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Yapay Zeka Hızlı Bakış & Çıkarımlar
AI Insight⚡ TL;DR(1 Cümlede Hızlı Çıkarım)
In training of MRF-CNN, Recursive Perceptron Learning Algorithms (RPLA) is studied.
📌 3 Maddede Bu Makale
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Amaç & Hipotez:In this paper, to improve image performance of biomedical data, Markov Random Field (MRF) and Cellular Neural Network (CNN) structures are combined and a new approach, Markov Random Field-Cellular Neural Networks (MRF-CNN) is introduced.
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Temel Bulgular & Veri:MRF-CNN structure can be applied to biomedical data for various image processing problems such as noise filtering, edge detecting, blank filing etc., with noise variance up to 9 dB and better results are obtained according to MRF and CNN schemes.
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Sonuç & Katkı:In training of MRF-CNN, Recursive Perceptron Learning Algorithms (RPLA) is studied.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri
Abstract
In this paper, to improve image performance of biomedical data, Markov Random Field (MRF) and Cellular Neural Network (CNN) structures are combined and a new approach, Markov Random Field-Cellular Neural Networks (MRF-CNN) is introduced. MRF-CNN structure can be applied to biomedical data for various image processing problems such as noise filtering, edge detecting, blank filing etc., with noise variance up to 9 dB and better results are obtained according to MRF and CNN schemes. In training of MRF-CNN, Recursive Perceptron Learning Algorithms (RPLA) is studied.
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Bu Makaleye Atıf Yap
KILIÇ, N., UÇAN, O. N. (2022). Biomedical image processing using combined MRF-CNN method. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 4 - Sayı: 2, 1227–1231. https://ajindex.com/articles/543886