Özgün Bir Yaklaşim Ile Otomatik Gemi Tipi Siniflandirmasi

Umit Kacar; Deniz Kumlu; Murvet Kirci

doi:10.1109/SIU.2015.7130299

Özgün Bir Yaklaşim Ile Otomatik Gemi Tipi Siniflandirmasi

Umit Kacar
, Deniz Kumlu
, Murvet Kirci

Elektronik ve Hab.Müh.Bölümü

Istanbul Technical University
Deniz Harp Okulu

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

2 Atıf (Scopus)

Özet

This work classifies the ship types from color images by using cameras mounted on ships. Our data set contains 10 different ship types. The synthetic images used for training imported from Google 3D Warehouse. Test data set imported from Google Images and contains real ship images. This work aims to classify real ship images by using synthetic images. We present a novel approach for combining four features extracted from synthetic images and we have achieved % 90 accuracy.

Tercüme edilen katkı başlığı	A Novel approach for automatic ship type classification
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	2153-2156
Sayfa sayısı	4
ISBN (Elektronik)	9781467373869
DOI'lar	https://doi.org/10.1109/SIU.2015.7130299
Yayın durumu	Yayınlandı - 19 Haz 2015
Etkinlik	2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Malatya, Türkiye Süre: 16 May 2015 → 19 May 2015

Yayın serisi

Adı	2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings

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

???event.eventtypes.event.conference???	2015 23rd Signal Processing and Communications Applications Conference, SIU 2015
Ülke/Bölge	Türkiye
Şehir	Malatya
Periyot	16/05/15 → 19/05/15

Bibliyografik not

Publisher Copyright:
© 2015 IEEE.

Keywords

Feature combining
Feature extraction
Object recognition
Ship type classification

Belgeye Erişim

10.1109/SIU.2015.7130299

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Kacar, U., Kumlu, D., & Kirci, M. (2015). Özgün Bir Yaklaşim Ile Otomatik Gemi Tipi Siniflandirmasi. In 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings (pp. 2153-2156). Makale 7130299 (2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2015.7130299

Kacar, Umit ; Kumlu, Deniz ; Kirci, Murvet. / Özgün Bir Yaklaşim Ile Otomatik Gemi Tipi Siniflandirmasi. 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2153-2156 (2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings).

@inproceedings{27d91c829fe64654a778d91d685540de,

title = "{\"O}zg{\"u}n Bir Yakla{\c s}im Ile Otomatik Gemi Tipi Siniflandirmasi",

abstract = "This work classifies the ship types from color images by using cameras mounted on ships. Our data set contains 10 different ship types. The synthetic images used for training imported from Google 3D Warehouse. Test data set imported from Google Images and contains real ship images. This work aims to classify real ship images by using synthetic images. We present a novel approach for combining four features extracted from synthetic images and we have achieved \% 90 accuracy.",

keywords = "Feature combining, Feature extraction, Object recognition, Ship type classification",

author = "Umit Kacar and Deniz Kumlu and Murvet Kirci",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 ; Conference date: 16-05-2015 Through 19-05-2015",

year = "2015",

month = jun,

day = "19",

doi = "10.1109/SIU.2015.7130299",

language = "T{\"u}rk{\c c}e",

series = "2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2153--2156",

booktitle = "2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings",

address = "United States",

}

Kacar, U, Kumlu, D & Kirci, M 2015, Özgün Bir Yaklaşim Ile Otomatik Gemi Tipi Siniflandirmasi. in 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings., 7130299, 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 2153-2156, 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015, Malatya, Türkiye, 16/05/15. https://doi.org/10.1109/SIU.2015.7130299

Özgün Bir Yaklaşim Ile Otomatik Gemi Tipi Siniflandirmasi. / Kacar, Umit; Kumlu, Deniz; Kirci, Murvet.
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2153-2156 7130299 (2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings).

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

TY - GEN

T1 - Özgün Bir Yaklaşim Ile Otomatik Gemi Tipi Siniflandirmasi

AU - Kacar, Umit

AU - Kumlu, Deniz

AU - Kirci, Murvet

PY - 2015/6/19

Y1 - 2015/6/19

N2 - This work classifies the ship types from color images by using cameras mounted on ships. Our data set contains 10 different ship types. The synthetic images used for training imported from Google 3D Warehouse. Test data set imported from Google Images and contains real ship images. This work aims to classify real ship images by using synthetic images. We present a novel approach for combining four features extracted from synthetic images and we have achieved % 90 accuracy.

AB - This work classifies the ship types from color images by using cameras mounted on ships. Our data set contains 10 different ship types. The synthetic images used for training imported from Google 3D Warehouse. Test data set imported from Google Images and contains real ship images. This work aims to classify real ship images by using synthetic images. We present a novel approach for combining four features extracted from synthetic images and we have achieved % 90 accuracy.

KW - Feature combining

KW - Feature extraction

KW - Object recognition

KW - Ship type classification

UR - https://www.scopus.com/pages/publications/84939199591

U2 - 10.1109/SIU.2015.7130299

DO - 10.1109/SIU.2015.7130299

M3 - Konferans katkısı

AN - SCOPUS:84939199591

T3 - 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings

SP - 2153

EP - 2156

BT - 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015

Y2 - 16 May 2015 through 19 May 2015

ER -

Kacar U, Kumlu D, Kirci M. Özgün Bir Yaklaşim Ile Otomatik Gemi Tipi Siniflandirmasi. In 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2153-2156. 7130299. (2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings). doi: 10.1109/SIU.2015.7130299

Özgün Bir Yaklaşim Ile Otomatik Gemi Tipi Siniflandirmasi

Özet

Yayın serisi

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

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap