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LUMINANCE ESTIMATION FROM SURFACES WITH DIFFERENT COLOR TEMPERATURE AND LAMP ILLUMINATION ANGLE: A DEEP LEARNING-BASED APPROACH

Yıl 2025, Cilt: 28 Sayı: 2, 883 - 896, 03.06.2025

Birkan Büyükarıkan , Erkan Ülker

https://doi.org/10.17780/ksujes.1626795

Öz

Traditional methods of luminance estimation are performed with the help of electronic systems. However, the changes in lighting properties, such as color temperature and the lamp illumination angle, affect the luminance on the object, making luminance estimation difficult compared to traditional methods. Therefore, this study proposes an image-based approach using convolutional neural networks (CNNs) models to provide an alternative solution for luminance estimation. In this study, luminance estimation is performed on defective and healthy apple images by considering the effects of color temperature and lamp illumination angle. According to the results, the GoogLeNet model exhibited the best performance at values where the learning rate was 0.001 and the batch size was eight. It also performed the best luminance estimation with a lower Root Mean Square Error (RMSE) value. According to color temperatures, defective apples showed the lowest RMSE value at warm white light, and healthy apples showed the lowest RMSE value at cold white light. According to color temperatures, the best luminance estimation is a 5.023 cd/m² RMSE value at a cold white light. According to lamp angle, defective apples obtained the lowest RMSE value at 5.106 cd/m² at a 60-degree angle, and healthy apples obtained the lowest RMSE value at 6.411 cd/m² at a 45-degree angle.

Anahtar Kelimeler

Luminance estimation color temperature lamp illumination angle deep learning

Teşekkür

This work was supported by the Scientific Research Project at Konya Technical University, Konya, Turkey (No. 201113006).

