3.6 Learning Colour Constancy
Dierent researchers felt inspired by the robustness of biological systems in their ability to adapt their colour vision to ever changing conditions. This section will show a cross section of attempts to achieve colour constancy on digital systems by means of utilising methods of Articial Intelligence (AI).
3.6.1 Biologically Based Approaches
Inspired by the human visual system, many scientists have tried to mock biological phenom- ena in colour constancy to yield colour corrected image perception. Courtney et al. have based an approach on psycho-physical data from a model of the human visual cortex V4 [9]. Spacio-chromatic problems in colour perception were met successfully using a Neural Net- work simulation software aimed at a physiologically correct neural simulation. In fact, the results were in accordance with the V4 stage of the visual cortex model as referred to in the research. Some other researchers have followed the path of implementing V4 like behaviour in software.
Within the visual cortex of the brain, the primary visual cortex (V1), as well as the areas V2 and V4 process colour information. It is quite well understood today, that the area V4 contains cells that respond to the color of objects irrespective of the light that illuminates the object [1]. But at the same time, the comparison of the light intensity of the light from a patch with the intensity of the surrounding area is accomplished by some as yet unknown mechanism. Therefore, looking at the research of Courtney et al. purely on this basis is a mere academically interesting approach. However, it does not bear any real signicance besides outlining yet another possible way inspired by biological research. But this approach in itself provides an interesting view point to make use of the information available. One of the benets of the implementation is that it avoids a wash-out eect, as observed by many other colour constancy algorithms that work on non-uniformly lit scenes. This eect leads to a colour saturation wash-out towards the centre of more extensive, solidly coloured areas.
3.6.2 Neural Network Based Approaches
Neural networks are apart from being biologically inspired also frequently used to solve problems of computational colour constancy (see Sect.3.6.1). Their advantage is, that it is not necessary to analytically determine the illuminant's properties and compensate for them. The idea is to train the system on a multitude of known cases to deduce a system capable of performing colour constancy. Compensation using the neural network after training can be performed in a computationally cheap way, even though the initial training can be quite extensive and time consuming. Neural network architectures can be applied in very dierent ways. The most common distinctions for colour constancy applications are towards global illumination compensation and local pixel colour compensation.
Funt et al. for example have used a global image compensation [35]. The image scene colour space is discretised. If pixels of the scene are present within a discrete region of the colour space, the state of a neuron in the input layer of the network is set to 1, otherwise it is set to 0. The output layer of the network consists of only two neurons reecting the image's white point in chromaticity coordinates. These can be used directly to colour correct the image. The global neural network approach as used by Funt et al. compares quite well against a few sampled others of the classic colour constancy algorithms (see Sect. 3.3). And it even performs very well for certain dicult scenes with only very few coloured areas, in which several others fail.
In contrast, Bascle et al. use neural networks to process the pixels of a scene individ- ually [36]. The network is trained on data from individual pixels sampled randomly. The images providing these pixels for the training result from dierent commodity camera models (web cameras) under a set of only a few common illuminants (uorescent lights, incandescent light bulbs, natural sunlight). The trained network is laid out as a modied multi-layer per- ceptron. It learns to ignore the luminance of the input and estimates two colour invariants (resembling the chromaticity characteristic) that are independent to the illumination. The trained network is used to disregard illumination and shadows within the scene for a colour based segmentation. The quality of the indierent chromaticity estimation was as good or better than the classic colour constancy algorithms (see Sect. 3.3 again). As a look-up table could be compiled from the trained network result, the computation performance on the sample data was very fast as well. Bascle et al. went into great detail about comparing the implementation to a large variety of other algorithms with respect to their strengths, weaknesses and performances.
3.6.3 Probabilistic Learning
Rosenberg et al. [37] are also using chromaticity histograms, but using a dierent approach to Porikli's histogram colour correlation (see Sect.3.4). Due to the logarithmic chromaticity space used, the illuminant's chromaticity will shift a colour linearly. In this approach one needs to establish (from a training set) the probabilities of a given chromaticity to appear in an image under a given illuminant's chromaticity. The individual probabilities are ac- cumulated in a chromaticity histogram. The estimated illuminant is determined through a maximum likelihood approach through histogram similarity computations. Dierent meth- ods for performing histogram similarity metrics are also compared.
Also this example of a statistical learning approach performs quite well in quality against common classic colour constancy approaches like the Grey World Assumption or the White Patch Retinex algorithm. Although, the quality comes at a price of being a rather compu- tationally expensive algorithm.
Van de Weijer and Schmid are using an approach on the basis of the Bayes theorem [38]. Colours are histogrammed in a perceptionally linear colour space (L∗a∗b∗) and colour names
3.7. ADAPTATION METHOD COMPARISON 37