760 research outputs found
Color Constancy Using CNNs
In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling, one fully connected layer and three output nodes. Within the network
structure, feature learning and regression are integrated into one optimization
process, which leads to a more effective model for estimating scene
illumination. This approach achieves state-of-the-art performance on a standard
dataset of RAW images. Preliminary experiments on images with spatially varying
illumination demonstrate the stability of the local illuminant estimation
ability of our CNN.Comment: Accepted at DeepVision: Deep Learning in Computer Vision 2015 (CVPR
2015 workshop
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
Extending minkowski norm illuminant estimation
The ability to obtain colour images invariant to changes of illumination is called colour
constancy. An algorithm for colour constancy takes sensor responses - digital images
- as input, estimates the ambient light and returns a corrected image in which the illuminant
influence over the colours has been removed. In this thesis we investigate the
step of illuminant estimation for colour constancy and aim to extend the state of the art
in this field.
We first revisit the Minkowski Family Norm framework for illuminant estimation.
Because, of all the simple statistical approaches, it is the most general formulation and,
crucially, delivers the best results. This thesis makes four technical contributions. First,
we reformulate the Minkowski approach to provide better estimation when a constraint
on illumination is employed. Second, we show how the method can (by orders of
magnitude) be implemented to run much faster than previous algorithms. Third, we
show how a simple edge based variant delivers improved estimation compared with the
state of the art across many datasets. In contradistinction to the prior state of the art our
definition of edges is fixed (a simple combination of first and second derivatives) i.e.
we do not tune our algorithm to particular image datasets. This performance is further
improved by incorporating a gamut constraint on surface colour -our 4th contribution.
The thesis finishes by considering our approach in the context of a recent OSA
competition run to benchmark computational algorithms operating on physiologically
relevant cone based input data. Here we find that Constrained Minkowski Norms operi
ii
ating on spectrally sharpened cone sensors (linear combinations of the cones that behave
more like camera sensors) supports competition leading illuminant estimation
Color Constancy Adjustment using Sub-blocks of the Image
Extreme presence of the source light in digital images decreases the performance of many image processing algorithms, such as video analytics, object tracking and image segmentation. This paper presents a color constancy adjustment technique, which lessens the impact of large unvarying color areas of the image on the performance of the existing statistical based color correction algorithms. The proposed algorithm splits the input image into several non-overlapping blocks. It uses the Average Absolute Difference (AAD) value of each block’s color component as a measure to determine if the block has adequate color information to contribute to the color adjustment of the whole image. It is shown through experiments that by excluding the unvarying color areas of the image, the performances of the existing statistical-based color constancy methods are significantly improved. The experimental results of four benchmark image datasets validate that the proposed framework using Gray World, Max-RGB and Shades of Gray statistics-based methods’ images have significantly higher subjective and competitive objective color constancy than those of the existing and the state-of-the-art methods’ images
Estimating varying illuminant colours in images
Colour Constancy is the ability to perceive colours independently of varying illumi-nation colour. A human could tell that a white t-shirt was indeed white, even under
the presence of blue or red illumination. These illuminant colours would actually make the reflectance colour of the t-shirt bluish or reddish. Humans can, to a good extent, see colours constantly. Getting a computer to achieve the same goal, with a high level of accuracy has proven problematic. Particularly if we wanted to use colour as a main cue in object recognition. If we trained a system on object colours under one illuminant and then tried to recognise the objects under another illuminant, the system would likely fail. Early colour constancy algorithms assumed that an image contains a single uniform illuminant. They would then attempt to estimate the colour
of the illuminant to apply a single correction to the entire image.
It’s not hard to imagine a scenario where a scene is lit by more than one illuminant. If we take the case of an outdoors scene on a typical summers day, we would see
objects brightly lit by sunlight and others that are in shadow. The ambient light in shadows is known to be a different colour to that of direct sunlight (bluish and
yellowish respectively). This means that there are at least two illuminant colours to be recovered in this scene. This thesis focuses on the harder case of recovering the
illuminant colours when more than one are present in a scene.
Early work on this subject made the empirical observation that illuminant colours are actually very predictable compared to surface colours. Real-world illuminants
tend not to be greens or purples, but rather blues, yellows and reds. We can think of an illuminant mapping as the function which takes a scene from some unknown
illuminant to a known illuminant. We model this mapping as a simple multiplication of the Red, Green and Blue channels of a pixel. It turns out that the set of realistic
mappings approximately lies on a line segment in chromaticity space. We propose an algorithm that uses this knowledge and only requires two pixels of the same surface
under two illuminants as input. We can then recover an estimate for the surface reflectance colour, and subsequently the two illuminants.
Additionally in this thesis, we propose a more robust algorithm that can use vary-ing surface reflectance data in a scene. One of the most successful colour constancy
algorithms, known Gamut Mappping, was developed by Forsyth (1990). He argued that the illuminant colour of a scene naturally constrains the surfaces colours that are possible to perceive. We couldn’t perceive a very chromatic red under a deep blue illuminant. We introduce our multiple illuminant constraint in a Gamut Mapping
context and are able to further improve it’s performance.
The final piece of work proposes a method for detecting shadow-edges, so that we can automatically recover estimates for the illuminant colours in and out of shadow.
We also formulate our illuminant estimation algorithm in a voting scheme, that probabilistically chooses an illuminant estimate on both sides of the shadow edge.
We test the performance of all our algorithms experimentally on well known datasets, as well as our new proposed shadow datasets
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