Kaynakça

  • Aleixos, N., Blasco, J., Navarron, F., & Moltó, E. (2002). Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Computers and electronics in agriculture, 33 (2), 121-137. https://doi.org/10.1016/S0168-1699(02)00002-9
  • Auersignal. All about luminous intensity, luminous flux & illuminance. (2022). https://www.auersignal.com/en/technical-information/visual-signallingequipment/luminous-intensity/#What%20is%20the%20solid%20angle? Accessed 23.7.2022
  • Benweilight. Aydınlatma ışını açılarının yorumlanması ve analizi. (2021). https://tr.benweilight.com/info/interpretation-and-analysis-of-illumination-be-61992668.html Accessed 21.12.2024.
  • Brosnan, T. & Sun, D.-W. (2004). Improving quality inspection of food products by computer vision––a review. Journal of food engineering, 61 (1), 3-16. https://doi.org/10.1016/S0260-8774(03)00183-3
  • Buyukarikan, B. & Ulker, E. (2022). Classification of physiological disorders in apples fruit using a hybrid model based on convolutional neural network and machine learning methods. Neural Computing and Applications, 34 (19), 16973-16988. https://doi.org/10.1007/s00521-022-07350-x
  • Buyukarikan, B. & Ulker, E. (2023). Classification of physiological disorders in apples using deep convolutional neural network under different lighting conditions. Multimedia Tools and Applications, 82 (21), 32463-32483. https://doi.org/10.1007/s11042-023-14766-7
  • Büyükarıkan, B. (2022). Aydınlatmanın görüntü işleme problemlerine etkisinin yapay zeka teknikleri kullanılarak analizi. Doktora Tezi. Konya Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü Bilgisayar Mühendisliği Anabilim Dalı, Konya 147s.
  • Büyükarıkan, B. & Ülker, E. (2022). Using convolutional neural network models illumination estimation according to light colors. Optik, 271, 170058. https://doi.org/10.1016/j.ijleo.2022.170058
  • Catalbas, M. C. & Kobav, M. B. (2022). Measurement of correlated color temperature from RGB images by deep regression model. Measurement, 195, 111053. https://doi.org/10.1016/j.measurement.2022.111053
  • Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) 2017 (pp. 1251-1258). IEEE.
  • Girolami, A., Napolitano, F., Faraone, D. & Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat science, 93 (1), 111-118. https://doi.org/10.1016/j.meatsci.2012.08.010
  • Holtzschue, L. (2012). Understanding color: an introduction for designers. John Wiley & Sons.
  • Hong, S., Kim, I., Kim, H., Sohn, A., Choi, A. S., Sung, M. & Jeong, J. W. (2017). Evaluation of the visibility of colored objects under LED lighting with various correlated color temperatures. Color Research & Application, 42 (1), 78-88. https://doi.org/10.1002/col.22048
  • Huang, Y.-S., Luo, W.-C., Wang, H.-C., Feng, S.-W., Kuo, C.-T. & Lu, C.-M. (2017). How smart LEDs lighting benefit color temperature and luminosity transformation. Energies, 10 (4), 518. https://doi.org/10.3390/en10040518
  • Huang, Z. & Wei, M. (2021). Effects of adapting luminance and CCT on appearance of white and degree of chromatic adaptation, part II: extremely high adapting luminance. Optics Express, 29 (25), 42319-42330. https://doi.org/10.1364/OE.27.009276
  • Iacomussi, P., Radis, M., Rossi, G. & Rossi, L. (2015). Visual comfort with LED lighting. Energy Procedia, 78, 729-734. https://doi.org/10.1016/j.egypro.2015.11.082
  • Ireri, D., Belal, E., Okinda, C., Makange, N. & Ji, C. (2019). A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artificial Intelligence in Agriculture, 2, 28-37. https://doi.org/10.1016/j.aiia.2019.06.001
  • Kamath, V., Kurian, C. P. & Padiyar, U. S. (2022). Development of bayesian neural network model to predict the correlated color temperature using digital camera and Macbeth ColorChecker chart. IEEE Access, 10, 55499-55507. https://doi.org/10.1109/ACCESS.2022.3177195
  • Kandpal, L. M., Lee, J., Bae, J., Lohumi, S. & Cho, B.-K. (2019). Development of a low-cost multi-waveband LED illumination imaging technique for rapid evaluation of fresh meat quality. Applied Sciences, 9 (5), 912. https://doi.org/10.3390/app9050912
  • Kayakuş, M. & Çevik, K. K. (2019). Estimating luminance measurements in road lighting by deep learning method. Artificial Intelligence and Applied Mathematics in Engineering Problems: Proceedings of the International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2019) (pp. 940-948).
  • Kim, J. H., Jang, J. W. & Jang, K. J. (2018). A color adjustment convolutional neural network for image superresolution. 2018 International Conference on Electronics, Information, and Communication (ICEIC) (pp. 1-2).
  • Kocabey, S. (2008). İç hacimlerde aydınlık düzeyi dağılımının bulunması ve sonlu elemanlar yöntemi ile incelenmesi. Doktora Tezi. Marmara Üniversitesi Fen Bilimleri Enstitüsü Elektrik Eğitimi Anabilim Dalı, İstanbul 155s.
