Voksel tabanli 3-b nesnelerin siniflandirilmasi için evrişimsel sinir aǧlari odakli bir yaklaşim

Ridvan Sirma; Berkan Dinar; Yusuf Huseyin Sahin; Gozde Unal

doi:10.1109/SIU.2018.8404568

Voksel tabanli 3-b nesnelerin siniflandirilmasi için evrişimsel sinir aǧlari odakli bir yaklaşim

Ridvan Sirma, Berkan Dinar, Yusuf Huseyin Sahin, Gozde Unal

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

In our work, 3D objects classification has been dealt with convolutional neural networks which is a common paradigm recently in image recognition. In the first phase of experiments, 3D models in ModelNet10 and ModelNet40 data sets were voxelized and scaled with certain parameters. Classical CNN and 3D Dense CNN architectures were designed for training the pre-processed data. In addition, the two trained CNNs were ensembled and the results of them were observed. A success rate of 95.37% achieved on ModelNet10 by using 3D dense CNN, a success rate of 91.24% achieved with ensemble of two CNNs on ModelNet40.

Tercüme edilen katkı başlığı	A convolutional neural networks oriented approach for voxel-based 3D object classification
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1-4
Sayfa sayısı	4
ISBN (Elektronik)	9781538615010
DOI'lar	https://doi.org/10.1109/SIU.2018.8404568
Yayın durumu	Yayınlandı - 5 Tem 2018
Etkinlik	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Süre: 2 May 2018 → 5 May 2018

Yayın serisi

Adı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Ülke/Bölge	Turkey
Şehir	Izmir
Periyot	2/05/18 → 5/05/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

Keywords

3D object classification
Convolutional neural networks
Deep learning

Belgeye Erişim

10.1109/SIU.2018.8404568

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Sirma, R., Dinar, B., Sahin, Y. H., & Unal, G. (2018). Voksel tabanli 3-b nesnelerin siniflandirilmasi için evrişimsel sinir aǧlari odakli bir yaklaşim. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404568

Sirma, Ridvan ; Dinar, Berkan ; Sahin, Yusuf Huseyin et al. / Voksel tabanli 3-b nesnelerin siniflandirilmasi için evrişimsel sinir aǧlari odakli bir yaklaşim. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

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abstract = "In our work, 3D objects classification has been dealt with convolutional neural networks which is a common paradigm recently in image recognition. In the first phase of experiments, 3D models in ModelNet10 and ModelNet40 data sets were voxelized and scaled with certain parameters. Classical CNN and 3D Dense CNN architectures were designed for training the pre-processed data. In addition, the two trained CNNs were ensembled and the results of them were observed. A success rate of 95.37% achieved on ModelNet10 by using 3D dense CNN, a success rate of 91.24% achieved with ensemble of two CNNs on ModelNet40.",

keywords = "3D object classification, Convolutional neural networks, Deep learning",

author = "Ridvan Sirma and Berkan Dinar and Sahin, {Yusuf Huseyin} and Gozde Unal",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

year = "2018",

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Sirma, R, Dinar, B, Sahin, YH & Unal, G 2018, Voksel tabanli 3-b nesnelerin siniflandirilmasi için evrişimsel sinir aǧlari odakli bir yaklaşim. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 2/05/18. https://doi.org/10.1109/SIU.2018.8404568

Voksel tabanli 3-b nesnelerin siniflandirilmasi için evrişimsel sinir aǧlari odakli bir yaklaşim. / Sirma, Ridvan; Dinar, Berkan; Sahin, Yusuf Huseyin et al.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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AU - Dinar, Berkan

AU - Sahin, Yusuf Huseyin

AU - Unal, Gozde

PY - 2018/7/5

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N2 - In our work, 3D objects classification has been dealt with convolutional neural networks which is a common paradigm recently in image recognition. In the first phase of experiments, 3D models in ModelNet10 and ModelNet40 data sets were voxelized and scaled with certain parameters. Classical CNN and 3D Dense CNN architectures were designed for training the pre-processed data. In addition, the two trained CNNs were ensembled and the results of them were observed. A success rate of 95.37% achieved on ModelNet10 by using 3D dense CNN, a success rate of 91.24% achieved with ensemble of two CNNs on ModelNet40.

AB - In our work, 3D objects classification has been dealt with convolutional neural networks which is a common paradigm recently in image recognition. In the first phase of experiments, 3D models in ModelNet10 and ModelNet40 data sets were voxelized and scaled with certain parameters. Classical CNN and 3D Dense CNN architectures were designed for training the pre-processed data. In addition, the two trained CNNs were ensembled and the results of them were observed. A success rate of 95.37% achieved on ModelNet10 by using 3D dense CNN, a success rate of 91.24% achieved with ensemble of two CNNs on ModelNet40.

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Sirma R, Dinar B, Sahin YH , Unal G. Voksel tabanli 3-b nesnelerin siniflandirilmasi için evrişimsel sinir aǧlari odakli bir yaklaşim. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-4. (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). doi: 10.1109/SIU.2018.8404568

Voksel tabanli 3-b nesnelerin siniflandirilmasi için evrişimsel sinir aǧlari odakli bir yaklaşim

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap