Color balancing based upon gamut and temporal correlations

A color constancy mechanism is proposed to perceive the image color from the strengths of three RGB responses of a color camera or the like, independent of the color of the light illuminating the object. The first step of the method is to use a finite-dimensional linear model to estimate the color signals. It is shown that any color signal can be characterized as a linear combination of four principal component basis functions. Once the color signals have been estimated the unknown illuminant can be determined by S. Tominaga and B.A. Wandell's (1989) estimation method, which is based on a dichromatic model. In color constancy, the knowledge of surface reflectance helps in determining the canonical color descriptors despite the variations in the spectral power distribution of the ambient light. It is shown that the estimate of surface reflectance is derived from both the estimated color signal and illuminant straightforwardly. The model of a combination of the estimations of the surface spectral reflectance and the color signals can be employed in designing a color constancy electronic video camera which indeed improves the shortcomings of the traditional video camcorders.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Using convolutional neural network models illumination estimation according to light colors

Color constancy is of important for many computer vision applications, such as image classification, color object recognition, object tracking and so on. But unlike the human visual system, imaging device cannot be able to compute color constant descriptors which do not vary with the color of the illuminant, so solving color constancy problem is necessary. In the calculation of color constancy, illuminant estimation is the key. Because grey surfaces can perfectly reflect the color of the scene illumination, many methods have been proposed to identify grey surfaces to estimate the illuminant. But they either rely on the camera.s parameters, lacking universality, or work inaccurate in worse conditions. In order to solve these problems, in this paper, an iterative method is proposed. The quality of the proposed method is tested and compared to the previous color constancy methods on the Macbeth Chart and two data sets of synthetic and real images. Through MATLAB simulation, experimental pictures and quantitative data for performance evaluation were gotten. The simulated results show that the proposed algorithm is accurate and efficient in identification of the grey surfaces, even in worse condition. And it performs well in color constancy computation on both synthetic and real images.

Selection for gamut mapping colour constancy

Visual information, in the form of images and video, comes from the interaction of light with objects. Illumination is a fundamental element of visual information. Detecting and interpreting illumination effects is part of our everyday life visual experience. Shading for instance allows us to perceive the three-dimensional nature of objects. Shadows are particularly salient cues for inferring depth information. However, we do not make any conscious or unconscious effort to avoid them as if they were an obstacle when we walk around. Moreover, when humans are asked to describe a picture, they generally omit the presence of illumination effects, such as shadows, shading, and highlights, to give a list of objects and their relative position in the scene. Processing visual information in a way that is close to what the human visual system does, thus being aware of illumination effects, represents a challenging task for computer vision systems. Illumination phenomena interfere in fact with fundamental tasks in image analysis and interpretation applications, such as object extraction and description. On the other hand, illumination conditions are an important element to be considered when creating new and richer visual content that combines objects from different sources, both natural and synthetic. When taken into account, illumination effects can play an important role in achieving realism. Among illumination effects, shadows are often integral part of natural scenes and one of the elements contributing to naturalness of synthetic scenes. In this thesis, the problem of extracting shadows from digital images is discussed. A new analysis method for the segmentation of cast shadows in still and moving images without the need of human supervision is proposed. The problem of separating moving cast shadows from moving objects in image sequences is particularly relevant for an always wider range of applications, ranging from video analysis to video coding, and from video manipulation to interactive environments. Therefore, particular attention has been dedicated to the segmentation of shadows in video. The validity of the proposed approach is however also demonstrated through its application to the detection of cast shadows in still color images. Shadows are a difficult phenomenon to model. Their appearance changes with changes in the appearance of the surface they are cast upon. It is therefore important to exploit multiple constraints derived from the analysis of the spectral, geometric and temporal properties of shadows to develop effective techniques for their extraction. The proposed method combines an analysis of color information and of photometric invariant features to a spatio-temporal verification process. With regards to the use of color information for shadow analysis, a complete picture of the existing solutions is provided, which points out the fundamental assumptions, the adopted color models and the link with research problems such as computational color constancy and color invariance. The proposed spatial verification does not make any assumption about scene geometry nor about object shape. The temporal analysis is based on a novel shadow tracking technique. On the basis of the tracking results, a temporal reliability estimation of shadows is proposed which allows to discard shadows which do not present time coherence. The proposed approach is general and can be applied to a wide class of applications and input data. The proposed cast shadow segmentation method has been evaluated on a number of different video data representing indoor and outdoor real-world environments. The obtained results have confirmed the validity of the approach, in particular its ability to deal with different types of content and its robustness to different physically important independent variables, and have demonstrated the improvement with respect to the state of the art. Examples of application of the proposed shadow segmentation tool to the enhancement of video object segmentation, tracking and description operations, and to video composition, have demonstrated the advantages of a shadow-aware video processing.

The White-Patch method, one of the very first colour constancy methods, estimates the illuminant colour from the maximum response of three colour channels. However, this simple method has been superseded by advanced physical, statistical and learning based colour constancy methods. Recently, a few research works have suggested that the simple idea of using maximum pixel values is not as limited an idea as it seems on first glance. These works show that in several situations some manipulations can indeed made it perform very well. Here, we extend the White-Patch assumption to include any of: white patch, highlights or light source; let us refer to these pixels in an image as the &#x201C;bright&#x201D; pixels areas. We propose that bright pixels are surprisingly helpful in the illumination estimation process.In this paper, we investigate the effects of bright pixels on several current colour constancy algorithms. Moreover, we describe a simple framework for an illumination estimation method based on bright pixels and compare its accuracy to well-known colour constancy algorithms applied to four standard datasets. We also investigate failure and success cases, using bright pixels, and propose desiderata on input images with regard to the proposed method.