  • Li, J., Chen, L., Huang, W., Wang, Q., Zhang, B., Tian, X., Fan, S. & Li, B. (2016). Multispectral detection of skin defects of bi-colored peaches based on vis–NIR hyperspectral imaging. Postharvest Biology and Technology, 112, 121-133. https://doi.org/10.1016/j.postharvbio.2015.10.007
  • Liu, Q., Huang, Z., Li, Z., Pointer, M. R., Zhang, G., Liu, Z., Gong, H. & Hou, Z. (2020a). A field study of the impact of indoor lighting on visual perception and cognitive performance in classroom. Applied Sciences, 10 (21), 7436. https://doi.org/10.3390/app10217436
  • Liu, Y., Colburn, A. & Inanici, M. (2020b). Deep neural network approach for annual luminance simulations. Journal of Building Performance Simulation, 13 (5), 532-554. https://doi.org/10.1080/19401493.2020.1803404
  • Luo, M. R. (2011). The quality of light sources. Coloration Technology, 127 (2), 75-87. https://doi.org/10.1111/j.1478-4408.2011.00282.x
  • Ma, L., Sun, K., Tu, K., Pan, L. & Zhang, W. (2017). Identification of double-yolked duck egg using computer vision. PloS one, 12 (12), e0190054. https://doi.org/10.1371/journal.pone.0190054
  • Manav, B. (2005). Ofislerde aydınlık düzeyi, parıltı farkı ve renk sıcaklığının görsel konfor koşullarına etkisi: Bir model çalışması. Doktora Tezi. İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü Mimarlık Anabilim Dalı, İstanbul 147s.
  • Nie, T. & Lv, X. (2023). Deep Learning-Based Machine Color Emotion Generation. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 14 (1), 1-14.
  • Odabas, M. S., Şenyer, N. & Kurt, D. (2023). Determination of quality grade of tobacco leaf by image processing on correlated color temperature. Concurrency and Computation: Practice and Experience, 35 (2), e7506. https://doi.org/10.1002/cpe.7506
  • Özkaya, M. & Tüfekçi, T. (2011). Aydınlatma tekniği. Birsen Yayınevi.
  • Saldaña, E., Siche, R., Luján, M. & Quevedo, R. (2013). Computer vision applied to the inspection and quality control of fruits and vegetables. Brazilian journal of food technology, 16, 254-272. https://doi.org/10.1590/S1981-67232013005000031
  • Sharma, G. & Bala, R. (2017). Digital color imaging handbook. CRC press.
  • Shi, B. & Chen, Z. (2021). A layer-wise multi-defect detection system for powder bed monitoring: Lighting strategy for imaging, adaptive segmentation and classification. Materials & Design, 210, 110035. https://doi.org/10.1016/j.matdes.2021.110035
  • Simon, A. P. & Uma, B. (2022). DeepLumina: A method based on deep features and luminance information for color texture classification. Computational Intelligence and Neuroscience, 2022 (1), 9510987. https://doi.org/10.1155/2022/9510987
  • Sims, P., Lai, Y.-Y. & Jory, T. (2021). A Review of Various Models for Classifying Light Source Color Rendition and Guide to Using LEDs to Achieve Fidelity Color Rendering for Retail and Other Indoor Environments, https://www.luminus.com/datasheets/WhitePaper_Rev1_Color_Rendering_211130.pdf Accessed 21.06.2022.
  • Smith, N. (2000). Lighting for Health and Safety (Vol. 1). Butterworth Heinemann Oxford.
  • Songwa, P. U., Saeed, A., Bhardwaj, S., Kruisselbrink, T. W. & Ozcelebi, T. (2021). LumNet: Learning to Estimate Vertical Visual Field Luminance for Adaptive Lighting Control. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5 (2), 1-20. https://doi.org/10.1145/3463500
  • Sumon, M. B. U. (2022), Deep Learning Methods for Classification of Photometric Images of Materials. Master Thesis. Norwegian University of Science and Technology 77p.
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. & Rabinovich, A. (2014). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
  • Unay, D. & Gosselin, B. (2005). Artificial neural network-based segmentation and apple grading by machine vision. IEEE International Conference on Image Processing 2005 (pp. II-630).
  • Wang, Z., Dong, Y., Sui, X., Shao, X., Li, K., Zhang, H., Xu, Z. & Zhang, D. (2024). An artificial intelligence-assisted microfluidic colorimetric wearable sensor system for monitoring of key tear biomarkers. npj Flexible Electronics, 8 (1), 35. https://doi.org/10.1038/s41528-024-00321-3
  • Wu, Y.-h., Hu, Y.-h., Jiang, F. & Zhang, L.-h. (2010). Design of temperature measurement system based on two-color imaging in adaptive optics of CCD. 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 1062-1067. https://doi.org/10.1117/12.865571
  • Xu, P., Tan, Q., Zhang, Y., Zha, X., Yang, S. & Yang, R. (2022). Research on maize seed classification and recognition based on machine vision and deep learning. Agriculture, 12 (2), 232. https://doi.org/10.3390/agriculture12020232
  • Yang, H.-H., Chen, W.-T., Luo, H.-L. & Kuo, S.-Y. (2021). Multi-modal bifurcated network for depth guided image relighting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 260-267).
  • Yang, Q. (1994). An approach to apple surface feature detection by machine vision. Computers and electronics in agriculture, 11 (2-3), 249-264. https://doi.org/10.1016/0168-1699(94)90012-4

