Color Appearance Models

Fernández-Maloigne, Christine; Trémeau, Alain; Robert-Inacio, Frédérique; Macaire, Ludovic

doi:10.1002/9781118562680.ch3

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…A particular color stimulus in the visible wavelength region is represented by tristimulus values XYZ in the CIE system. The XYZ values were experimentally determined by using human observers . This experiment resulted in the definition of color matching functions (CMFs) and the standards that are used by industry.…”

Section: Optical Properties Of Band Gap Tunable Mixed Halide Perovskitesmentioning

confidence: 99%

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Perovskite Color Detectors: Approaching the Efficiency Limit

Hossain

Khan

Kozawa

et al. 2020

ACS Appl. Mater. Interfaces

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Color image sensing by a smartphone or digital camera employs sensor elements with an array of color filters for capturing basic blue, green, and red color information. However, the normalized optical efficiency of such color filter-based sensor elements is limited to only one-third. Optical detectors based on perovskites are described, which can overcome this limitation. An efficient color sensor design has been proposed in this study that uses a vertically stacked arrangement of perovskite diodes. As compared to the conventional color filter-based sensors, the proposed sensor structure can potentially reach normalized optical efficiency approaching 100%. In addition, the proposed sensor design does not exhibit color aliasing or color Moiré effects, which is one of the main limitations for the filter-based sensors. Furthermore, up to our knowledge, for the first time, it could be theoretically shown that both vertically arranged sensor and conventional color filter-based sensor provide almost comparable color errors. The optical properties of the perovskite materials are determined by optical measurements in combination with an energy shift model. The optics of the stacked perovskite sensors is investigated by threedimensional finite-difference timedomain simulations. Finally, colorimetric characterization was carried out to determine the color error of the sensors.

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Section: Optical Properties Of Band Gap Tunable Mixed Halide Perovskitesmentioning

confidence: 99%

“…The XYZ values were experimentally determined by using human observers. 17 This experiment resulted in the definition of color matching functions (CMFs) and the standards that are used by industry. The CMFs are provided in Section 4.2.…”

Section: Optical Properties Of Band Gap Tunablementioning

confidence: 99%

Perovskite Color Detectors: Approaching the Efficiency Limit

Hossain

Khan

Kozawa

et al. 2020

ACS Appl. Mater. Interfaces

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“…To identify the different colours in the images, we have used colour segmentation by region growing [4]. For the initialisation of the seeds, we performed an analysis of greyscale histogram.…”

Section: Coloursmentioning

confidence: 99%

“…We just work with the scale 4 composed of 91 orientations. Each orientation contains 2 4 * 2 4 (256) coefficients. We also performed OpponentSIFT proposed by Van de Sande and Snoek [18].…”

Section: Texturesmentioning

confidence: 99%

Extraction of emotional impact in colour images

Gbèhounou¹,

Lecellier²,

Fernández-Maloigne³

2012

cgiv

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This paper proposes a method to extract the emotional impact of images. Emotions are often associated with facial expressions, but we decided consider other features as first emotional characteristic of natural images, which, in general, does not contain faces. For a seek of generally we have built a new image database composed of a large variety of low semantic images. We used colour images because often colours and emotions are supposed to be linked. For the modelling of the emotions, we considered colours features completed with other recent and efficient descriptors. We supposed that different features used could also implicitly encode high-level information about emotions. The concept of emotion is not easy to model. The perception of emotion is not only influenced by the content and the colour of the images. It is also modified by some personal experiences like cultural aspects and personal semantic associated to some colours or objects. The complexity of emotion modelling was considered in classification process through psycho-visual tests. The twenty-five observers assessed the nature and the power of emotions they felt. These tests allowed us to distinguish three classes of emotions, which are "Negative", "Neutral" and "Positive" emotions. We used a Support Vector Machine for classification and the average success rate is 51,75%; that is really relevant regarding the equivalent results in the literature.

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