Short Communication
Year 2020,
Volume: 1 Issue: 1, 17 - 20, 01.01.2020
Abstract
Eski zamanlarda bilgisayarlar gelişmiş değildi,
sadece sayısal verileri işleyebiliyorlardı. Ancak günümüzde ki bilgisayarlar,
gelişmiş teknikleri ve algoritmaları kullanarak, sayısal veri işlemenin yanı sıra
görüntüleri otomatik olarak işleyebilir ve kategorize edebilirler. Görüntülerin
analizinde, bilgisayarlar görüntüleri daha ayrıntılı olarak kolayca
tanımlayabilirler. Günümüzde, görüntü işleme için birçok yöntem vardır ve bu
yöntemler gün geçtikçe artmaktadır. Bu makalede, derin öğrenme ile görüntü
analizi üzerinde çalışan en yeni bir yöntemi gözden geçireceğiz.
Keywords
References
- [1] Yian Seo., Kyung-shik Shin, 2018.Hierarchical convolutional neural networks for fashion image classification.[2] Boukaye Boubacar Traore, Bernard Kamsu- Foguem, Fana Tangara, 2018Deep convolution neural network for image recognition[3] Geert Litjens, Thijs Kooi , Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, JeroenA.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez 2017A survey on deep learning in medical image analysis[4] Dinggang Shen, Guorong Wu, and Heung-Il Suk 2017 - Deep Learning in Medical Image Analysis[5] Renoh Johnson Chalakkal, Waleed Habib Abdulla,Sinumol Sukumaran,Thulaseedharan 2018Quality and content analysis of fundus images using deep learning[6] Yuting Lyu, Junghui Chen, Zhihuan Song 2019Image-based process monitoring using deep learning framework.[7] Weibo Liua, Zidong Wanga, Xiaohui Liua, Nianyin Zengb, Yurong Liuc,d, Fuad E. Alsaadid 2016 A survey of deep neural network architectures and their applications[10] Ross Girshick,Microsoft Research IEEE- Fast R-CNNinternet makale kaynakları:[8] R-CNN https://github.com/rbgirshick/rcnn[9]R-CNN https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e
Year 2020,
Volume: 1 Issue: 1, 17 - 20, 01.01.2020
Abstract
References
- [1] Yian Seo., Kyung-shik Shin, 2018.Hierarchical convolutional neural networks for fashion image classification.[2] Boukaye Boubacar Traore, Bernard Kamsu- Foguem, Fana Tangara, 2018Deep convolution neural network for image recognition[3] Geert Litjens, Thijs Kooi , Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, JeroenA.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez 2017A survey on deep learning in medical image analysis[4] Dinggang Shen, Guorong Wu, and Heung-Il Suk 2017 - Deep Learning in Medical Image Analysis[5] Renoh Johnson Chalakkal, Waleed Habib Abdulla,Sinumol Sukumaran,Thulaseedharan 2018Quality and content analysis of fundus images using deep learning[6] Yuting Lyu, Junghui Chen, Zhihuan Song 2019Image-based process monitoring using deep learning framework.[7] Weibo Liua, Zidong Wanga, Xiaohui Liua, Nianyin Zengb, Yurong Liuc,d, Fuad E. Alsaadid 2016 A survey of deep neural network architectures and their applications[10] Ross Girshick,Microsoft Research IEEE- Fast R-CNNinternet makale kaynakları:[8] R-CNN https://github.com/rbgirshick/rcnn[9]R-CNN https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e
There are 1 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | January 1, 2020 |
| Submission Date | May 17, 2019 |
| Acceptance Date | November 11, 2019 |
| Published in Issue | Year 2020 Volume: 1 Issue: 1 |
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