On the von Kries Model: Estimation, Dependence on Light and Device, and Applications

Lecca, Michela

doi:10.1007/978-94-007-7584-8_4

Cited by 9 publications

(3 citation statements)

References 47 publications

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“…In this case, the surface reflectance can be described by the Lambertian model, for which the reflectance at point x is proportional to the scalar product of the surface normal vector at x and the direction of the line connecting x to the light source. Lambertian surfaces are the basis of the von Kries model, 13 , 14 which approximates the image colour variations due to a light change by a linear, diagonal transform of the RGB image coordinates 15 . The von Kries model (and thus Lambertian surfaces) is employed by many colorisation techniques.…”

Section: Common Assumptions In MCC and Their Limitationsmentioning

confidence: 99%

Machine colour constancy: a work in progress

Lecca

2020

Coloration Technology

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The term “colour constancy” denotes the human capability to recognise an object as the same entity even if its colours change due to illumination. Implementing such a human capability is highly desirable in computer vision to retrieve, detect, recognise and track objects regardless of the illumination. Many efforts have been made in this direction, leading to several “machine colour constancy” (MCC) algorithms, that is, routines inspired by human colour constancy (HCC) that are aiming to achieve an image representation independent of the light and thus invariant against light changes. Different to HCC, which is subject to illusions and only partially removes illuminant effects, MCC pursues the implementation of a perfect, total removal of light effects from images. Such an implementation represents a major challenge and is therefore a focus of ongoing research. In fact, the currently available MCC algorithms only work under constrained domains. Therefore, a priori knowledge about the image content is needed to choose the most appropriate MCC procedure and to properly set its parameters. Here, we present the main common assumptions underlying most of MCC algorithms. Our work shows that two main issues should be addressed in the future to ensure MCC is more efficacious and useable: first, the lack of a reliable MCC algorithm operating without supervision in multiple scenarios; and second, the difficulty which any user encounters when choosing the MCC procedure most appropriate for a particular application.

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Section: Common Assumptions In MCC and Their Limitationsmentioning

confidence: 99%

Machine colour constancy: a work in progress

Lecca

2020

Coloration Technology

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“…The chromaticity of an illuminant measures the color of the illuminant regardless of the brightness and it is specified as a 2D vector with independent coordinates. The estimate of this vector allows correcting the colors of an image as this would be acquired under a canonical known light [4].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, a same scene captured under different lights results in two different color images. The dependence of colors on the light adversely affects the performances of color-based vision system [4]. Computing the chromaticity of the light and discarding it from the image is a popular way to compensate for the image color distortion due to the light [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Estimating illuminant chromaticity with a low-power color pixel

Olumodeji

Lecca

Giordani

et al. 2015

2015 XVIII AISEM Annual Conference

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The estimation of the chromaticity of an ambient light is relevant to many color-based vision applications, like object/people recognition, video-surveillance and assisted living. In this paper, we propose to estimate the chromaticity of an environmental light by means of a smart low-power color sensor. The sensor is composed by a RGB pixel connected to a microcontroller, embedding signal processing on board. The RGB pixel acquires the color signals in a high dynamic range with low power consumption. The microcontroller selects the signals coming from achromatic surfaces, whose chromaticity is equal to the light chromaticity, the chromaticity of the selected signals are sent to a processor, that returns the most frequent chromaticity as the most probable light chromaticity.

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Enhancing Backlight and Spotlight Images by the Retinex-Inspired Bilateral Filter SuPeR-B

Lecca

2023

Communications in Computer and Information Science

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On the von Kries Model: Estimation, Dependence on Light and Device, and Applications

Cited by 9 publications

References 47 publications

Machine colour constancy: a work in progress

Machine colour constancy: a work in progress

Estimating illuminant chromaticity with a low-power color pixel

Enhancing Backlight and Spotlight Images by the Retinex-Inspired Bilateral Filter SuPeR-B

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