Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 35 Sayı: 1, 479 - 494, 25.10.2019
https://doi.org/10.17341/gazimmfd.480562

Öz

Kaynakça

  • 1. Uzar M Automatic Building Extraction with Multi-sensor Data Using Rule-based Classification, European Journal of Remote Sensing, 47:1, 1-18, 2014.
  • 2. Fırat O. ve Erdoğan, M., Nesne (obje) tabanlı sınıflandırma tekniği ile multispektral hava fotoğraflarından otomatik bina çıkarımı, TUFUAB VIII. Teknik Sempozyumu, 21-23 Mayıs, Konya, Türkiye, 2015.
  • 3. Benz, U., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M., Multi-resolution, nesnect-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS Journal of Photogrammetry and Remote Sensing, 58, 239-258, 2004.
  • 4. Blaschke T., Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, no. 1, pp. 2–16, 2010.
  • 5. Blaschke, T., Hay, G., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R., van der Meer, F., van der Werff, H., van Collie, F., ve Tiede, D., Geographic Object-Based Image Analysis - Towards a new paradigm, ISPRS J. Photogramm. Remote Sens 87, 180–191, 2014.
  • 6. Gu, H., Li H., Yan, L., Liu Z., Blaschke T. ve Soergel, U., An object-based semantic classification method for high resolution remote sensing ımagery using ontology, Remote Sens., 9, 329, 2017.
  • 7. Hay, G. J., Castılla, G., Wulder, M. A., Ruiz, J. R., An automated object-based approach for the multiscale image segmentation of forest scenes, International Journal of Applied Earth Observation and Geoinformation, 7 (4), 339-359, 2005.
  • 8. Ranade R. A., Object Recognition of Very High Resolution Satellite Imagery using Ontology, Master of Technology in Remote Sensing and GIS in Andhra University, India, 2015.
  • 9. Hay, G., Castilla, G., Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT), 1st International Conference on Object-based Image Analysis (OBIA 2006), 2006.
  • 10. Robertson, L.D., King, D.J., Comparison of pixel—And object-based classification in land-cover change mapping. Int. J. Remote Sens., 32, 1505–1529, 2011.
  • 11. Duro, D.C., Franklin, S.E., Dubé, M.G., A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ, 118, 259–272, 2012.
  • 12. Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q., Per-pixel vs. object-based classification of urban land-cover extraction using high spatial resolution imagery, Remote Sens. Environ., 115, 1145–1161, 2011.
  • 13. Gu, H. Y., Li, H.T., Yan, L., Lu, X. J., A framework for geographıc object-based ımage analysıs (geobıa) based on geographıc ontology, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W4, 2015 International Workshop on Image and Data Fusion, 21 – 23 Temmuz, Hawaii, ABD, 2015.
  • 14. Arvor, D., Durieux, L., Andres, S., vd., Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 82, pp.125–137, 2013.
  • 15. Belgiu, M., Tomljenovic, I., Lampoltshammer, T. J., Blaschke, T., & Höfle, B, Ontology-based classification of building types detected from airborne laser scanning data. Remote Sensing, 6(2), pp.1347-1366, 2014.
  • 16. Gomes, J., Montenegro, N., Urbano, P. ve Duarte, J., A Land Use Identication and Visualization Tool Driven by OWL Ontologies, 2012.
  • 17. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1349–1380, 2000.
  • 18. Bouslah, M. ve Benblidia, N., Ontology based ımage annotation: A survey, LRDSI Laboratory, Computer Science Department, 2017.
  • 19. Bıttner, T. ve Winter, S., On Ontology in image analysis in integrated spatial database. Integrating spatial databases: digital images and GIS, Portland, ME, USA, Springer-Verlag, 1999.
  • 20. Câmara, G., Egenhofer, M. vd., What’s in an Image? COSIT ‘01-Conference on Spatial Information Theory, Morro Bay, CA. Springer, 2001.
  • 21. Belgiu, M., Thomas, J., Ontology based interpretation of Very High Resolution imageries – grounding ontologies on visual interpretation keys 14–17, 2013.
  • 22. Lüscher, P., Weibel, R. ve Burghardt, D., Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data. Comput. Environ. Urban Syst. 33, pp.363–374, 2009.
  • 23. Gruber, T. R. vd., A translation approach to portable ontology specifications, Knowledge acquisition, vol. 5, no. 2, pp. 199–220, 1993.
  • 24. Agarwal, P.,. Ontological considerations in GIScience. International Journal of Geographical Information Science, 19, pp.501–536, 2005.
  • 25. Andres, S., Arvor, D. ve Pierkot, C., Towards an ontological approach for classifying remote sensing images. In: Signal Image Technology and Internet Based Systems (SITIS), IEEE, 2012 Eighth International Conference, pp.825–832, 2012.
