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This paper presents a three-color balance adjustment for color constancy correction. White balancing is a typical adjustment for color constancy in an image, but there are still lighting effects on colors other than white. Cheng et al. proposed multi-color balancing to improve the performance of white balancing by mapping multiple target colors into corresponding ground truth colors. However, there are still three problems that have not been discussed: choosing the number of target colors, selecting target colors, and minimizing error which causes computational complexity to increase. In this paper, we first discuss the number of target colors for multi-color balancing. From our observation, when the number of target colors is greater than or equal to three, the best performance of multi-color balancing in each number of target colors is almost the same regardless of the number of target colors, and it is superior to that of white balancing. Moreover, if the number of target colors is three, multi-color balancing can be performed without any error minimization. Accordingly, we propose three-color balancing. In addition, the combination of three target colors is discussed to achieve color constancy correction. In an experiment, the proposed method not only outperforms white balancing but also has almost the same performance as Cheng’s method with 24 target colors.

Physicist Gabriel Lippmann's (1845–1921) photographic process is one of the oldest methods for producing colour photographs. So why do the achievements of this 1908 Nobel laureate remain mostly unknown outside niche circles? Using the centenary of Lippmann’s death as an opportunity to reflect upon his scientific, photographic, and cultural legacy, this book is the first to explore his interferential colour photography. Initially disclosed in 1891, the emergence of this medium is considered here through three shaping forces: science, media, and museums. A group of international scholars reassess Lippmann’s reception in the history of science, where he is most recognised, by going well beyond his endeavours in France and delving into the complexity of his colour photography as a challenge to various historiographies. Moreover, they analyse colour photographs as optical media, thus pluralising Lippmann photography's ties to art, cultural and imperial history, as well as media archaeology. The contributors also focus on the interferential plate as a material object in need of both preservation and exhibition, one that continues to fascinate contemporary analogue photographers. This volume allows readers to get to know Lippmann, grasp the interdisciplinary complexity of his colourful work, and ultimately expand his place in the history of photography.

Slide-free digital pathology techniques, including nondestructive 3D microscopy, are gaining interest as alternatives to traditional slide-based histology. In order to facilitate clinical adoption of these fluorescence-based techniques, software methods have been developed to convert grayscale fluorescence images into color images that mimic the appearance of standard absorptive chromogens such as hematoxylin and eosin (H&E). However, these false-coloring algorithms often require manual and iterative adjustment of parameters, with results that can be inconsistent in the presence of intensity nonuniformities within an image and/or between specimens (intra- and inter-specimen variability). Here, we present an open-source (Python-based) rapid intensity-leveling and digital-staining package that is specifically designed to render two-channel fluorescence images (i.e. a fluorescent analog of H&E) to the traditional H&E color space for 2D and 3D microscopy datasets. However, this method can be easily tailored for other false-coloring needs. Our package offers (1) automated and uniform false coloring in spite of uneven staining within a large thick specimen, (2) consistent color-space representations that are robust to variations in staining and imaging conditions between different specimens, and (3) GPU-accelerated data processing to allow these methods to scale to large datasets. We demonstrate this platform by generating H&E-like images from cleared tissues that are fluorescently imaged in 3D with open-top light-sheet (OTLS) microscopy, and quantitatively characterizing the results in comparison to traditional slide-based H&E histology.

This paper constructs a Chinese painting color teaching platform based on intelligent image color processing technology. Firstly, the region segmentation method is used to reasonably segment the pixel points according to the similar values of color and texture parameters of the image. Then, the image’s primary color grayscale parameters are fuzzy, detected, equalized, and fused by color difference values. Finally, the gradient optimization algorithm is combined to identify and control the image color parameters. The results show that the highest value of peak signal-to-noise ratio can reach 73.4db for the images processed by the method of this paper, and the mean value of STRESS is kept between 0-10%. The intelligent image color processing technique has been proven to enhance the aesthetics and innovation of Chinese painting colors in students.

This article develops theoretical, algorithmic, perceptual, and interaction aspects of script legibility enhancement in the visible light spectrum for the purpose of scholarly editing of papyri texts. Novel legibility enhancement algorithms based on color processing and visual illusions are compared to classic methods in a user experience experiment. (1) The proposed methods outperformed the comparison methods. (2) Users exhibited a broad behavioral spectrum, under the influence of factors such as personality and social conditioning, tasks and application domains, expertise level and image quality, and affordances of software, hardware, and interfaces. No single enhancement method satisfied all factor configurations. Therefore, it is suggested to offer users a broad choice of methods to facilitate personalization, contextualization, and complementarity. (3) A distinction is made between casual and critical vision on the basis of signal ambiguity and error consequences. The criteria of a paradigm for enhancing images for critical applications comprise: interpreting images skeptically; approaching enhancement as a system problem; considering all image structures as potential information; and making uncertainty and alternative interpretations explicit, both visually and numerically.

