You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel -- MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.

Cited Abstract(s):
[1]: The aim of computational color constancy is to estimate the actual surface color in an acquired scene disregarding its illuminant. Many solutions try to first estimate the illuminant and then correct the image with the illuminant estimate. Based on the linear image formation model, we propose in this work a new strategy to estimate the illuminant. Inspired by the feedback modulation from horizontal cells to the cones in the retina, we first normalize each local patch with its local maximum to obtain the so-called locally normalized reflectance estimate (LNRE). Then, we experimentally found that the ratio of the global summation of true surface reflectance to the global summation of LNRE in a scene is approximately achromatic for both indoor and outdoor scenes. Based on this substantial observation, we estimate the illuminant by computing the ratio of the global summation of the intensities to the global summation of the locally normalized intensities of the color-biased image. The proposed model has only one free parameter and requires no explicit training with learning-based approach. Experimental results on four commonly used datasets show that our model can produce competitive or even better results compared to the state-of-the-art approaches with low computational cost.

[2]: If color appearance is to be a useful feature in identifying an object, then color appearance must remain roughly constant when the object is viewed in different contexts. People maintain approximate color constancy despite variation in the color of nearby objects and despite variation in the spectral power distribution of the ambient light. Land's retinex algorithm is a model of human color constancy. We analyze the retinex algorithm and discuss its general properties. We show that the algorithm is too sensitive to changes in the color of nearby objects to serve as an adequate model of human color constancy.

[3]: 

[4]: A comprehensive mathematical model to account for colour constancy is formulated. Since the visual system is able to measure true object colour in complex scenes under a broad range of spectral compositions, for the illumination; it is assumed that the visual system must implicitly estimate and illuminant. The basic hypothesis is that the estimate of the illuminant is made on the basis of spatial information from the entire visual field. This estimate is then used by the visual system to arrive at an estimate of the (object) reflectance of the various subfields in the complex visual scene. The estimates are made by matching the inputs to the system to linear combinations of fixed bases and standards in the colour space. The model provides a general unified mathematical framework for related psychophysical phenomenology.

[5]: Color constancy is a well-studied topic in color vision. Methods are generally categorized as (1) low-level statistical methods, (2) gamut-based methods, and (3) learning-based methods. In this work, we distinguish methods depending on whether they work directly from color values (i.e., color domain) or from values obtained from the image’s spatial information (e.g., image gradients frequencies). We show that spatial information does not provide any additional information that cannot be obtained directly from the color distribution and that the indirect aim of spatial-domain methods is to obtain large color differences for estimating the illumination direction. This finding allows us to develop a simple and efficient illumination estimation method that chooses bright and dark pixels using a projection distance in the color distribution and then applies principal component analysis to estimate the illumination direction. Our method gives state-of-the-art results on existing public color constancy datasets as well as on our newly collected dataset (NUS dataset) containing 1736 images from eight different high-end consumer cameras.

[6]: We introduce a context for testing computational color constancy, specify our approach to the implementation of a number of the leading algorithms, and report the results of three experiments using synthesized data. Experiments using synthesized data are important because the ground truth is known, possible confounds due to camera characterization and pre-processing are absent, and various factors affecting color constancy can be efficiently investigated because they can be manipulated individually and precisely. The algorithms chosen for close study include two gray world methods, a limiting case of a version of the Retinex method, a number of variants of Forsyth's (1990) gamut-mapping method, 's (2000) neural net method, and 's color by correlation method ( 1997, 2001; Hubel and Finlayson 2000) . We investigate the ability of these algorithms to make estimates of three different color constancy quantities: the chromaticity of the scene illuminant, the overall magnitude of that illuminant, and a corrected, illumination invariant, image. We consider algorithm performance as a function of the number of surfaces in scenes generated from reflectance spectra, the relative effect on the algorithms of added specularities, and the effect of subsequent clipping of the data. All data is available on-line at http: www.cs.sfu.ca spl sim color data, and implementations for most of the algorithms are also available (http: www.cs.sfu.ca spl sim color code).

[7]: Illuminant estimation is a key step for computational color constancy. Instead of using the grey world or grey edge assumptions, we propose in this paper a novel method for illuminant estimation by using the information of grey pixels detected in a given color-biased image. The underlying hypothesis is that most of the natural images include some detectable pixels that are at least approximately grey, which can be reliably utilized for illuminant estimation. We first validate our assumption through comprehensive statistical evaluation on diverse collection of datasets and then put forward a novel grey pixel detection method based on the illuminant-invariant measure (IIM) in three logarithmic color channels. Then the light source color of a scene can be easily estimated from the detected grey pixels. Experimental results on four benchmark datasets (three recorded under single illuminant and one under multiple illuminants) show that the proposed method outperforms most of the state-of-the-art color constancy approaches with the inherent merit of low computational cost.

[8]: Colour constancy is a central problem for any visual system performing a task which requires stable perception of the colour world. To solve the colour constancy problem we estimate the colour of the prevailing light and then, at the second stage, remove it. Two of the most commonly used simple techniques for estimating the colour of the light are the Grey-World and Max-RGB algorithms. In this paper we begin by observing that this two colour constancy computations will respectively return the right answer if the average scene colour is grey or the maximum is white (and conversely, the degree of failure is proportional to the extent that these assumptions hold). We go on to ask the following question: “ Would we perform better colour constancy by assuming the scene average is some shade of grey?”. We give a mathematical answer to this question. Firstly, we show that Max-RGB and Grey-World are two instantia-tions of Minkowski norm. Secondly, that for a large cali-brated dataset L6 norm colour constancy works best over-all (we have improved the performance achieved by a sim-ple normalization based approach). Surprisingly we found performance to be similar to more elaborated algorithm.

[9]:
estimate illumination by making some assumptions about the local or global regularity of the illumination and reflectance of the input image. The simplest such method is [4] , that assumes that the global average of reflectance is achromatic. The generalization of this assumption by restricting it to local patches and higher-order gradients has led to some classical and recent statistics-based methods, such as White Patch [2] , General Gray World [6] , Gray Edge [3] , Shades-of-Gray [8] and LSRS [1] , among others [5] . The closest works to ours are Xiong [9] and Gray Pixel [7] . Xiong [9] finds gray surfaces based on a special LIS space, but this method is camera-dependent. The Gray Pixel method will be discussed in .