99 research outputs found

    Image Content Enhancement Through Salient Regions Segmentation for People With Color Vision Deficiencies

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    Color vision deficiencies affect visual perception of colors and, more generally, color images. Several sciences such as genetics, biology, medicine, and computer vision are involved in studying and analyzing vision deficiencies. As we know from visual saliency findings, human visual system tends to fix some specific points and regions of the image in the first seconds of observation summing up the most important and meaningful parts of the scene. In this article, we provide some studies about human visual system behavior differences between normal and color vision-deficient visual systems. We eye-tracked the human fixations in first 3 seconds of observation of color images to build real fixation point maps. One of our contributions is to detect the main differences between the aforementioned human visual systems related to color vision deficiencies by analyzing real fixation maps among people with and without color vision deficiencies. Another contribution is to provide a method to enhance color regions of the image by using a detailed color mapping of the segmented salient regions of the given image. The segmentation is performed by using the difference between the original input image and the corresponding color blind altered image. A second eye-tracking of color blind people with the images enhanced by using recoloring of segmented salient regions reveals that the real fixation points are then more coherent (up to 10%) with the normal visual system. The eye-tracking data collected during our experiments are in a publicly available dataset called Eye-Tracking of Color Vision Deficiencies

    Image Content Enhancement Through Salient Regions Segmentation for People With Color Vision Deficiencies.

    Get PDF
    Color vision deficiencies affect visual perception of colors and, more generally, color images. Several sciences such as genetics, biology, medicine, and computer vision are involved in studying and analyzing vision deficiencies. As we know from visual saliency findings, human visual system tends to fix some specific points and regions of the image in the first seconds of observation summing up the most important and meaningful parts of the scene. In this article, we provide some studies about human visual system behavior differences between normal and color vision-deficient visual systems. We eye-tracked the human fixations in first 3 seconds of observation of color images to build real fixation point maps. One of our contributions is to detect the main differences between the aforementioned human visual systems related to color vision deficiencies by analyzing real fixation maps among people with and without color vision deficiencies. Another contribution is to provide a method to enhance color regions of the image by using a detailed color mapping of the segmented salient regions of the given image. The segmentation is performed by using the difference between the original input image and the corresponding color blind altered image. A second eye-tracking of color blind people with the images enhanced by using recoloring of segmented salient regions reveals that the real fixation points are then more coherent (up to 10%) with the normal visual system. The eye-tracking data collected during our experiments are in a publicly available dataset called Eye-Tracking of Color Vision Deficiencies

    Individualized Models of Colour Differentiation through Situation-Specific Modelling

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    In digital environments, colour is used for many purposes: for example, to encode information in charts, signify missing field information on websites, and identify active windows and menus. However, many people have inherited, acquired, or situationally-induced Colour Vision Deficiency (CVD), and therefore have difficulties differentiating many colours. Recolouring tools have been developed that modify interface colours to make them more differentiable for people with CVD, but these tools rely on models of colour differentiation that do not represent the majority of people with CVD. As a result, existing recolouring tools do not help most people with CVD. To solve this problem, I developed Situation-Specific Modelling (SSM), and applied it to colour differentiation to develop the Individualized model of Colour Differentiation (ICD). SSM utilizes an in-situ calibration procedure to measure a particular user’s abilities within a particular situation, and a modelling component to extend the calibration measurements into a full representation of the user’s abilities. ICD applies in-situ calibration to measuring a user’s unique colour differentiation abilities, and contains a modelling component that is capable of representing the colour differentiation abilities of almost any individual with CVD. This dissertation presents four versions of the ICD and one application of the ICD to recolouring. First, I describe the development and evaluation of a feasibility implementation of the ICD that tests the viability of the SSM approach. Second, I present revised calibration and modelling components of the ICD that reduce the calibration time from 32 minutes to two minutes. Next, I describe the third and fourth ICD versions that improve the applicability of the ICD to recolouring tools by reducing the colour differentiation prediction time and increasing the power of each prediction. Finally, I present a new recolouring tool (ICDRecolour) that uses the ICD model to steer the recolouring process. In a comparative evaluation, ICDRecolour achieved 90% colour matching accuracy for participants – 20% better than existing recolouring tools – for a wide range of CVDs. By modelling the colour differentiation abilities of a particular user in a particular environment, the ICD enables the extension of recolouring tools to helping most people with CVD, thereby reducing the difficulties that people with CVD experience when using colour in digital environments