FARKLI RENK SICAKLIĞI VE AYDINLATMA AÇISI İLE YÜZEYLERDEN PARILTI TAHMİNİ: DERİN ÖĞRENME TABANLI BİR YAKLAŞIM

Yıl 2025, Cilt: 28 Sayı: 2, 883 - 896, 03.06.2025

Birkan Büyükarıkan , Erkan Ülker

https://doi.org/10.17780/ksujes.1626795

Öz

Geleneksel yöntemlerle parıltı tahmini, elektronik sistemler yardımıyla gerçekleştirilmektedir. Ancak, nesne üzerindeki parıltıyı etkileyen renk sıcaklığı ve lambanın nesneyi aydınlatma açısı gibi aydınlatma özelliklerinin değişimi geleneksel yöntemlerle parıltı tahminini zorlaştırmaktadır. Dolayısıyla bu çalışmada, parıltı tahminine alternatif bir çözüm sunmak amacıyla evrişimsel sinir ağları (CNN) modellerinden yararlanılarak görüntü tabanlı bir yaklaşım önerilmiştir. Çalışmada, renk sıcaklığı ve lambanın konum açısının etkileri göz önünde bulundurularak, kusurlu ve kusursuz elma görüntüleri üzerinde parıltı tahmini yapılmıştır. Elde edilen sonuçlara göre, GoogLeNet modeli, öğrenme oranının 0.001 ve yığın boyutunun 8 olduğu değerlerde en iyi performansı sergileyerek, daha düşük Karekök Ortalama Kare Hata (RMSE) değeri ile en iyi parıltı tahminini gerçekleştirmiştir. Renk sıcaklıklarına göre, kusurlu elmalar en düşük RMSE değerini 2700 K renk sıcaklığında ve kusursuz elmalar ise en düşük RMSE değerini 6500 K renk sıcaklığında göstermiştir. Renk sıcaklıklarına göre; en iyi parıltı tahmini, 6500 K renk sıcaklığında 5.023 cd/m² RMSE değerindedir. Lamba açısına göre; kusurlu elmalar en düşük RMSE değerini 60 derecelik açıda 5.106 cd/m² ve kusursuz elmalar en düşük RMSE değeri ise 45 derecelik açıda 6.411 cd/m² olarak elde etmiştir.

Anahtar Kelimeler

Parıltı tahmini renk sıcaklığı lambanın aydınlatma açısı derin öğrenme

Teşekkür

This work was supported by the Scientific Research Project at Konya Technical University, Konya, Turkey (No. 201113006).