  • 26. Mark, D.M., Smith, B., Egenhofer, M.J. ve Hirtle, S.C., Ontological foundations for geographic information science. In: McMaster, R.B., Usery, E.L. (Eds.), A Research Agenda for Geographic Information Science. CRC Press, Boca Raton, FLA, 2005.
  • 27. Teller, J., Billen, R., Cutting-Decelle, A.F.: Bringing urban ontologies into practice. Journal of Information Technology in Construction 15, 2010.
  • 28. Yue, P., Di, L., Wei, Y. ve Han, W., Intelligent services for discovery of complex geospatial features from remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing ,83,pp.151-164, 2013.
  • 29. Jesús, M. A.-J., Luis, D. ve José A. P.-F., A Framework for Ocean Satellite Image Classification Based on Ontologies. IEEE Journal of selected topics in applied earth observations and remote sensing, 6(2), pp.1048-1063, 2013.
  • 30. Dejrriri, K. ve Malki, M., Object-based image analysis and data mining for building ontology of informal urban settlements. Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85371I, 2012.
  • 31. Forestier, G., Puissant, A., Wemmert, C., vd., Knowledge-based region labeling for remote sensing image interpretation. Computers, Environment and Urban Systems, 36(5),pp.470–480, 2012.
  • 32. Kyzirakos, K., Karpathiotakis, M., Garbis, G. Vd.. Wildfire monitoring using satellite images, ontologies and linked geospatial data. Web Semantics: Science, Services and Agents on the World Wide Web, 24, pp.18–26, 2014.
  • 33. Bouyerbou H., Bechkoum, K., Benblidia N. ve Lepage R., Ontology-based semantic classification of satellite images: Case of major disasters”. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, pp. 2347-2350, 2014.
  • 34. Zevenbergen L. ve Thorne C., Quantitative Analysis of Land Surface Topography, Earth Surface Processes and Landforms 12:47–56, 1987.
  • 35. Navulur K., Multispectral Image Analysis Using the Object-Oriented Paradigm, CRC Press, Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742, 2007.
  • 36. Kim, N,. 2.5D Reconstruction of Building from very High Resolution SAR and Optical Data by Using Object-Oriented Image Analysis Technique. The Faculty of Geo-Information Science and Earth Observation of the University of Twente, Master of Science, The Netherlands, 2011.
  • 37. Bülent Bostancı, Neşe Yılmaz Bakır, Umut Doğan, Merve Koçak Güngör, Bulanık karar verme teknikleri ile CBS destekli konut memnuniyeti araştırması, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 32:4 (2017) 1193-1207, 2017.
  • 38. Arpinar, I. B., Sheth, A. ve Ramakrishnan, C., Geospatial Ontology Development and Semantic Analytics, Handbook of Geographic Information Science, Eds: J. P. Wilson and A. S. Fotheringham, Blackwell Publishing, (in print), 2004.
  • 39. Masri, A., Zeitouni, K., Kedad, Z., ve Leroy, B., An Automatic Matcher and Linker for Transportation Datasets, ISPRS Int. J. Geo-Inf., 6, 29, 2017.
  • 40. Hudelot, ., ve Thonnat, M., A cognitive vision platform for automatic recognition of natural complex objects,” in Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on. IEEE, pp. 398–405, 2003.
  • 41. Rajbhandari, S., Aryal J., Osborn, J., Musk, R. ve Lucieer, A., Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis, ISPRS Int. J. Geo-Inf., 6, 386, 2017.
  • 42. Falquet, G., Métral, C., Teller, J. Ve Tweed, C., Ontologies in Urban Development Projects, Advanced Information and Knowledge Processing, Springer-Verlag London Limited 2011.
  • 43. Lampoltshammer, T., ve Heistracher, T., Ontology Evaluation with Protégé using OWLET, Infocommunications Journal, 2014.
  • 44. Belgiu, M., Lampoltshammer, T. J. ve Hofer, B., An Extension of an Ontology-Based Land Cover Designation Approach for Fuzzy Rules, GI_Forum 2013. Creating the GISociety, 2013.
  • 45. Almendros-Jiménez, J. M., Domene, L. ve Piedra-Fernández, J. A., Framework for Ocean Satellite Image Classification Based on Ontologies, IEEE Journal of Selected Topıcs in Applied Earth Observations and Remote Sensıng, vol. 6, no. 2, 2013.
  • 46. Teller, J.: Ontologies for an improved communication in urban development projects. In: Ontologies for Urban Development. Volume 61 of Studies in Computational Intelligence. Springer, 1-14, 2007.
  • 47. Wang, L., Shi, C., Diao, C., Ji, W. ve Yin, D., A survey of methods incorporating spatial information in image classification and spectral unmixing, Internatıonal Journal Of Remote Sensıng, vol. 37, no. 16, 3870–3910, 2016.
  • 48. McNeil, L. M., ve Kelso, T. S., Spatial Temporal Information Systems An Ontological Approach Using STK, Taylor & Francis Group, LLCCRC Press, 2014.

