{
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    "type": "research_article",
    "title": "Eşik Seçiminin Benzerliğe Dayalı Kenar Belirlemeye Etkisi",
    "title_secondary": null,
    "abstract": "Görüntüde yer alan nesnelerin kenarlarının belirlenmesi, görüntü işleme alanında önemli bir yer tutmaktadır. Piksellerin, komşu oldukları piksellere benzerlikleri de kenar belirleme amacıyla kullanılmaktadır. Ancak elde edilen benzerlik değerlerine göre kenar piksellerin belirlenmesinde eşik seçimi büyük önem taşımaktadır. Bu çalışmada, farklı katsayılar kullanılarak elde edilen benzeşim dönüşümü görüntülerinin üzerinde Otsu, Kapur ve Histogramın Ağırlık Merkezi (HAM) teknikleri ile eşikleme yapılmıştır. BSDS veritabanındaki 200 görüntü üzerinde yapılan benzeşim dönüşümü ve eşikleme işlemi sonuçlarının kenar belirleme başarıları ise F-skor olarak ölçülmüş ve birbirleriyle karşılaştırılmıştır.  ",
    "language": "tr",
    "english_title": "The Effect of Threshold Selection on Similarity-Based Edge Detection",
    "english_abstract": "Identifying the edges of objects in an image is a significant task in image processing. The similarity of pixels to their neighboring pixels is also used for edge detection. However, threshold selection is crucial in identifying edge pixels based on the obtained similarity values. In this study, thresholding was performed on similarity transformation images obtained using different coefficients using Otsu, Kapur, and Centroid of Histogram (CUM) techniques. The edge detection success rates of the similarity transformation and thresholding operations performed on 200 images in the BSDS database were measured as F-scores and compared with each other.",
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                    "name": "Bülent Ecevit University",
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            "given": "Ufuk",
            "family": "Tanyeri",
            "full": "Ufuk Tanyeri",
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            "email": "ufuktanyeri@ankara.edu.tr",
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            "affiliations": [
                {
                    "name": "Ankara University",
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            ]
        },
        {
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            "given": "Mahmut",
            "family": "Kılıçaslan",
            "full": "Mahmut Kılıçaslan",
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            "email": "m.kilicaslan@ankara.edu.tr",
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                    "name": "Ankara University",
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        {
            "public_id": "a116a7d1-2dc1-44d9-adb1-0fbf7909de7c",
            "given": "Burcu",
            "family": "Yakışır Girgin",
            "full": "Burcu Yakışır Girgin",
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            "orcid": null,
            "orcid_url": null,
            "email": "bygirgin@ankara.edu.tr",
            "corresponding": false,
            "sequence": "additional",
            "affiliations": [
                {
                    "name": "Ankara University",
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            ]
        },
        {
            "public_id": "dc062905-d011-4844-8e62-153ba9813b14",
            "given": "Recep",
            "family": "Demirci",
            "full": "Recep Demirci",
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            "email": "rdemirci@gazi.edu.tr",
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            "sequence": "additional",
            "affiliations": [
                {
                    "name": "Gazi University",
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            ]
        }
    ],
    "keywords": [
        "Kenar Belirleme",
        "Eşik",
        "Histogram"
    ],
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        {
            "language": "en",
            "title": "The Effect of Threshold Selection on Similarity-Based Edge Detection",
            "abstract": "Identifying the edges of objects in an image is a significant task in image processing. The similarity of pixels to their neighboring pixels is also used for edge detection. However, threshold selection is crucial in identifying edge pixels based on the obtained similarity values. In this study, thresholding was performed on similarity transformation images obtained using different coefficients using Otsu, Kapur, and Centroid of Histogram (CUM) techniques. The edge detection success rates of the similarity transformation and thresholding operations performed on 200 images in the BSDS database were measured as F-scores and compared with each other.",
            "keywords": []
        }
    ],
    "subjects": [
        {
            "name": "Computer Science",
            "label": "Computer Science",
            "path": "computer-science",
            "is_primary": true
        }
    ],
    "subject_names": [
        "Computer Science"
    ],
    "published_date": "2017-12-08",
    "published_year": "2017",
    "received_date": null,
    "accepted_date": null,
    "volume": "1",
    "issue": null,
    "page_first": "102",
    "page_last": "106",
    "article_number": null,
    "conference": {
        "short_name": "ISMSIT2017",
        "full_title": "International Symposium on Multidisciplinary Studies and Innovative Technologies",
        "name": "International Symposium on Multidisciplinary Studies and Innovative Technologies",
        "acronym": "ISMSIT2017",
        "location": "Tokat, Türkiye",
        "start_date": "2017-12-02",
        "end_date": null,
        "public_id": "a63b4822-97d6-4580-83b6-aec40f3f7964"
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    "proceedings": {
        "title": "International Symposium on Multidisciplinary Studies and Innovative Technologies",
        "series_title": "Setsci conference indexing system",
        "publisher": "SET Technology",
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        "issn": "2618-6446",
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        "slug": "setsci-volume-1-2017-legacy-1",
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        "url": "https://creativecommons.org/licenses/by/4.0/",
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        "year": "2017"
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    "references": [
        {
            "order": 1,
            "raw": "R. C. Gonzalez and R. E. Woods, Digital Image Processing Int. Ed., Pearson, Prentice Hall, New Jersey, 2008.",
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