6,530 research outputs found
A Large Image Database for Color Constancy Research
We present a study on various statistics relevant to research on color constancy. Many of these analyses could not have been done before simply because a large database for color constancy was not available. Our image database consists of approximately 11,000 images in which the RGB color of the ambient illuminant in each scene is measured. To build such a large database we used a novel set-up consisting of a digital video camera with a neutral gray sphere attached to the camera so that the sphere always appears in the field of view. Using a gray sphere instead of the standard gray card facilitates measurement of the variation in illumination as a function of incident angle. The study focuses on the analysis of the distribution of various illuminants in the natural scenes and the correlation between the rg-chromaticity of colors recorded by the camera and the rg-chromaticity of the ambient illuminant. We also investigate the possibility of improving the performance of the naïve Gray World algorithm by considering a sequence of consecutive frames instead of a single image. The set of images is publicly available and can also be used as a database for testing color constancy algorithms
Colour constancy using von Kries transformations: colour constancy "goes to the Lab"
Colour constancy algorithms aim at correcting colour towards a correct perception within
scenes. To achieve this goal they estimate a white point (the illuminant's colour), and correct
the scene for its in uence. In contrast, colour management performs on input images colour
transformations according to a pre-established input pro le (ICC pro le) for the given con-
stellation of input device (camera) and conditions (illumination situation). The latter case
presents a much more analytic approach (it is not based on an estimation), and is based on
solid colour science and current industry best practises, but it is rather in exible towards cases
with altered conditions or capturing devices. The idea as outlined in this paper is to take up
the idea of working on visually linearised and device independent CIE colour spaces as used
in colour management, and to try to apply them in the eld of colour constancy. For this
purpose two of the most well known colour constancy algorithms White Patch Retinex and
Grey World Assumption have been ported to also work on colours in the CIE LAB colour
space. Barnard's popular benchmarking set of imagery was corrected with the original imple-
mentations as a reference and the modi ed algorithms. The results appeared to be promising,
but they also revealed strengths and weaknesses
Color Constancy Convolutional Autoencoder
In this paper, we study the importance of pre-training for the generalization
capability in the color constancy problem. We propose two novel approaches
based on convolutional autoencoders: an unsupervised pre-training algorithm
using a fine-tuned encoder and a semi-supervised pre-training algorithm using a
novel composite-loss function. This enables us to solve the data scarcity
problem and achieve competitive, to the state-of-the-art, results while
requiring much fewer parameters on ColorChecker RECommended dataset. We further
study the over-fitting phenomenon on the recently introduced version of
INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both
field and non-field scenes acquired by three different camera models.Comment: 6 pages, 1 figure, 3 table
Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism
Pooling is a ubiquitous operation in image processing algorithms that allows
for higher-level processes to collect relevant low-level features from a region
of interest. Currently, max-pooling is one of the most commonly used operators
in the computational literature. However, it can lack robustness to outliers
due to the fact that it relies merely on the peak of a function. Pooling
mechanisms are also present in the primate visual cortex where neurons of
higher cortical areas pool signals from lower ones. The receptive fields of
these neurons have been shown to vary according to the contrast by aggregating
signals over a larger region in the presence of low contrast stimuli. We
hypothesise that this contrast-variant-pooling mechanism can address some of
the shortcomings of max-pooling. We modelled this contrast variation through a
histogram clipping in which the percentage of pooled signal is inversely
proportional to the local contrast of an image. We tested our hypothesis by
applying it to the phenomenon of colour constancy where a number of popular
algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and
Double-Opponency). For each of these methods, we investigated the consequences
of replacing their original max-pooling by the proposed
contrast-variant-pooling. Our experiments on three colour constancy benchmark
datasets suggest that previous results can significantly improve by adopting a
contrast-variant-pooling mechanism
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