Remember Me
Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:

OpenAIRE is about to release its new face with lots of new content and services.
During September, you may notice downtime in services, while some functionalities (e.g. user registration, login, validation, claiming) will be temporarily disabled.
We apologize for the inconvenience, please stay tuned!
For further information please contact helpdesk[at]openaire.eu

fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Afifi, Mahmoud (2018)
Languages: English
Types: Preprint
Subjects: Computer Science - Computer Vision and Pattern Recognition

Classified by OpenAIRE into

The goal of computational color constancy is to preserve the perceptive colors of objects under different lighting conditions by removing the effect of color casts caused by the scene's illumination. With the rapid development of deep learning based techniques, significant progress has been made in image semantic segmentation. In this work, we exploit the semantic information together with the color and spatial information of the input image in order to remove color casts. We train a convolutional neural network (CNN) model that learns to estimate the illuminant color and gamma correction parameters based on the semantic information of the given image. Experimental results show that feeding the CNN with the semantic information leads to a significant improvement in the results by reducing the error by more than 40%.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Abdelhamed, A. K. S. Two-illuminant estimation and user-preferred correction for image color constancy. PhD thesis, 2016.
    • [2] Barron, J. T. Convolutional color constancy. In Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 379-387.
    • [3] Barron, J. T., and Tsai, Y.-T. Fast fourier color constancy. In IEEE Conf. Comput. Vis. Paˆern Recognit (2017).
    • [4] Beigpour, S., Riess, C., Van De Weijer, J., and Angelopoulou, E. Multiilluminant estimation with conditional random €elds. IEEE Transactions on Image Processing 23, 1 (2014), 83-96.
    • [5] Brainard, D. H., and Wandell, B. A. Analysis of the retinex theory of color vision. JOSA A 3, 10 (1986), 1651-1661.
    • [6] Buchsbaum, G. A spatial processor model for object colour perception. Journal of the Franklin institute 310, 1 (1980), 1-26.
    • [7] Cheng, D., Kamel, A., Price, B., Cohen, S., and Brown, M. S. Two illuminant estimation and user correction preference. In Computer Vision and Paˆern Recognition (CVPR), 2016 IEEE Conference on (2016), IEEE, pp. 469-477.
    • [8] Cheng, D., Prasad, D. K., and Brown, M. S. Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution. JOSA A 31, 5 (2014), 1049-1058.
    • [9] Gehler, P. V., Rother, C., Blake, A., Minka, T., and Sharp, T. Bayesian color constancy revisited. In Computer Vision and Paˆern Recognition, 2008. CVPR 2008. IEEE Conference on (2008), IEEE, pp. 1-8.
    • [10] Head€arters, C. A adobe® rgb (1998) color image encoding.
    • [11] Hu, Y., Wang, B., and Lin, S. Fc 4: Fully convolutional color constancy with con€dence-weighted pooling. In Proceedings of the IEEE Conference on Computer Vision and Paˆern Recognition (2017), pp. 4085-4094.
    • [12] Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet classi€cation with deep convolutional neural networks. In Advances in neural information processing systems (2012), pp. 1097-1105.
    • [13] Lin, G., Milan, A., Shen, C., and Reid, I. Re€nenet: Multi-path re€nement networks for high-resolution semantic segmentation. In IEEE Conference on Computer Vision and Paˆern Recognition (CVPR) (2017).
    • [14] Zhang, J., Cao, Y., Wang, Y., Zha, Z.-J., Wen, C., and Chen, C. W. Fully pointwise convolutional neural network for modeling statistical regularities in natural images. arXiv preprint arXiv:1801.06302 (2018).
    • [15] Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A. Semantic understanding of scenes through the ade20k dataset. arXiv preprint arXiv:1608.05442 (2016).
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from

Cookies make it easier for us to provide you with our services. With the usage of our services you permit us to use cookies.
More information Ok