Automatic color constancy algorithm selection and combination

Bianco, Simone; Ciocca, Gianluigi; Cusano, Claudio; Schettini, Raimondo

doi:10.1016/j.patcog.2009.08.007

Cited by 99 publications

(82 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, since it has previously been shown 5 that within a set of AWB algorithms, the best and the worst ones do not exist, but they change on the basis of the image characteristic, we consider a set of single AWB algorithms, 17 and two classification-based modules, 10,8 able to identify the best AWB algorithm to use for each image exploiting automatically extracted information about the image class or image content in terms of low-level features.…”

Section: Proposed Approachmentioning

confidence: 99%

“…Numerous methods exist in the literature, and excellent reviews of them can be found in the works of Hordley, 4 Bianco et al, 6 and Gijsenij et al 7 A recent research area, which has shown promising results, aims to improve illuminant estimation by using visual information automatically extracted from the images. The existing algorithms exploit both low-level, 8,9 intermediatelevel, 10 and high-level 11,12 visual information. The second stage of the color correction pipeline transforms the image data into a standard color space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Color correction pipeline optimization for digital cameras

Bianco¹,

Bruna²,

Naccari³

et al. 2013

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. The processing pipeline of a digital camera converts the RAW image acquired by the sensor to a representation of the original scene that should be as faithful as possible. There are mainly two modules responsible for the color-rendering accuracy of a digital camera: the former is the illuminant estimation and correction module, and the latter is the color matrix transformation aimed to adapt the color response of the sensor to a standard color space. These two modules together form what may be called the color correction pipeline. We design and test new color correction pipelines that exploit different illuminant estimation and correction algorithms that are tuned and automatically selected on the basis of the image content. Since the illuminant estimation is an ill-posed problem, illuminant correction is not error-free. An adaptive color matrix transformation module is optimized, taking into account the behavior of the first module in order to alleviate the amplification of color errors. The proposed pipelines are tested on a publicly available dataset of RAW images. Experimental results show that exploiting the cross-talks between the modules of the pipeline can lead to a higher color-rendition accuracy. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Section: Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Color correction pipeline optimization for digital cameras

Bianco¹,

Bruna²,

Naccari³

et al. 2013

J. Electron. Imaging

View full text Add to dashboard Cite

show abstract

“…The second category of color constancy method consists of machine learning based methods [7～9] , gamut-based methods and probabilistic methods [10] . Meanwhile, the most recent combinational-based color constancy methods include the natural image statistics (NIS) [11] algorithm and classification based algorithm selection (CAS) algorithm [12] . NIS uses the Weibull parameter to predict the best algorithm for the given image, whereas CAS uses a decision tree to predict the best algorithm.…”

Section: ⅰ Introductionmentioning

confidence: 99%

Illuminant Chromaticity Estimation via Optimization of RGB Channel Standard Deviation

Subhashdas¹,

Yoo²,

Ha³

2016

Journal of the Institute of Electronics and Information Enginee

View full text Add to dashboard Cite

The primary aim of the color constancy algorithm is to estimate illuminant chromaticity. There are various statistical-based, learning-based and combinational-based color constancy algorithms already exist. However, the statistical-based algorithms can only perform well on images that satisfy certain assumptions, learning-based methods are complex methods that require proper preprocessing and training data, and combinational-based methods depend on either pre-determined or dynamically varying weights, which are difficult to determine and prone to error. Therefore, this paper presents a new optimization based illuminant estimation method which is free from complex preprocessing and can estimate the illuminant under different environmental conditions. A strong color cast always has an odd standard deviation value in one of the RGB channels. Based on this observation, a cost function called the degree of illuminant tinge(DIT) is proposed to determine the quality of illuminant color-calibrated images. This DIT is formulated in such a way that the image scene under standard illuminant (d65) has lower DIT value compared to the same scene under different illuminant. Here, a swarm intelligence based particle swarm optimizer(PSO) is used to find the optimum illuminant of the given image that minimizes the degree of illuminant tinge. The proposed method is evaluated using real-world datasets and the experimental results validate the effectiveness of the proposed method. Keywords Ⅰ. IntroductionIn the real world, the perceived color of an object relies on the surface reflectance and light source color that illuminate the scene. However, the human eye can perceive an object color to be same, even when there is a change in the illuminant color. This ability of the human visual system to discount the influence of illuminant color is referred to as color constancy. In a computational approach, an implied assumption is that the influence of light source colorJournal of The Institute of Electronics and Information Engineers Vol.53, NO.6, June 2016 is static throughout a scene. Therefore, the scene illuminant is estimated first and the scene colors are then corrected using a chromatic adaptation model.The main objective of a color constancy algorithm is to estimate the scene illuminant. However, illuminant estimation is very difficult process and various approximations have been made on physical world scenes to cope up with that. State-of-the-art color constancy approaches can be categorized into three groups: 1) statistical-based methods, 2) learningbased methods, and 3) combinational-based methods [1～2] . The first type of algorithm is based on low level statistics or a physics based reflection model, the second type algorithm makes use of information extracted from the training phase to estimate the light source color, and the third type is formulated by combining statistical-based algorithms or choosing a superlative algorithm for a given image.The Max-RGB or white patch retinex algorithm is a well known statis...

show abstract

“…Incosistencies may occur in lighting condition, focus, and angle of view [4]. A color constancy technique is used to approximate the original color of the captured image [5], [6]. It enables consistent color acquisition.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Method for Dipstick Urinalysis Using Smartphone Camera

Ginardi

Saikhu

Sarno

et al. 2014

Information and Communication Technology

View full text Add to dashboard Cite

Abstract. This paper introduces an intelligent method for helping people to maintain their healthy by doing a self urinalysis utilising a smartphone camera. A color sensing method using a smartphone camera is designed to determine the value of a reagent strip in a urinalysis dipstick. In the dipstick urinalysis, a color change in each reagent strip is examined. This color change is a result of the reaction of dipstick to the chemical contents of urine including pH, Protein, Glucose, Ketones, Leucocyte, Nitrite, Bilirubine, and Urobilinogen. Performing disptick urinalysis can be done in almost any places even on the very remote area where medical laboratory cannot be found, and it is much easier and cheaper than medical lab visit.The proposed intelligent method includes a framework for color acquisition using a smartphone camera which covers the color management system, color correction, color matching, and quantification. The usage of RGB and CIELAB color space is discussed in the color management part. An automated color constancy approach is introduced to provide a better color correction, and a step-wise linear interpolation is introduced to better estimate the urinalysis value.To implement this proposed method, a closed acquisition box does not required. Disptick capturing can be done directly with a smartphone camera in almost any normal lighting condition.

show abstract

Automatic color constancy algorithm selection and combination

Cited by 99 publications

References 24 publications

Color correction pipeline optimization for digital cameras

Color correction pipeline optimization for digital cameras

Illuminant Chromaticity Estimation via Optimization of RGB Channel Standard Deviation

Intelligent Method for Dipstick Urinalysis Using Smartphone Camera

Contact Info

Product

Resources

About