Kaynakça

  • Aleixos, N., Blasco, J., Navarron, F., & Moltó, E. (2002). Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Computers and electronics in agriculture, 33 (2), 121-137. https://doi.org/10.1016/S0168-1699(02)00002-9
  • Auersignal. All about luminous intensity, luminous flux & illuminance. (2022). https://www.auersignal.com/en/technical-information/visual-signallingequipment/luminous-intensity/#What%20is%20the%20solid%20angle? Accessed 23.7.2022
  • Benweilight. Aydınlatma ışını açılarının yorumlanması ve analizi. (2021). https://tr.benweilight.com/info/interpretation-and-analysis-of-illumination-be-61992668.html Accessed 21.12.2024.
  • Brosnan, T. & Sun, D.-W. (2004). Improving quality inspection of food products by computer vision––a review. Journal of food engineering, 61 (1), 3-16. https://doi.org/10.1016/S0260-8774(03)00183-3
  • Buyukarikan, B. & Ulker, E. (2022). Classification of physiological disorders in apples fruit using a hybrid model based on convolutional neural network and machine learning methods. Neural Computing and Applications, 34 (19), 16973-16988. https://doi.org/10.1007/s00521-022-07350-x
  • Buyukarikan, B. & Ulker, E. (2023). Classification of physiological disorders in apples using deep convolutional neural network under different lighting conditions. Multimedia Tools and Applications, 82 (21), 32463-32483. https://doi.org/10.1007/s11042-023-14766-7
  • Büyükarıkan, B. (2022). Aydınlatmanın görüntü işleme problemlerine etkisinin yapay zeka teknikleri kullanılarak analizi. Doktora Tezi. Konya Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü Bilgisayar Mühendisliği Anabilim Dalı, Konya 147s.
  • Büyükarıkan, B. & Ülker, E. (2022). Using convolutional neural network models illumination estimation according to light colors. Optik, 271, 170058. https://doi.org/10.1016/j.ijleo.2022.170058
  • Catalbas, M. C. & Kobav, M. B. (2022). Measurement of correlated color temperature from RGB images by deep regression model. Measurement, 195, 111053. https://doi.org/10.1016/j.measurement.2022.111053
  • Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) 2017 (pp. 1251-1258). IEEE.
  • Girolami, A., Napolitano, F., Faraone, D. & Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat science, 93 (1), 111-118. https://doi.org/10.1016/j.meatsci.2012.08.010
  • Holtzschue, L. (2012). Understanding color: an introduction for designers. John Wiley & Sons.
  • Hong, S., Kim, I., Kim, H., Sohn, A., Choi, A. S., Sung, M. & Jeong, J. W. (2017). Evaluation of the visibility of colored objects under LED lighting with various correlated color temperatures. Color Research & Application, 42 (1), 78-88. https://doi.org/10.1002/col.22048
  • Huang, Y.-S., Luo, W.-C., Wang, H.-C., Feng, S.-W., Kuo, C.-T. & Lu, C.-M. (2017). How smart LEDs lighting benefit color temperature and luminosity transformation. Energies, 10 (4), 518. https://doi.org/10.3390/en10040518
  • Huang, Z. & Wei, M. (2021). Effects of adapting luminance and CCT on appearance of white and degree of chromatic adaptation, part II: extremely high adapting luminance. Optics Express, 29 (25), 42319-42330. https://doi.org/10.1364/OE.27.009276
  • Iacomussi, P., Radis, M., Rossi, G. & Rossi, L. (2015). Visual comfort with LED lighting. Energy Procedia, 78, 729-734. https://doi.org/10.1016/j.egypro.2015.11.082
  • Ireri, D., Belal, E., Okinda, C., Makange, N. & Ji, C. (2019). A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artificial Intelligence in Agriculture, 2, 28-37. https://doi.org/10.1016/j.aiia.2019.06.001
  • Kamath, V., Kurian, C. P. & Padiyar, U. S. (2022). Development of bayesian neural network model to predict the correlated color temperature using digital camera and Macbeth ColorChecker chart. IEEE Access, 10, 55499-55507. https://doi.org/10.1109/ACCESS.2022.3177195
  • Kandpal, L. M., Lee, J., Bae, J., Lohumi, S. & Cho, B.-K. (2019). Development of a low-cost multi-waveband LED illumination imaging technique for rapid evaluation of fresh meat quality. Applied Sciences, 9 (5), 912. https://doi.org/10.3390/app9050912
  • Kayakuş, M. & Çevik, K. K. (2019). Estimating luminance measurements in road lighting by deep learning method. Artificial Intelligence and Applied Mathematics in Engineering Problems: Proceedings of the International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2019) (pp. 940-948).
  • Kim, J. H., Jang, J. W. & Jang, K. J. (2018). A color adjustment convolutional neural network for image superresolution. 2018 International Conference on Electronics, Information, and Communication (ICEIC) (pp. 1-2).
  • Kocabey, S. (2008). İç hacimlerde aydınlık düzeyi dağılımının bulunması ve sonlu elemanlar yöntemi ile incelenmesi. Doktora Tezi. Marmara Üniversitesi Fen Bilimleri Enstitüsü Elektrik Eğitimi Anabilim Dalı, İstanbul 155s.
  • Li, J., Chen, L., Huang, W., Wang, Q., Zhang, B., Tian, X., Fan, S. & Li, B. (2016). Multispectral detection of skin defects of bi-colored peaches based on vis–NIR hyperspectral imaging. Postharvest Biology and Technology, 112, 121-133. https://doi.org/10.1016/j.postharvbio.2015.10.007
  • Liu, Q., Huang, Z., Li, Z., Pointer, M. R., Zhang, G., Liu, Z., Gong, H. & Hou, Z. (2020a). A field study of the impact of indoor lighting on visual perception and cognitive performance in classroom. Applied Sciences, 10 (21), 7436. https://doi.org/10.3390/app10217436
