scholarly journals Deep structured-output regression learning for computational color constancy

2016 ◽  
Author(s):  
Yanlin Qian ◽  
Ke Chen ◽  
Joni-Kristian Kamarainen ◽  
Jarno Nikkanen ◽  
Jiri Matas
Keyword(s):  
Color Constancy ◽  
Regression Learning ◽  
Structured Output ◽  
Computational Color Constancy

Computational Color Constancy under Multiple Light Sources

2020 ◽  
Vol 2020 (10) ◽  
pp. 135-1-135-6
Author(s):  
Jaeduk Han ◽  
Soonyoung Hong ◽  
Moon Gi Kang
Keyword(s):  
Computer Vision ◽  
Color Constancy ◽  
Artificial Light ◽  
Light Sources ◽  
Color Representation ◽  
Conventional Methods ◽  
Computational Color Constancy

Without sunlight, imaging devices typically depend on various artificial light sources. However, scenes captured with the artificial light sources often violate the assumptions employed in color constancy algorithms. These violations of the scenes, such as non-uniformity or multiple light sources, could disturb the computer vision algorithms. In this paper, complex illumination of multiple artificial light sources is decomposed into each illumination by considering the sensor responses and the spectrum of the artificial light sources, and the fundamental color constancy algorithms (e.g., gray-world, gray-edge, etc.) are improved by employing the estimated illumination energy. The proposed method effectively improves the conventional methods, and the results of the proposed algorithms are demonstrated using the images captured under laboratory settings for measuring the accuracy of the color representation.

scholarly journals Cascading Convolutional Color Constancy

2020 ◽  
Vol 34 (07) ◽  
pp. 12725-12732
Author(s):  
Huanglin Yu ◽  
Ke Chen ◽  
Kaiqi Wang ◽  
Yanlin Qian ◽  
Zhaoxiang Zhang ◽  
...  
Keyword(s):  
Network Optimization ◽  
Color Constancy ◽  
Superior Performance ◽  
Generalization Capability ◽  
The Public ◽  
Regression Learning ◽  
Properties Of Materials ◽  
Unique Framework ◽  
Camera Sensors ◽  
Coarse To Fine

Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy. However, it's still challenging due to intrinsic appearance and label ambiguities caused by unknown illuminants, diverse reflection properties of materials and extrinsic imaging factors (such as different camera sensors). In this paper, we introduce a novel algorithm – Cascading Convolutional Color Constancy (in short, C4) to improve robustness of regression learning and achieve stable generalization capability across datasets (different cameras and scenes) in a unique framework. The proposed C4 method ensembles a series of dependent illumination hypotheses from each cascade stage via introducing a weighted multiply-accumulate loss function, which can inherently capture different modes of illuminations and explicitly enforce coarse-to-fine network optimization. Experimental results on the public Color Checker and NUS 8-Camera benchmarks demonstrate superior performance of the proposed algorithm in comparison with the state-of-the-art methods, especially for more difficult scenes.

Computational Color Constancy: Survey and Experiments

2011 ◽  
Vol 20 (9) ◽  
pp. 2475-2489 ◽  
Author(s):  
A. Gijsenij ◽  
T. Gevers ◽  
J. van de Weijer
Keyword(s):  
Color Constancy ◽  
Computational Color Constancy
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