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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Recognition of Real-World Texture Images Under Challenging Conditions With Deep Learning

Derin Öğrenme Kullanarak Gerçek Dünya Doku Görüntülerinin Zorlu Koşullarda Tanınması

How to cite: Yıldırım , Uçar A, Baloğlu UB. Recognition of real-world texture images under challenging conditions with deep learning. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2018; 1(2): 122-126. DOI: 10.54856/jiswa.201812039

Full Text: PDF, in Turkish.

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Title: Recognition of Real-World Texture Images Under Challenging Conditions With Deep Learning

Abstract: Images obtained from the real world environments usually have various distortions in image quality. For example, when an object in motion is filmed, or when an environment is being filmed on the move, motion tracking effects occur on the image. Increasing the recognition performance of expert systems, which perform image recognition on data obtained under such conditions, is an important research area. In this study, we propose a Convolutional Neural Network (CNN) based Deep System Model (CNN-DSM) for accurate classification of images under challenging conditions. In the proposed model, a new layer is designed in addition to the classical CNN layers. This layer works as an enhancement layer. For the performance evaluations, various real world surface images were selected from the Curet database. Finally, results are presented and discussed.

Keywords: Deep learning; convolutional nerural networks; real-world texture images


Başlık: Derin Öğrenme Kullanarak Gerçek Dünya Doku Görüntülerinin Zorlu Koşullarda Tanınması

Özet: Gerçek dünyadan elde edilen görüntüler üzerinde çeşitli kalite bozulmaları meydana gelebilmektedir. Örneğin hareket halindeki bir nesnenin görüntülenmesi veya bir ortamın hareket halindeyken görüntülenmesi görüntü üzerinde hareket izi etkisi oluşturmaktadır. Bu tür şartlarda elde edilen görüntü verileri üzerinde tanıma işlemi gerçekleştiren uzman sistemlerin yüksek tanıma performansı sağlaması önemli bir çalışma konusudur. Bu makale çalışmasında, zorlu şartlar altında görüntülerin yüksek başarımla sınıflandırılması için Konvolüsyonel Sinir Ağı (KSA) tabanlı bir Derin Sistem Modeli (KSA-DSM) önerilmiştir. Önerilen bu modelde klasik KSA katmanlarına ek olarak bozulmuş görüntü verilerini katmanlara süren bir artırma katmanı tasarlanmıştır. Çalışmanın performans testleri için gerçek dünyadan elde edilen çeşitli yüzey görüntüleri Curet veri tabanından seçilerek kullanılmıştır. Elde edilen sonuçlar sunularak tartışılmıştır.

Anahtar kelimeler: Derin öğrenme; konvolüsyonel sinir ağı; gerçek dünya doku görüntüleri


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