Bag of Color Features For Color Constancy

In this paper, we propose a novel color constancy approach, called Bag of Color Features (BoCF), building upon Bag-of-Features pooling. The proposed method substantially reduces the number of parameters needed for illumination estimation. At the same time, the proposed method is consistent with the color constancy assumption stating that global spatial information is not relevant for illumination estimation and local information ( edges, etc.) is sufficient. Furthermore, BoCF is consistent with color constancy statistical approaches and can be interpreted as a learning-based generalization of many statistical approaches. To further improve the illumination estimation accuracy, we propose a novel attention mechanism for the BoCF model with two variants based on self-attention. BoCF approach and its variants achieve competitive, compared to the state of the art, results while requiring much fewer parameters on three benchmark datasets: ColorChecker RECommended, INTEL-TUT version 2, and NUS8.Comment: 12 pages, 5 figures, 6 table

Filling in the retinal image

The optics of the eye form an image on a surface at the back of the eyeball called the retina. The retina contains the photoreceptors that sample the image and convert it into a neural signal. The spacing of the photoreceptors in the retina is not uniform and varies with retinal locus. The central retinal field, called the macula, is densely packed with photoreceptors. The packing density falls off rapidly as a function of retinal eccentricity with respect to the macular region and there are regions in which there are no photoreceptors at all. The retinal regions without photoreceptors are called blind spots or scotomas. The neural transformations which convert retinal image signals into percepts fills in the gaps and regularizes the inhomogeneities of the retinal photoreceptor sampling mosaic. The filling-in mechamism plays an important role in understanding visual performance. The filling-in mechanism is not well understood. A systematic collaborative research program at the Ames Research Center and SRI in Menlo Park, California, was designed to explore this mechanism. It was shown that the perceived fields which are in fact different from the image on the retina due to filling-in, control some aspects of performance and not others. Researchers have linked these mechanisms to putative mechanisms of color coding and color constancy

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

Constancy Mechanisms and the Normativity of Perception

In this essay, we draw on John Haugeland’s work in order to argue that Burge is wrong to think that exercises of perceptual constancy mechanisms suffice for perceptual representation. Although Haugeland did not live to read or respond to Burge’s Origins of Objectivity, we think that his work contains resources that can be developed into a critique of the very foundation of Burge’s approach. Specifically, we identify two related problems for Burge. First, if (what Burge calls) mere sensory responses are not representational, then neither are exercises of constancy mechanisms, since the differences between them do not suffice to imply that one is representational and the other is not. Second, taken by themselves, exercises of constancy mechanisms are only derivatively representational, so merely understanding how they work is not sufficient for understanding what is required for something, in itself, to be representational (and thereby provide a full solution to the problem of perceptual representation)

Limits to the salience of ultraviolet: Lessons from colour vision in bees and birds

Publisher version: http://jeb.biologists.org/content/204/14/2571/F1.expansio

Computational models of human vision with applications

Perceptual problems in aeronautics were studied. The mechanism by which color constancy is achieved in human vision was examined. A computable algorithm was developed to model the arrangement of retinal cones in spatial vision. The spatial frequency spectra are similar to the spectra of actual cone mosaics. The Hartley transform as a tool of image processing was evaluated and it is suggested that it could be used in signal processing applications, GR image processing