  • Liu, Y., Colburn, A. & Inanici, M. (2020b). Deep neural network approach for annual luminance simulations. Journal of Building Performance Simulation, 13 (5), 532-554. https://doi.org/10.1080/19401493.2020.1803404
  • Luo, M. R. (2011). The quality of light sources. Coloration Technology, 127 (2), 75-87. https://doi.org/10.1111/j.1478-4408.2011.00282.x
  • Ma, L., Sun, K., Tu, K., Pan, L. & Zhang, W. (2017). Identification of double-yolked duck egg using computer vision. PloS one, 12 (12), e0190054. https://doi.org/10.1371/journal.pone.0190054
  • Manav, B. (2005). Ofislerde aydınlık düzeyi, parıltı farkı ve renk sıcaklığının görsel konfor koşullarına etkisi: Bir model çalışması. Doktora Tezi. İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü Mimarlık Anabilim Dalı, İstanbul 147s.
  • Nie, T. & Lv, X. (2023). Deep Learning-Based Machine Color Emotion Generation. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 14 (1), 1-14.
  • Odabas, M. S., Şenyer, N. & Kurt, D. (2023). Determination of quality grade of tobacco leaf by image processing on correlated color temperature. Concurrency and Computation: Practice and Experience, 35 (2), e7506. https://doi.org/10.1002/cpe.7506
  • Özkaya, M. & Tüfekçi, T. (2011). Aydınlatma tekniği. Birsen Yayınevi.
  • Saldaña, E., Siche, R., Luján, M. & Quevedo, R. (2013). Computer vision applied to the inspection and quality control of fruits and vegetables. Brazilian journal of food technology, 16, 254-272. https://doi.org/10.1590/S1981-67232013005000031
  • Sharma, G. & Bala, R. (2017). Digital color imaging handbook. CRC press.
  • Shi, B. & Chen, Z. (2021). A layer-wise multi-defect detection system for powder bed monitoring: Lighting strategy for imaging, adaptive segmentation and classification. Materials & Design, 210, 110035. https://doi.org/10.1016/j.matdes.2021.110035
  • Simon, A. P. & Uma, B. (2022). DeepLumina: A method based on deep features and luminance information for color texture classification. Computational Intelligence and Neuroscience, 2022 (1), 9510987. https://doi.org/10.1155/2022/9510987
  • Sims, P., Lai, Y.-Y. & Jory, T. (2021). A Review of Various Models for Classifying Light Source Color Rendition and Guide to Using LEDs to Achieve Fidelity Color Rendering for Retail and Other Indoor Environments, https://www.luminus.com/datasheets/WhitePaper_Rev1_Color_Rendering_211130.pdf Accessed 21.06.2022.
  • Smith, N. (2000). Lighting for Health and Safety (Vol. 1). Butterworth Heinemann Oxford.
  • Songwa, P. U., Saeed, A., Bhardwaj, S., Kruisselbrink, T. W. & Ozcelebi, T. (2021). LumNet: Learning to Estimate Vertical Visual Field Luminance for Adaptive Lighting Control. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5 (2), 1-20. https://doi.org/10.1145/3463500
  • Sumon, M. B. U. (2022), Deep Learning Methods for Classification of Photometric Images of Materials. Master Thesis. Norwegian University of Science and Technology 77p.
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. & Rabinovich, A. (2014). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
  • Unay, D. & Gosselin, B. (2005). Artificial neural network-based segmentation and apple grading by machine vision. IEEE International Conference on Image Processing 2005 (pp. II-630).
  • Wang, Z., Dong, Y., Sui, X., Shao, X., Li, K., Zhang, H., Xu, Z. & Zhang, D. (2024). An artificial intelligence-assisted microfluidic colorimetric wearable sensor system for monitoring of key tear biomarkers. npj Flexible Electronics, 8 (1), 35. https://doi.org/10.1038/s41528-024-00321-3
  • Wu, Y.-h., Hu, Y.-h., Jiang, F. & Zhang, L.-h. (2010). Design of temperature measurement system based on two-color imaging in adaptive optics of CCD. 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 1062-1067. https://doi.org/10.1117/12.865571
  • Xu, P., Tan, Q., Zhang, Y., Zha, X., Yang, S. & Yang, R. (2022). Research on maize seed classification and recognition based on machine vision and deep learning. Agriculture, 12 (2), 232. https://doi.org/10.3390/agriculture12020232
  • Yang, H.-H., Chen, W.-T., Luo, H.-L. & Kuo, S.-Y. (2021). Multi-modal bifurcated network for depth guided image relighting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 260-267).
  • Yang, Q. (1994). An approach to apple surface feature detection by machine vision. Computers and electronics in agriculture, 11 (2-3), 249-264. https://doi.org/10.1016/0168-1699(94)90012-4
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Görüşü, Görüntü İşleme, Makine Öğrenme (Diğer)
Bölüm Bilgisayar Mühendisliği
Yazarlar

Birkan Büyükarıkan 0000-0002-9703-9678

Erkan Ülker 0000-0003-4393-9870

Yayımlanma Tarihi 3 Haziran 2025
Gönderilme Tarihi 25 Ocak 2025
Kabul Tarihi 22 Mart 2025
Yayımlandığı Sayı Yıl 2025Cilt: 28 Sayı: 2

Kaynak Göster

APA Büyükarıkan, B., & Ülker, E. (2025). LUMINANCE ESTIMATION FROM SURFACES WITH DIFFERENT COLOR TEMPERATURE AND LAMP ILLUMINATION ANGLE: A DEEP LEARNING-BASED APPROACH. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 883-896. https://doi.org/10.17780/ksujes.1626795
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