Nesne tabanlı görüntü analizinde yeni trend - ontoloji

Yıl 2020, Cilt: 35 Sayı: 1, 479 - 494, 25.10.2019
https://doi.org/10.17341/gazimmfd.480562

Öz

Kent yönetiminde, yaşam alanlarına ait
problemlerin çözümü, sağlıklı ve sürdürülebilir kentlerin oluşturulması, akıllı
şehirlerin altyapısının kurulması gibi amaçlar için mekânsal bilgi içeren
verilerden yararlanılmaktadır. Bu sebeple mekansal verilerin toplanması,
işlenmesi, değerlendirilmesi ve bilgiye dönüştürülmesi kent yöneticilerinin hızlı
ve doğru kararların verilebilmesi için önem arz etmektedir. Son yıllarda, mekansal
verilerin değerlendirilmesi çalışmalarında, obje çıkarım tekniklerinin
geliştirilmesi ve optimize edilmesi için farklı yöntem ve algoritmalar geliştirilmiştir.
Ancak bu çalışmalarda kullanılan mekansal veriler, çoğunlukla farklı veri
kaynaklarından elde edilmesi sebebiyle farklı teknik özelliklere (geometrik,
radyometrik, zamansal çözünürlük, vb.) sahip veriler olduğundan, mekansal
semantik kavramı özelinde heterojen bir yapı göstermektedir. Bu heterojen yapı uzman
bilgisinin kavramsallaştırılması, birlikte çalışabilirlik ve yeniden
kullanılabilirlik konularında problemler oluşturmaktadır. Ontoloji, uzman bilgisinin
kavramsallaştırılarak semantik olarak tam açıklanmış ve birbirleri ile bağlı
bir yapı sunması sebebi ile obje çıkarımı çalışmalarında heterojenlikten
kaynaklanan sorunların giderilmesinde güncel araştırma konusu haline gelmiştir.
Bu çalışmada, Kırklareli ili, Evrencik bölgesine ait LiDAR sistem verileri
kullanılarak ontoloji destekli obje çıkarımı hedeflenmiştir. Bu amaç
doğrultusunda obje tabanlı görüntü analiz yöntemi, bulanık mantık ile
sınıflandırma kullanılarak obje çıkarımı yapılmış ve kavramsal sınıf tanımları,
obje ve veri ilişkileri, kurallar ve aksiyomlar tanımlanarak semantik altyapı
modeli kurulmuştur. Bu çalışmanın sonucunda doğruluk analizi ve görüntü
objelerinin ontoloji ile entegrasyonu yapılmıştır.

