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Attention-based Convolutional Neural Network for Computer Vision Color Constancy


Koščević, Karlo; Subašić, Marko; Lončarić, Sven
Attention-based Convolutional Neural Network for Computer Vision Color Constancy // Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis
Dubrovnik, Hrvatska, 2019. str. 372-377 doi:10.1109/ISPA.2019.8868806 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Attention-based Convolutional Neural Network for Computer Vision Color Constancy

Autori
Koščević, Karlo ; Subašić, Marko ; Lončarić, Sven

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis / - , 2019, 372-377

Skup
2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)

Mjesto i datum
Dubrovnik, Hrvatska, 23-25.09.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Attention mechanism, color constancy, convolutional neural networks, deep learning, illumination estimation, image enhancement

Sažetak
Achieving color constancy is an important part of image preprocessing pipeline of contemporary digital cameras. Its goal is to eliminate the influence of the illumination color on the colors of the objects in the image scene. State-of-the- art results have been achieved with learning- based methods, especially when the deep learning approaches have been applied. Several methods that are combining local patches for global illumination estimations exist. However, in this paper, a new convolutional neural network architecture is proposed. It is trained to look for the regions, i.e., patches in the image where the most useful information about the scene illumination is contained. This is achieved with the attention mechanism stacked on top of the pretrained convolutional neural network. Additionally, the common problem of the lack of data in color constancy benchmark datasets is alleviated utilizing the stage-wise training. Experimental results show that the proposed approach achieves competitive results.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2016-06-2092 - Metode i algoritmi za poboljšanje slika u boji u stvarnom vremenu (Sven Lončarić, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

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