The Zeta-image, illuminant estimation, and specularity manipulation

Colour is derived from three physical properties: incident light, object reflectance and sensor sensitivities. Incident light varies under natural conditions; hence, recovering scene illuminant is an important issue in computational colour. One way to deal with this problem under calibrated conditions is by following three steps, 1) building a narrow-band sensor basis to accomplish the diagonal model, 2) building a feasible set of illuminants, and 3) defining criteria to select the best illuminant. In this work we focus on colour constancy for natural images by introducing perceptual criteria in the first and third stages.To deal with the illuminant selection step, we hypothesize that basic colour categories can be used as anchor categories to recover the best illuminant. These colour names are related to how the human visual system has evolved to encode relevant natural colour statistics. Therefore the recovered image provides the best representation of the scene labelled with the basic colour terms. We demonstrate with several experiments how this selection criterion achieves current state-of-art results in computational colour constancy. In addition to this result, we psychophysically prove that usual angular error used in colour constancy does not correlate with human preferences, and we propose a new perceptual colour constancy evaluation.The implementation of this selection criterion strongly relies on the use of a diagonal model for illuminant change. Then, the second contribution focuses on building an appropriate narrow-band sensor basis to represent natural images. We propose to use the spectral sharpening technique to compute a unique narrow-band basis optimized to represent a large set of natural reflectances under natural illuminants and given in the basis of human cones. The proposed sensors allow predicting unique hues and the World colour Survey data independently of the illuminant by using a compact singularity function. Additionally, we studied different families of sharp sensors to minimize different perceptual measures. This study brought us to extend the spherical sampling procedure from 3D to 6D.Several research lines remain still open, such as, measuring the effects of using the computed sharp sensors on the category hypothesis; or inserting spatial contextual information to improve category hypothesis. Finally, to explore how individual sensors can be adjusted to the colours in a scene.

The color appearance of a stimulus depends on the context in which it is viewed. Well-known examples of context effects include simultaneous color contrast and color constancy. In simultaneous contrast, the color immediately surrounding a test influences its appearance. In color constancy, the visual system adapts to the ambient illumination to keep surface color approximately constant. Color constancy is a context effect because the way that the visual system interprets the light reaching the eye depends on the context defined by the ambient illumination.To predict accurately the color appearance of stimuli rendered on a CRT monitor, we need a theory of color context effects that applies to such stimuli. Simultaneous contrast suggests that the color perceived at a particular monitor location may vary with the immediate context provided by the image at other locations on the monitor (the monitor context). Color constancy suggests that the color appearance of stimuli displayed on a monitor may vary with the ambient illumination of the room in which the monitor is viewed (the illumination context), at least if images from a monitor are processed by the visual system in the same way as reflective surfaces.In this paper, we report initial experiments designed to measure psychophysically how the monitor context and the illumination context influence the appearance of stimuli presented on CRT color monitors. Our emphasis, however, is on the effect of the illumination context.

A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison-there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the most likely explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.

A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison -- there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the *most likely* explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.

Color constancy is the ability to measure image features independent of the color of the scene illuminant and is an important topic in color and computer vision. As many color constancy algorithms exist, different distance measures are used to compute their accuracy. In general, these distances measures are based on mathematical principles such as the angular error and Euclidean distance. However, it is unknown to what extent these distance measures correlate to human vision.Therefore, in this paper, a taxonomy of different distance measures for color constancy algorithms is presented. The main goal is to analyze the correlation between the observed quality of the output images and the different distance measures for illuminant estimates. The output images are the resulting color corrected images using the illuminant estimates of the color constancy algorithms, and the quality of these images is determined by human observers. Distance measures are analyzed how they mimic differences in color naturalness of images as obtained by humans.Based on the theoretical and experimental results on spectral and real-world data sets, it can be concluded that the perceptual Euclidean distance (PED) with weight-coefficients (w R = 0.26, w G = 0.70, w B = 0.04) finds its roots in human vision and correlates significantly higher than all other distance measures including the angular error and Euclidean distance.KeywordsDistance MeasureColor SpaceHuman ObserverAngular ErrorHyperspectral DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. This has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset.

Robot-vision models of color constancy that are based on a linear approximation of illuminant and reflectance spectra are often generalized to the human visual system. Dannemiller's1 computational approach to color constancy is a good example. In this paper it is shown that such a procedure to estimate illuminant and reflectance spectra is less plausible for the human visual system than is implicitly assumed in robot-vision. The resemblance of the modeled hypothetical visual systems to the human visual system is misleading since it implies that surface reflectance is the illuminant-invariant object color descriptor the human visual system uses to achieve color constancy. However, an alternative type of descriptor is available that is not used to recover reflectance spectra. It has the advantage of allowing an interpretation that is preferable from the human point of view.

Human has the visual system called "color constancy" that maintains the perceptive colors of same object across various light sources. The effective method of color constancy algorithm was proposed to use the human facial color in a digital color image, however, this method has wrong estimation results by the difference of individual facial colors. In this paper, we present the novel color constancy algorithm based on skin color analysis. The skin color analysis is the method to separate the skin color into the components of melanin, hemoglobin and shading. We use the stationary property of Japanese facial color, and this property is calculated from the components of melanin and hemoglobin. As a result, we achieve to propose the method to use subject's facial color in image and not depend on the individual difference among Japanese facial color.