Colour Transformation Algorithm On Website Images For The Colour Blind Users

Teknik penukaran daripada ruangan warna RGB ke ruangan warna HSV tertumpu pada warna merah lampu isyarat dan selalunya diterapkan pada pengenalan lampu isyarat. The conversion technique from RGB colour space to HSV colour space focuses on the red colour of the traffic lights as it is mostly applied on traffic lights recognition

Live Video and Image Recolouring for Colour Vision Deficient Patients

Colour Vision Deficiency (CVD) is an important issue for a significant population across the globe. There are several types of CVD\u27s, such as monochromacy, dichromacy, trichromacy, and anomalous trichromacy. Each of these categories contain specific other subtypes. The aim of this research is to device a scheme to address CVD by using variations in pixel plotting of colours to capture colour disparities and perform colour compensation. The proposed scheme recolours the video and images by colour contrast variation of each colour for CVD patients, and depending on the type of deficiency, it is able to provide live results. Different types of CVD’s can be identified and cured by changing the particular colour related to it and based upon the type of diseases, it performs RGB (Red, Green, and Blue) to LMS (Long, Medium, and Short) transformation. This helps in colour identification and also adjustments of colour contrasts. The processing and rendering of recoloured video and images, allows the affected patients with CVD to see perfect shades in the recoloured frames of video or images and other modes of files. In this thesis, we propose an efficient recolouring algorithm with a strong focus on real-time applications that is capable of providing different recoloured outputs based on specific types of CVD

