A Data Set for Camera-Independent Color Constancy

Aytekin, Çağlar; Nikkanen, Jarno; Gabbouj, Moncef

doi:10.1109/tip.2017.2764264

Cited by 22 publications

(21 citation statements)

References 31 publications

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“…Nevertheless, since all well-known learning-based methods are supervised, a major obstacle for their application is that for a given sensor, despite proposed workarounds [43], supervised learning-based methods have to be trained on calibrated images taken by preferably the same sensor [44]. To calibrate arXiv:1712.00436v4 [cs.CV] 19 Mar 2019 the images, a calibration object has to be placed in the scenes of these images and later segmented to extract the groundtruth illumination.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning for Color Constancy

Banić

Lončarić

2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based color constancy methods, but they require a significant amount of calibrated training images with known ground-truth illumination. Such calibration is time consuming, preferably done for each sensor individually, and therefore a major bottleneck in acquiring high color constancy accuracy. Statistics-based methods do not require calibrated training images, but they are less accurate. In this paper an unsupervised learning-based method is proposed that learns its parameter values after approximating the unknown ground-truth illumination of the training images, thus avoiding calibration. In terms of accuracy the proposed method outperforms all statisticsbased and many learning-based methods. An extension of the method is also proposed, which learns the needed parameters from non-calibrated images taken with one sensor and which can then be successfully applied to images taken with another sensor. This effectively enables inter-camera unsupervised learning for color constancy. Additionally, a new high quality color constancy benchmark dataset with 1707 calibrated images is created, used for testing, and made publicly available. The results are presented and discussed. The source code and the dataset are available at

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Section: Introductionmentioning

confidence: 99%

Unsupervised Learning for Color Constancy

Banić

Lončarić

2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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“…Intel-TUT was proposed in [31]. It contained a subset of 1558 images of the proposed INTEL-TAU dataset.…”

Section: Previously Published Color Constancy Datasetsmentioning

confidence: 99%

“…Thus, the dataset is now fully GDPR compliant. A subset of 1558 images of the current dataset was previously published as Intel-TUT dataset [31], but had to be retracted due to its GDPR non-compliance. Images in INTEL-TAU dataset were collected using three different cameras: Canon 5DSR, Nikon D810, and Mobile Sony IMX135.…”

Section: Introductionmentioning

confidence: 99%

INTEL-TAU: A Color Constancy Dataset

Laakom

Raitoharju

Nikkanen³

et al. 2021

IEEE Access

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In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5DSR, Nikon D810, and Sony IMX135, makes the dataset appropriate for evaluating the camera and scene invariance of the different illumination estimation techniques. Privacy masking is done for sensitive information, e.g., faces. Thus, the dataset is coherent with the new General Data Protection Regulation (GDPR). Furthermore, the effect of color shading for mobile images can be evaluated with INTEL-TAU dataset, as both corrected and uncorrected versions of the raw data are provided. Furthermore, this paper benchmarks several color constancy approaches on the proposed dataset.

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“…vignette [19]), image sensor (e.g. color shading [20]), and dust on the camera, color of a test image was illumination corrected by a background image using the Spectral Nonuniform Illumination Correction (SNIC) algorithm proposed in [18]:…”

Section: Illumination Correctionmentioning

confidence: 99%

Smartphone Modulated Colorimetric Reader with Color Subtraction

Zhao¹,

Choi²,

Lou-Franco³

et al. 2019

2019 Ieee Sensors

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Color analysis has been essential for the interpretation of optical readouts, e.g. colorimetry, fluorescence, spectroscopy, and scanometry. However, existing colorimetric readers can hardly eliminate the color interference of colored solutions, e.g., interpreting pH test strips to assess the pH value of red wine. This paper introduces a smartphone modulated colorimetric reader that is compatible with most smartphone models and a novel color subtraction algorithm that eliminates color interferences due to colored solutions. Experiments were conducted to validate the effectiveness of the developed reader and algorithm on evaluating pH test strips produced from transparent and colored solutions using multiple smartphone models. Applicability of the developed reader was demonstrated through its interpretation of pH test strips measuring pH values of colored and non-transparent food samples including red wine and milk.

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A Data Set for Camera-Independent Color Constancy

Cited by 22 publications

References 31 publications

Unsupervised Learning for Color Constancy

Unsupervised Learning for Color Constancy

INTEL-TAU: A Color Constancy Dataset

Smartphone Modulated Colorimetric Reader with Color Subtraction

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