    Colour constancy in dichromats and trichromats: dependence on task

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    An important topic in the field of colour vision is the impact of colour vision deficiencies on daily life tasks. Investigating the extent to which colour constancy (i.e. the ability to recognise surface colour under different illuminants) is preserved in colour vision- deficient observers can provide us with insight into the nature and function of trichromatic colour vision. The first chapter of this thesis provides a summary of the very basics of colour vision, colour vision deficiencies, as well as colour constancy. Studies conducted on the colour constancy abilities of colour-vision-deficient observers versus those with normal colour vision are reviewed. The second chapter presents and reports the aims and methods of the proposed experiment (which could not take place due to the COVID-19 pandemic). This experiment investigated the colour constancy abilities of trichromats versus dichromats using two different colour constancy tasks (2D achromatic adjustment vs. 3D blocks-copying/selection task) and aimed to show how colour constancy depends on observer type as well as task type. The third chapter comprises of a computerised simulation. This simulation aimed to model the colour constancy of “ideal” observers when presented with various surfaces and illuminants. These observers involve simulated normal trichromats, anomalous trichromats and dichromats. A variety of yellow, blue, green and red illuminant shifts (from neutral daylight) were used, and surface chromaticity and observer types were compared. Overall, whilst no three-way interaction between illuminant shift, surface chromaticity and observer type were found in the simulation, strong main effects were found. It is suggested that a combination of simulated and experimental research is needed to understand the colour constancy mechanisms underpinning dichromacy and trichromacy at multiple levels (cone-based, cognitive and computational)

    Commercial harvesting has driven the evolution of camouflage in an alpine plant

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Colour in nature mediates numerous among and within species interactions, and anthropogenic impacts have long had major influences on the colour evolution of wild animals. An under-explored area is commercial harvesting, which in animals can exert a strong selection pressure on various traits, sometimes greater even that natural selection or other human activities. Natural populations of plants that are used by humans have likely also suffered strong pressure from harvesting, yet the potential for evolutionary change induced by humans has received surprisingly little attention. Here we show that the leaf coloration of a herb used in traditional Chinese medicine (Fritillaria delavayi) varies among populations, with leaves matching their local backgrounds most closely. The degree of background matching correlates with estimates of harvest pressure, with plants being more cryptic in heavily collected populations. In a human search experiment, the time it took participants to find plants was greatly influenced by target concealment. These results point to humans as driving the evolution of camouflage in populations of this species through commercial harvesting, changing the phenotype of wild plants in an unexpected and dramatic wa

    Estimation of illuminants from color signals of illuminated objects

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    Color constancy is the ability of the human visual systems to discount the effect of the illumination and to assign approximate constant color descriptions to objects. This ability has long been studied and widely applied to many areas such as color reproduction and machine vision, especially with the development of digital color processing. This thesis work makes some improvements in illuminant estimation and computational color constancy based on the study and testing of existing algorithms. During recent years, it has been noticed that illuminant estimation based on gamut comparison is efficient and simple to implement. Although numerous investigations have been done in this field, there are still some deficiencies. A large part of this thesis has been work in the area of illuminant estimation through gamut comparison. Noting the importance of color lightness in gamut comparison, and also in order to simplify three-dimensional gamut calculation, a new illuminant estimation method is proposed through gamut comparison at separated lightness levels. Maximum color separation is a color constancy method which is based on the assumption that colors in a scene will obtain the largest gamut area under white illumination. The method was further derived and improved in this thesis to make it applicable and efficient. In addition, some intrinsic questions in gamut comparison methods, for example the relationship between the color space and the application of gamut or probability distribution, were investigated. Color constancy methods through spectral recovery have the limitation that there is no effective way to confine the range of object spectral reflectance. In this thesis, a new constraint on spectral reflectance based on the relative ratios of the parameters from principal component analysis (PCA) decomposition is proposed. The proposed constraint was applied to illuminant detection methods as a metric on the recovered spectral reflectance. Because of the importance of the sensor sensitivities and their wide variation, the influence from the sensor sensitivities on different kinds of illuminant estimation methods was also studied. Estimation method stability to wrong sensor information was tested, suggesting the possible solution to illuminant estimation on images with unknown sources. In addition, with the development of multi-channel imaging, some research on illuminant estimation for multi-channel images both on the correlated color temperature (CCT) estimation and the illuminant spectral recovery was performed in this thesis. All the improvement and new proposed methods in this thesis are tested and compared with those existing methods with best performance, both on synthetic data and real images. The comparison verified the high efficiency and implementation simplicity of the proposed methods
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