Kaynakça

  • 1. Uzar M Automatic Building Extraction with Multi-sensor Data Using Rule-based Classification, European Journal of Remote Sensing, 47:1, 1-18, 2014.
  • 2. Fırat O. ve Erdoğan, M., Nesne (obje) tabanlı sınıflandırma tekniği ile multispektral hava fotoğraflarından otomatik bina çıkarımı, TUFUAB VIII. Teknik Sempozyumu, 21-23 Mayıs, Konya, Türkiye, 2015.
  • 3. Benz, U., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M., Multi-resolution, nesnect-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS Journal of Photogrammetry and Remote Sensing, 58, 239-258, 2004.
  • 4. Blaschke T., Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, no. 1, pp. 2–16, 2010.
  • 5. Blaschke, T., Hay, G., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R., van der Meer, F., van der Werff, H., van Collie, F., ve Tiede, D., Geographic Object-Based Image Analysis - Towards a new paradigm, ISPRS J. Photogramm. Remote Sens 87, 180–191, 2014.
  • 6. Gu, H., Li H., Yan, L., Liu Z., Blaschke T. ve Soergel, U., An object-based semantic classification method for high resolution remote sensing ımagery using ontology, Remote Sens., 9, 329, 2017.
  • 7. Hay, G. J., Castılla, G., Wulder, M. A., Ruiz, J. R., An automated object-based approach for the multiscale image segmentation of forest scenes, International Journal of Applied Earth Observation and Geoinformation, 7 (4), 339-359, 2005.
  • 8. Ranade R. A., Object Recognition of Very High Resolution Satellite Imagery using Ontology, Master of Technology in Remote Sensing and GIS in Andhra University, India, 2015.
  • 9. Hay, G., Castilla, G., Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT), 1st International Conference on Object-based Image Analysis (OBIA 2006), 2006.
  • 10. Robertson, L.D., King, D.J., Comparison of pixel—And object-based classification in land-cover change mapping. Int. J. Remote Sens., 32, 1505–1529, 2011.
  • 11. Duro, D.C., Franklin, S.E., Dubé, M.G., A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ, 118, 259–272, 2012.
  • 12. Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q., Per-pixel vs. object-based classification of urban land-cover extraction using high spatial resolution imagery, Remote Sens. Environ., 115, 1145–1161, 2011.
  • 13. Gu, H. Y., Li, H.T., Yan, L., Lu, X. J., A framework for geographıc object-based ımage analysıs (geobıa) based on geographıc ontology, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W4, 2015 International Workshop on Image and Data Fusion, 21 – 23 Temmuz, Hawaii, ABD, 2015.
  • 14. Arvor, D., Durieux, L., Andres, S., vd., Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 82, pp.125–137, 2013.
  • 15. Belgiu, M., Tomljenovic, I., Lampoltshammer, T. J., Blaschke, T., & Höfle, B, Ontology-based classification of building types detected from airborne laser scanning data. Remote Sensing, 6(2), pp.1347-1366, 2014.
  • 16. Gomes, J., Montenegro, N., Urbano, P. ve Duarte, J., A Land Use Identication and Visualization Tool Driven by OWL Ontologies, 2012.
  • 17. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1349–1380, 2000.
  • 18. Bouslah, M. ve Benblidia, N., Ontology based ımage annotation: A survey, LRDSI Laboratory, Computer Science Department, 2017.
  • 19. Bıttner, T. ve Winter, S., On Ontology in image analysis in integrated spatial database. Integrating spatial databases: digital images and GIS, Portland, ME, USA, Springer-Verlag, 1999.
  • 20. Câmara, G., Egenhofer, M. vd., What’s in an Image? COSIT ‘01-Conference on Spatial Information Theory, Morro Bay, CA. Springer, 2001.
  • 21. Belgiu, M., Thomas, J., Ontology based interpretation of Very High Resolution imageries – grounding ontologies on visual interpretation keys 14–17, 2013.
  • 22. Lüscher, P., Weibel, R. ve Burghardt, D., Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data. Comput. Environ. Urban Syst. 33, pp.363–374, 2009.
  • 23. Gruber, T. R. vd., A translation approach to portable ontology specifications, Knowledge acquisition, vol. 5, no. 2, pp. 199–220, 1993.
  • 24. Agarwal, P.,. Ontological considerations in GIScience. International Journal of Geographical Information Science, 19, pp.501–536, 2005.
  • 25. Andres, S., Arvor, D. ve Pierkot, C., Towards an ontological approach for classifying remote sensing images. In: Signal Image Technology and Internet Based Systems (SITIS), IEEE, 2012 Eighth International Conference, pp.825–832, 2012.
  • 26. Mark, D.M., Smith, B., Egenhofer, M.J. ve Hirtle, S.C., Ontological foundations for geographic information science. In: McMaster, R.B., Usery, E.L. (Eds.), A Research Agenda for Geographic Information Science. CRC Press, Boca Raton, FLA, 2005.
