Implementation of Single-Scan Connected Component Labeling for FPGA Hardware

İsmail Yabanova; Tarık Ünler

Makale detayları

Implementation of Single-Scan Connected Component Labeling for FPGA Hardware

Yazarlar:

İsmail Yabanova | Tarık Ünler

Yayıncı:

Necmettin Erbakan University

Ana Başlıklar:

Görüntü İşleme | Gömülü Sistemler

Dergi:

Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.7 Issue 1, pp.12-21

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001338800

pISSN:

2667-7989

Anahtar kelimeler:

Özet:

 Image processing is a branch of computer science that tries to extract information and identify objects from digital photographs through processing and analysis. Today, imaging systems are used in almost every aspect of our lives, particularly in medicine, the defense industry, security, automobile, and automation. As a result, the image must be processed in accordance with the intended purposes. Blob detection is a concept used in image processing to recognize and detect objects. It refers to a group of pixels that define the borders of an object in the image. Blob analysis algorithms vary. One of these techniques is the linked component labeling algorithm, which is commonly used in image processing and assigns the same label to all pixels of an object. In this study, the connected component labeling algorithm for detecting objects in the image was prepared to be implemented on the FPGA structure and tested in a simulation environment. Since processing a full image frame at once is inefficient in terms of memory resources, the image was read and processed line by line. Each pixel was labeled in a single pass through the image frame, and the area, boundaries, and centroid of each blob were determined by combining the corresponding pixels. This provides the foundation for the application of the CCL algorithm, allowing the use of PL-PS structures together. Future research will focus on constructing an efficient system that applies the blob identification procedure to the PS and PL components of the ZYNQ structure.

 Image processing is a branch of computer science that tries to extract information and identify objects from digital photographs through processing and analysis. Today, imaging systems are used in almost every aspect of our lives, particularly in medicine, the defense industry, security, automobile, and automation. As a result, the image must be processed in accordance with the intended purposes. Blob detection is a concept used in image processing to recognize and detect objects. It refers to a group of pixels that define the borders of an object in the image. Blob analysis algorithms vary. One of these techniques is the linked component labeling algorithm, which is commonly used in image processing and assigns the same label to all pixels of an object. In this study, the connected component labeling algorithm for detecting objects in the image was prepared to be implemented on the FPGA structure and tested in a simulation environment. Since processing a full image frame at once is inefficient in terms of memory resources, the image was read and processed line by line. Each pixel was labeled in a single pass through the image frame, and the area, boundaries, and centroid of each blob were determined by combining the corresponding pixels. This provides the foundation for the application of the CCL algorithm, allowing the use of PL-PS structures together. Future research will focus on constructing an efficient system that applies the blob identification procedure to the PS and PL components of the ZYNQ structure.

 Image processing is a branch of computer science that tries to extract information and identify objects from digital photographs through processing and analysis. Today, imaging systems are used in almost every aspect of our lives, particularly in medicine, the defense industry, security, automobile, and automation. As a result, the image must be processed in accordance with the intended purposes. Blob detection is a concept used in image processing to recognize and detect objects. It refers to a group of pixels that define the borders of an object in the image. Blob analysis algorithms vary. One of these techniques is the linked component labeling algorithm, which is commonly used in image processing and assigns the same label to all pixels of an object. In this study, the connected component labeling algorithm for detecting objects in the image was prepared to be implemented on the FPGA structure and tested in a simulation environment. Since processing a full image frame at once is inefficient in terms of memory resources, the image was read and processed line by line. Each pixel was labeled in a single pass through the image frame, and the area, boundaries, and centroid of each blob were determined by combining the corresponding pixels. This provides the foundation for the application of the CCL algorithm, allowing the use of PL-PS structures together. Future research will focus on constructing an efficient system that applies the blob identification procedure to the PS and PL components of the ZYNQ structure.

 Görüntü işleme dijital görüntüyü işleyip analiz ederek belirli bilgileri çıkarmayı ve nesnelerin tanımlanmasını hedefleyen bilgisayar bilimidir. Görüntüleme sistemleri günümüzde tıp, savunma sanayi, güvenlik, otomotiv ve otomasyon alanları başta olmak üzere hayatımızın her alanında yer almaktadır. Bundan dolayı görüntünün belirlenen amaçlar doğrultusunda işlenmesi gerekmektedir. Blob tespiti görüntü işlemede nesne tanıma, algılama için kullanılan bir kavramdır ve görüntüdeki bir nesnenin sınırlarını belirleyen bir grup pikseli ifade eder. Blob analizi için farklı algoritmalar geliştirilmiştir. Bu algoritmalardan birisi görüntü işlemede yaygın olarak kullanılan ve amacı bir nesneye ait tüm piksellere aynı etiketi atamak olan bağlantılı bileşen etiketleme algoritmasıdır. Bu çalışmada görüntüdeki nesnelerin tespiti için bağlantılı bileşen etiketleme algoritması FPGA yapısı üzerinde uygulanmak üzere hazırlanmış ve simülasyon ortamında test edilmiştir. Bir görüntü karesinin tamamının aynı anda işlenmesi bellek kaynakları açısından verimsiz olduğundan dolayı görüntü satır satır okunup işlenmiştir. Görüntü karesinden tek geçişte her piksel etiketlenmiş ve bağlantılı pikseller birleştirilerek her blobun alanı, sınırları ve ağırlık merkezi hesaplanmıştır. Bu şekilde PL-PS yapılarının bir arada kullanılmasına imkân sağlayacak CCL algoritmasının uygulanması için altyapı sağlanmıştır. İlerde yapılacak çalışmalarda blob tespiti işlemi ZYNQ yapısının PS ve PL kısımlarına uygulanarak verimli bir sistem geliştirilmesi hedeflenmektedir.

Makale detayları

Implementation of Single-Scan Connected Component Labeling for FPGA Hardware

Yazarlar:

İsmail Yabanova | Tarık Ünler

Yayıncı:

Necmettin Erbakan University

Ana Başlıklar:

Görüntü İşleme | Gömülü Sistemler

Dergi:

Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.7 Issue 1, pp.12-21

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001338800

pISSN:

2667-7989

Blob analysis

Connected Component Labeling

FPGABağlantılı Bileşen Etiketleme

Blob Analizi

FPGA

Sayı

Makale detayları

Implementation of Single-Scan Connected Component Labeling for FPGA Hardware

Yazarlar: İsmail Yabanova | Tarık Ünler

Yayıncı: Necmettin Erbakan University

Ana Başlıklar: Görüntü İşleme | Gömülü Sistemler

Dergi: Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.7 Issue 1, pp.12-21

Belge Tipi: Makale

Yayın Tarihi: 4/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001338800

pISSN: 2667-7989

Blob analysis

Connected Component Labeling

FPGABağlantılı Bileşen Etiketleme

Blob Analizi

FPGA

Sayı

Yazarlar:

İsmail Yabanova | Tarık Ünler

Yayıncı:

Necmettin Erbakan University

Ana Başlıklar:

Görüntü İşleme | Gömülü Sistemler

Dergi:

Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi Vol.7 Issue 1, pp.12-21

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001338800

pISSN:

2667-7989