25
    Citation - Scopus: 39
    A Fusion-Based Framework for Wireless Multimedia Sensor Networks in Surveillance Applications
    (Ieee-inst Electrical Electronics Engineers inc, 2019) Yazici, Adnan; Koyuncu, Murat; Sert, Seyyit Alper; Yilmaz, Turgay; Information Systems Engineering
    Multimedia sensors enable monitoring applications to obtain more accurate and detailed information. However, the development of efficient and lightweight solutions for managing data traffic over wireless multimedia sensor networks (WMSNs) has become vital because of the excessive volume of data produced by multimedia sensors. As part of this motivation, this paper proposes a fusion-based WMSN framework that reduces the amount of data to be transmitted over the network by intra-node processing. This framework explores three main issues: 1) the design of a wireless multimedia sensor (WMS) node to detect objects using machine learning techniques; 2) a method for increasing the accuracy while reducing the amount of information transmitted by the WMS nodes to the base station, and; 3) a new cluster-based routing algorithm for the WMSNs that consumes less power than the currently used algorithms. In this context, a WMS node is designed and implemented using commercially available components. In order to reduce the amount of information to be transmitted to the base station and thereby extend the lifetime of a WMSN, a method for detecting and classifying objects on three different layers has been developed. A new energy-efficient cluster-based routing algorithm is developed to transfer the collected information/data to the sink. The proposed framework and the cluster-based routing algorithm are applied to our WMS nodes and tested experimentally. The results of the experiments clearly demonstrate the feasibility of the proposed WMSN architecture in the real-world surveillance applications.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 0
    A Component-Based Object Detection Method Extended With a Fuzzy Inference Engine
    (Ieee, 2015) Koyuncu, Murat; Cetinkaya, Basar; Information Systems Engineering
    In this paper, we propose a component-based object detection method extended with the fuzzy inference technique. The proposed method detects constituent components of a complex object instead of a whole object in images. For component detection, multiple multi-class support vector machines (SVM) are used in parallel. Each SVM classifies the candidate component using a different low-level image feature. The obtained results are fused to reach a decision about the component. Then, a fuzzy object extractor determines the whole object considering the detected components and their geometric configurations. The fuzzy object extractor is a fuzzy inference engine which tests various combinations of detected components and their fuzzified directions and distances. The initial tests yield promising results and encourage further studies to extend proposed method.