  • 27. Teller, J., Billen, R., Cutting-Decelle, A.F.: Bringing urban ontologies into practice. Journal of Information Technology in Construction 15, 2010.
  • 28. Yue, P., Di, L., Wei, Y. ve Han, W., Intelligent services for discovery of complex geospatial features from remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing ,83,pp.151-164, 2013.
  • 29. Jesús, M. A.-J., Luis, D. ve José A. P.-F., A Framework for Ocean Satellite Image Classification Based on Ontologies. IEEE Journal of selected topics in applied earth observations and remote sensing, 6(2), pp.1048-1063, 2013.
  • 30. Dejrriri, K. ve Malki, M., Object-based image analysis and data mining for building ontology of informal urban settlements. Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85371I, 2012.
  • 31. Forestier, G., Puissant, A., Wemmert, C., vd., Knowledge-based region labeling for remote sensing image interpretation. Computers, Environment and Urban Systems, 36(5),pp.470–480, 2012.
  • 32. Kyzirakos, K., Karpathiotakis, M., Garbis, G. Vd.. Wildfire monitoring using satellite images, ontologies and linked geospatial data. Web Semantics: Science, Services and Agents on the World Wide Web, 24, pp.18–26, 2014.
  • 33. Bouyerbou H., Bechkoum, K., Benblidia N. ve Lepage R., Ontology-based semantic classification of satellite images: Case of major disasters”. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, pp. 2347-2350, 2014.
  • 34. Zevenbergen L. ve Thorne C., Quantitative Analysis of Land Surface Topography, Earth Surface Processes and Landforms 12:47–56, 1987.
  • 35. Navulur K., Multispectral Image Analysis Using the Object-Oriented Paradigm, CRC Press, Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742, 2007.
  • 36. Kim, N,. 2.5D Reconstruction of Building from very High Resolution SAR and Optical Data by Using Object-Oriented Image Analysis Technique. The Faculty of Geo-Information Science and Earth Observation of the University of Twente, Master of Science, The Netherlands, 2011.
  • 37. Bülent Bostancı, Neşe Yılmaz Bakır, Umut Doğan, Merve Koçak Güngör, Bulanık karar verme teknikleri ile CBS destekli konut memnuniyeti araştırması, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 32:4 (2017) 1193-1207, 2017.
  • 38. Arpinar, I. B., Sheth, A. ve Ramakrishnan, C., Geospatial Ontology Development and Semantic Analytics, Handbook of Geographic Information Science, Eds: J. P. Wilson and A. S. Fotheringham, Blackwell Publishing, (in print), 2004.
  • 39. Masri, A., Zeitouni, K., Kedad, Z., ve Leroy, B., An Automatic Matcher and Linker for Transportation Datasets, ISPRS Int. J. Geo-Inf., 6, 29, 2017.
  • 40. Hudelot, ., ve Thonnat, M., A cognitive vision platform for automatic recognition of natural complex objects,” in Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on. IEEE, pp. 398–405, 2003.
  • 41. Rajbhandari, S., Aryal J., Osborn, J., Musk, R. ve Lucieer, A., Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis, ISPRS Int. J. Geo-Inf., 6, 386, 2017.
  • 42. Falquet, G., Métral, C., Teller, J. Ve Tweed, C., Ontologies in Urban Development Projects, Advanced Information and Knowledge Processing, Springer-Verlag London Limited 2011.
  • 43. Lampoltshammer, T., ve Heistracher, T., Ontology Evaluation with Protégé using OWLET, Infocommunications Journal, 2014.
  • 44. Belgiu, M., Lampoltshammer, T. J. ve Hofer, B., An Extension of an Ontology-Based Land Cover Designation Approach for Fuzzy Rules, GI_Forum 2013. Creating the GISociety, 2013.
  • 45. Almendros-Jiménez, J. M., Domene, L. ve Piedra-Fernández, J. A., Framework for Ocean Satellite Image Classification Based on Ontologies, IEEE Journal of Selected Topıcs in Applied Earth Observations and Remote Sensıng, vol. 6, no. 2, 2013.
  • 46. Teller, J.: Ontologies for an improved communication in urban development projects. In: Ontologies for Urban Development. Volume 61 of Studies in Computational Intelligence. Springer, 1-14, 2007.
  • 47. Wang, L., Shi, C., Diao, C., Ji, W. ve Yin, D., A survey of methods incorporating spatial information in image classification and spectral unmixing, Internatıonal Journal Of Remote Sensıng, vol. 37, no. 16, 3870–3910, 2016.
  • 48. McNeil, L. M., ve Kelso, T. S., Spatial Temporal Information Systems An Ontological Approach Using STK, Taylor & Francis Group, LLCCRC Press, 2014.
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Zeynep Şener 0000-0003-2209-4629

