Color Constancy Beyond Standard Illuminants

Ulucan, Oguzhan; Karakaya, Diclehan; Ebner, Marc

doi:10.1109/icip46576.2022.9897781

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…With the advent of smartphones, probably more photographs and videos are taken than ever before, and proper color balancing is essential for viewing images under different lighting contexts. Thus, a multitude of algorithms for this task have been developed that allow estimating and compensating for illuminant effects, commonly known as “white balance” ( Barnard et al., 2002 ; Gijsenij & Gevers, 2011 ; Gao, Yang, Li, & Li, 2015 ; Akbarinia & Parraga, 2018 ; Ulucan, Ulucan, & Ebner, 2022 ). Most recently, deep neural networks were introduced for color constancy ( Lou, Gevers, Hu, & Lucassen, 2015 ; Akbarinia & Gil Rodríguez, 2020 ; Xu, Liu, Hou, Liu, & Qiu, 2020 ; Flachot et al, 2022 ; Heidari-Gorji & Gegenfurtner, 2023 ) and for illuminant estimation, even in challenging multi-illuminant scenarios ( Li, Wang, Brown, & Tan, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Color constancy mechanisms in virtual reality environments

Gil Rodríguez,

Hedjar,

Toscani

et al. 2024

Journal of Vision

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Prior research has demonstrated high levels of color constancy in real-world scenarios featuring single light sources, extensive fields of view, and prolonged adaptation periods. However, exploring the specific cues humans rely on becomes challenging, if not unfeasible, with actual objects and lighting conditions. To circumvent these obstacles, we employed virtual reality technology to craft immersive, realistic settings that can be manipulated in real time. We designed forest and office scenes illuminated by five colors. Participants selected a test object most resembling a previously shown achromatic reference. To study color constancy mechanisms, we modified scenes to neutralize three contributors: local surround (placing a uniform-colored leaf under test objects), maximum flux (keeping the brightest object constant), and spatial mean (maintaining a neutral average light reflectance), employing two methods for the latter: changing object reflectances or introducing new elements. We found that color constancy was high in conditions with all cues present, aligning with past research. However, removing individual cues led to varied impacts on constancy. Local surrounds significantly reduced performance, especially under green illumination, showing strong interaction between greenish light and rose-colored contexts. In contrast, the maximum flux mechanism barely affected performance, challenging assumptions used in white balancing algorithms. The spatial mean experiment showed disparate effects: Adding objects slightly impacted performance, while changing reflectances nearly eliminated constancy, suggesting human color constancy relies more on scene interpretation than pixel-based calculations.

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Section: Discussionmentioning

confidence: 99%

Color constancy mechanisms in virtual reality environments

Gil Rodríguez,

Hedjar,

Toscani

et al. 2024

Journal of Vision

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Multi-scale color constancy based on salient varying local spatial statistics

Ulucan,

Ebner

2023

Vis Comput

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The human visual system unconsciously determines the color of the objects by “discounting” the effects of the illumination, whereas machine vision systems have difficulty performing this task. Color constancy algorithms assist computer vision pipelines by removing the effects of the illuminant, which in the end enables these pipelines to perform better on high-level vision tasks based on the color features of the scene. Due to its benefits, numerous color constancy algorithms have been developed, and existing techniques have been improved. Combining different strategies and investigating new methods might help us design simple yet effective algorithms. Thereupon, we present a color constancy algorithm based on the outcomes of our previous works. Our algorithm is built upon the biological findings that the human visual system might be discounting the illuminant based on the highest luminance patches and space-average color. We find the illuminant estimate based on the idea that if the world is gray on average, the deviation of the brightest pixels from the achromatic value should be caused by the illuminant. Our approach utilizes multi-scale operations by only considering the salient pixels. It relies on varying surface orientations by adopting a block-based approach. We show that our strategy outperforms learning-free algorithms and provides competitive results compared to the learning-based methods. Moreover, we demonstrate that using parts of our strategy can significantly improve the performance of several learning-free methods. We also briefly present an approach to transform our global color constancy method into a multi-illuminant color constancy approach.

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A Scale-space Approach for Surface Normal Vector Estimation from Depth Maps

Ulucan,

Ebner

2024

SN COMPUT. SCI.

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Surface normal vectors provide cues about the local geometric features of the scene which are utilized in many computer vision and computer graphics applications. Thus, the estimation of surface normals by utilizing structured range sensor data is an important research field. Thereupon, we propose a learning-free algorithm to estimate the surface normal vectors from depth maps. Our simple yet effective method relies on computations carried out in scale-space. Our main idea is to estimate the surface normals which cannot be properly computed in the finest scale from the coarser scales. Our method can estimate the surface normals even for images included in datasets that have challenging characteristics such as noisy real-world data or significantly large planar regions that either have a small or no gradient change. We analyze our algorithm’s performance by utilizing five benchmarks, namely, the MIT-Berkeley Intrinsic Images dataset, the New Tsukuba Dataset, the SceneNet RGB-D dataset, the IID-NORD dataset, and the NYU Depth Dataset V2, and by using two different evaluation strategies. According to the experimental results, our method can estimate surface normals efficiently without requiring neither complex computations nor huge amounts of data.

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Color Constancy Beyond Standard Illuminants

Cited by 7 publications

References 28 publications

Color constancy mechanisms in virtual reality environments

Color constancy mechanisms in virtual reality environments

Multi-scale color constancy based on salient varying local spatial statistics

A Scale-space Approach for Surface Normal Vector Estimation from Depth Maps

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