3D color homography model for photo-realistic color transfer re-coding

Gong, Han; Finlayson, Graham D.; Fisher, Robert B.; Fang, Fufu

doi:10.1007/s00371-017-1462-x

Cited by 19 publications

(18 citation statements)

References 28 publications

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“…where H s was a 3 × 3 matrix that transforms colors from the source to match the ones of the reference, and γ r , γ s are inverse gamma correction values. In this work, we extend the matrix H s as a projective transformation with size 4 × 4 (inspired by color homography [19], [20]). By doing this, the model can deal not only with pixels in the core of the color gamut, but also with those values that appear on the border, which are the most affected by gamut mapping and tone mapping.…”

Section: B Color Stabilizationmentioning

confidence: 99%

“…Hwang et al [18] suggested to use moving least squares for color transfer, by also incorporating a probabilistic measure to ensure robustness against noise and outliers. Gong et al [19], [20] proposed a color transfer method based on a projective transformation and a mean intensity mapping. All the above mentioned methods are global, although some local approaches also exist.…”

Section: Introductionmentioning

confidence: 99%

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Color Matching Images With Unknown Non-Linear Encodings

Rodríguez

Vázquez-Corral

Bertalmío

2020

IEEE Trans. on Image Process.

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We present a color matching method that deals with different non-linear encodings. In particular, given two different views of the same scene taken by two cameras with unknown settings and internal parameters, and encoded with unknown non-linear curves, our method is able to correct the colors of one of the images making it look as if it was captured under the other camera's settings. Our method is based on treating the in-camera color processing pipeline as a concatenation of a matrix multiplication on the linear image followed by a non-linearity. This allows us to model a color stabilization transformation among the two shots by estimating a single matrix -that will contain information from both of the original images-and an extra parameter that complies with the non-linearity. The method is fast and the results have no spurious colors. It outperforms the state-of-the-art both visually and according to several metrics, and can handle HDR encodings and very challenging real-life examples.

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Section: B Color Stabilizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Color Matching Images With Unknown Non-Linear Encodings

Rodríguez

Vázquez-Corral

Bertalmío

2020

IEEE Trans. on Image Process.

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“…В ней трехмерное проективное преобразование использовалось для сопоставления цветовых тел различных проекционных аппаратов [41]. Позже этот же подход был применен для фотореалистичного переноса цветовой палитры между изображениями [42]. В обоих случаях речь идет о взаимной калибровке двух изображений, а не о переходе в некоторое эталонное пространство.…”

Section: проективное преобразование трехмерных цветовых координатunclassified

ProLab: психофизически равномерная проективная система цветовых координат

Konovalenko¹,

Smagina²,

Nikolaev³

et al. 2020

Preprint

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В работе предлагается ввести новое пространство цветовых координат proLab, связанное с CIE XYZ трехмерным проективным преобразованием. В статье показывается, что по психофизической равномерности, оцениваемой с помощью метрики STRESS по отношению к формуле цветовых различий CIEDE2000, предлагаемое пространство значительно опережает широко используемую систему координат CIELAB, хотя и уступает современной CAM16-UCS. Угловые метрики ошибок определения цветности, обычно используемые в линейных цветовых пространствах, могут использоваться и в proLab, поскольку проективное преобразование сохраняет линейность многообразий. При этом, в отличие от линейных пространств, угловые ошибки, различные по цветовому тону, в proLab нормированы в соответствии с порогами цветоразличения человека. В работе также показывается, что гетероскедастичность дробового шума в proLab оказывается меньшей, чем в CAM16-UCS и стандартных цветовых пространствах. Это делает proLab удобной координатной системой для линейного цветового анализа — решения задач линейной регрессии в цветовом пространстве.

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“…In other words, it is possible to apply CS in projectively transformed CSp. Homography of 3-dimensional CSp proposed for the first time presumably in [33], but applied to another problem -photo-realistic colour transfer.…”

Section: Colour Space Projective Transformmentioning

confidence: 99%

Linear colour segmentation revisited

Smagina

Bozhkova

Gladilin

et al. 2019

Eleventh International Conference on Machine Vision (ICMV 2018)

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In this work we discuss the known algorithms for linear colour segmentation based on a physical approach and propose a new modification of segmentation algorithm. This algorithm is based on a region adjacency graph framework without a pre-segmentation stage. Proposed edge weight functions are defined from linear image model with normal noise. The colour space projective transform is introduced as a novel pre-processing technique for better handling of shadow and highlight areas. The resulting algorithm is tested on a benchmark dataset consisting of the images of 19 natural scenes selected from the Barnard's DXC-930 SFU dataset and 12 natural scene images newly published for common use. The dataset is provided with pixel-by-pixel ground truth colour segmentation for every image. Using this dataset, we show that the proposed algorithm modifications lead to qualitative advantages over other model-based segmentation algorithms, and also show the positive effect of each proposed modification. The source code and datasets for this work are available for free access at http://github.com/visillect/segmentation.

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3D color homography model for photo-realistic color transfer re-coding

Cited by 19 publications

References 28 publications

Color Matching Images With Unknown Non-Linear Encodings

Color Matching Images With Unknown Non-Linear Encodings

ProLab: психофизически равномерная проективная система цветовых координат

Linear colour segmentation revisited

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