Melis Uzar 0000-0003-0873-3797

Yayımlanma Tarihi 25 Ekim 2019
Gönderilme Tarihi 8 Kasım 2018
Kabul Tarihi 20 Mayıs 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 35 Sayı: 1

Kaynak Göster

APA Şener, Z., & Uzar, M. (2019). Nesne tabanlı görüntü analizinde yeni trend - ontoloji. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(1), 479-494. https://doi.org/10.17341/gazimmfd.480562
AMA Şener Z, Uzar M. Nesne tabanlı görüntü analizinde yeni trend - ontoloji. GUMMFD. Ekim 2019;35(1):479-494. doi:10.17341/gazimmfd.480562
Chicago Şener, Zeynep, ve Melis Uzar. “Nesne Tabanlı görüntü Analizinde Yeni Trend - Ontoloji”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35, sy. 1 (Ekim 2019): 479-94. https://doi.org/10.17341/gazimmfd.480562.
EndNote Şener Z, Uzar M (01 Ekim 2019) Nesne tabanlı görüntü analizinde yeni trend - ontoloji. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35 1 479–494.
IEEE Z. Şener ve M. Uzar, “Nesne tabanlı görüntü analizinde yeni trend - ontoloji”, GUMMFD, c. 35, sy. 1, ss. 479–494, 2019, doi: 10.17341/gazimmfd.480562.
ISNAD Şener, Zeynep - Uzar, Melis. “Nesne Tabanlı görüntü Analizinde Yeni Trend - Ontoloji”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35/1 (Ekim 2019), 479-494. https://doi.org/10.17341/gazimmfd.480562.
JAMA Şener Z, Uzar M. Nesne tabanlı görüntü analizinde yeni trend - ontoloji. GUMMFD. 2019;35:479–494.
MLA Şener, Zeynep ve Melis Uzar. “Nesne Tabanlı görüntü Analizinde Yeni Trend - Ontoloji”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 35, sy. 1, 2019, ss. 479-94, doi:10.17341/gazimmfd.480562.
Vancouver Şener Z, Uzar M. Nesne tabanlı görüntü analizinde yeni trend - ontoloji. GUMMFD. 2019;35(1):479-94.