Dyschromatopsia is a disorder involving difficulty or inability to recognize color distributions and distinguish between shades of different colours in severe cases. Recently, a non-invasive algorithmic technique for its diagnosis and monitoring has been proposed. Based on these results, in this paper, the DEA (Dyschromatopsia re-Education Algorithm) algorithm for the re-education of the eye to color vision is presented through a training path that exploits brain plasticity. DEA has also been converted into an app in order to run on personal smartphones. Also, for this reason, the use of DEA can take place without the help of a professional figure. After the diagnostic phase in which type and severity of dyschromatopsia are assessed, the algorithm proceeds with eye re-education by exploiting the brain’s plastic readjustment ability. The re-education method is based on the standard artificial intelligence learning model. Eye re-education shows progressive improvement in visual color ability and color distribution. Moreover, the achieved results appear to be permanent (at least within three months after training). This study represents a proof of concept for the proposed re-educational method. The results from the pilot experimentation are promising and demonstrate the feasibility of the approach. Future studies on a larger scale, in collaboration with clinical experts, will be necessary to validate and optimize the methodology.

Literacy affects many aspects of language and cognition, including the shift from a more holistic mode of processing to a more analytical part-based mode of processing. Here we examined whether this shift impacts the ability of preschool and primary school children to learn the rules underlying a finite-state grammar using an artificial grammar learning (AGL) paradigm implemented with either linguistic (letters) or non-linguistic (colors) materials to further examine if children’s AGL performance was modulated by type of stimuli. Both tasks involved a training phase in which half of the preschool children and half of the primary school children were exposed to a set of either letter or color strings without any information about the rules underlying the construction of those strings. Later, in the test phase, they were asked to decide whether a new set of letter or color strings conformed to those rules to test grammar learning. Results showed that only primary school children showed evidence of learning, and, importantly, only with colors. These findings seem to support the view that learning to read promotes reliance on smaller linguistic units that might hinder the ability of first-graders to learn the rules underlying finite-state grammars implemented with linguistic materials.

The class of quaternion matrix optimization (QMO) problems, with quaternion matrices as decision variables, has been widely used in color image processing and other engineering areas in recent years. However, optimization theory for QMO is far from adequate. The main objective of this paper is to provide necessary theoretical foundations on optimality analysis, in order to enrich the contents of optimization theory and to pave way for the design of efficient numerical algorithms as well. We achieve this goal by conducting a thorough study on the first-order and second-order (sub)differentiation of real-valued functions in quaternion matrices, with a newly introduced operation called R-product as the key tool for our calculus. Combining with the classical optimization theory, we establish the first-order and the second-order optimality analysis for QMO. Particular treatments on convex functions, the ℓ0-norm and the rank function in quaternion matrices are tailored for a sparse low rank QMO model, arising from color image denoising, to establish its optimality conditions via stationarity.

Many pollinating insects acquire their entire nutrition from visiting flowers, and they must therefore be efficient both at detecting flowers and at recognizing familiar rewarding flower types. A crucial first step in recognition is the identification of edges and the segmentation of the visual field into areas that belong together. Honeybees and bumblebees acquire visual information through three types of photoreceptors; however, they only use a single receptor type—the one sensitive to longer wavelengths—for edge detection and movement detection. Here, we show that these long-wavelength receptors (peak sensitivity at ~544 nm, i.e., green) provide the most consistent signals in response to natural objects. Using our multispectral image database of flowering plants, we found that long-wavelength receptor responses had, depending on the specific scenario, up to four times higher signal-to-noise ratios than the short- and medium-wavelength receptors. The reliability of the long-wavelength receptors emerges from an intricate interaction between flower coloration and the bee’s visual system. This finding highlights the adaptive significance of bees using only long-wavelength receptors to locate flowers among leaves, before using information provided by all three receptors to distinguish the rewarding flower species through trichromatic color vision.

With the invent of automobile and advances in the world of machinery, there is a notable increase in the number of vehicles, two wheelers and three wheelers in the road transport. With the raise in the number of vehicles there is a lot of traffic violations that is happening in the road system. Based on the Indian traffic road system, monitoring the violation of such a incident is a huge task and also a tedious system. But however steps are taken to monitor the traffic breach that happens in the road transport through License Plate Detection mechanism for the vehicles that involves in over speeding or violating the rules. License plate location is a very important concept in vehicle license plate recognition for intelligent transport systems. Number plates can have different shapes and sizes along with different colors. The most common vehicle number plate in India have the background color as yellow or white with the font color black. Identification of number plate for vehicles in India has been discussed in this paper and the numbers have been segmented to identify them specifically. Our focus here is on two main steps: first is to find the number plate and second the segmentation of the number to identify them specifically. The main objective of this paper is to efficiently design and implement a method for License Plate Recognition (LPR) of Indian License Plates. We have manually acquired the images of various vehicles. Here for the detection point, we have used COCO-API RCNN and K-NEAREST NEIGHBOR.