Adaptive Methods for Color Vision Impaired Users

Color plays a key role in the understanding of the information in computer environments. It happens that about 5% of the world population is affected by color vision deficiency (CVD), also called color blindness. This visual impairment hampers the color perception, ending up by limiting the overall perception that CVD people have about the surrounding environment, no matter it is real or virtual. In fact, a CVD individual may not distinguish between two different colors, what often originates confusion or a biased understanding of the reality, including web environments, whose web pages are plenty of media elements like text, still images, video, sprites, and so on. Aware of the difficulties that color-blind people may face in interpreting colored contents, a significant number of recoloring algorithms have been proposed in the literature with the purpose of improving the visual perception of those people somehow. However, most of those algorithms lack a systematic study of subjective assessment, what undermines their validity, not to say usefulness. Thus, in the sequel of the research work behind this Ph.D. thesis, the central question that needs to be answered is whether recoloring algorithms are of any usefulness and help for colorblind people or not. With this in mind, we conceived a few preliminary recoloring algorithms that were published in conference proceedings elsewhere. Except the algorithm detailed in Chapter 3, these conference algorithms are not described in this thesis, though they have been important to engender those presented here. The first algorithm (Chapter 3) was designed and implemented for people with dichromacy to improve their color perception. The idea is to project the reddish hues onto other hues that are perceived more regularly by dichromat people. The second algorithm (Chapter 4) is also intended for people with dichromacy to improve their perception of color, but its applicability covers the adaptation of text and image, in HTML5- compliant web environments. This enhancement of color contrast of text and imaging in web pages is done while keeping the naturalness of color as much as possible. Also, to the best of our knowledge, this is the first web recoloring approach targeted to dichromat people that takes into consideration both text and image recoloring in an integrated manner. The third algorithm (Chapter 5) primarily focuses on the enhancement of some of the object contours in still images, instead of recoloring the pixels of the regions bounded by such contours. Enhancing contours is particularly suited to increase contrast in images, where we find adjacent regions that are color indistinguishable from dichromat’s point of view. To our best knowledge, this is one of the first algorithms that take advantage of image analysis and processing techniques for region contours. After accurate subjective assessment studies for color-blind people, we concluded that the CVD adaptation methods are useful in general. Nevertheless, each method is not efficient enough to adapt all sorts of images, that is, the adequacy of each method depends on the type of image (photo-images, graphical representations, etc.). Furthermore, we noted that the experience-based perceptual learning of colorblind people throughout their lives determines their visual perception. That is, color adaptation algorithms must satisfy requirements such as color naturalness and consistency, to ensure that dichromat people improve their visual perception without artifacts. On the other hand, CVD adaptation algorithms should be object-oriented, instead of pixel-oriented (as typically done), to select judiciously pixels that should be adapted. This perspective opens an opportunity window for future research in color accessibility in the field of in human-computer interaction (HCI).A cor desempenha um papel fundamental na compreensão da informação em ambientes computacionais. Porém, cerca de 5% da população mundial é afetada pela deficiência de visão de cor (ou Color Vision Deficiency (CVD), do Inglês), correntemente designada por daltonismo. Esta insuficiência visual dificulta a perceção das cores, o que limita a perceção geral que os indivíduos têm sobre o meio, seja real ou virtual. Efetivamente, um indivíduo com CVD vê como iguais cores que são diferentes, o que origina confusão ou uma compreensão distorcida da realidade, assim como dos ambientes web, onde existe uma abundância de conteúdos média coloridos, como texto, imagens fixas e vídeo, entre outros. Com o intuito de mitigar as dificuldades que as pessoas com CVD enfrentam na interpretação de conteúdos coloridos, tem sido proposto na literatura um número significativo de algoritmos de recoloração, que têm como o objetivo melhorar, de alguma forma, a perceção visual de pessoas com CVD. Porém, a maioria desses trabalhos carece de um estudo sistemático de avaliação subjetiva, o que põe em causa a sua validação, se não mesmo a sua utilidade. Assim, a principal questão à qual se pretende responder, como resultado do trabalho de investigação subjacente a esta tese de doutoramento, é se os algoritmos de recoloração têm ou não uma real utilidade, constituindo assim uma ajuda efetiva às pessoas com daltonismo. Tendo em mente esta questão, concebemos alguns algoritmos de recoloração preliminares que foram publicados em atas de conferências. Com exceção do algoritmo descrito no Capítulo 3, esses algoritmos não são descritos nesta tese, não obstante a sua importância na conceção daqueles descritos nesta dissertação. O primeiro algoritmo (Capítulo 3) foi projetado e implementado para pessoas com dicromacia, a fim de melhorar a sua perceção da cor. A ideia consiste em projetar as cores de matiz avermelhada em matizes que são melhor percebidos pelas pessoas com os tipos de daltonismo em causa. O segundo algoritmo (Capítulo 4) também se destina a melhorar a perceção da cor por parte de pessoas com dicromacia, porém a sua aplicabilidade abrange a adaptação de texto e imagem, em ambientes web compatíveis com HTML5. Isto é conseguido através do realce do contraste de cores em blocos de texto e em imagens, em páginas da web, mantendo a naturalidade da cor tanto quanto possível. Além disso, tanto quanto sabemos, esta é a primeira abordagem de recoloração em ambiente web para pessoas com dicromacia, que trata o texto e a imagem de forma integrada. O terceiro algoritmo (Capítulo 5) centra-se principalmente na melhoria de alguns dos contornos de objetos em imagens, em vez de aplicar a recoloração aos pixels das regiões delimitadas por esses contornos. Esta abordagem é particularmente adequada para aumentar o contraste em imagens, quando existem regiões adjacentes que são de cor indistinguível sob a perspetiva dos observadores com dicromacia. Também neste caso, e tanto quanto é do nosso conhecimento, este é um dos primeiros algoritmos em que se recorre a técnicas de análise e processamento de contornos de regiões. Após rigorosos estudos de avaliação subjetiva com pessoas com daltonismo, concluiu-se que os métodos de adaptação CVD são úteis em geral. No entanto, cada método não é suficientemente eficiente para todos os tipo de imagens, isto é, o desempenho de cada método depende do tipo de imagem (fotografias, representações gráficas, etc.). Além disso, notámos que a aprendizagem perceptual baseada na experiência das pessoas daltónicas ao longo de suas vidas é determinante para perceber aquilo que vêem. Isto significa que os algoritmos de adaptação de cor devem satisfazer requisitos tais como a naturalidade e a consistência da cor, de modo a não pôr em causa aquilo que os destinatários consideram razoável ver no mundo real. Por outro lado, a abordagem seguida na adaptação CVD deve ser orientada aos objetos, em vez de ser orientada aos pixéis (como tem sido feito até ao momento), de forma a possibilitar uma seleção mais criteriosa dos pixéis que deverão ser sujeitos ao processo de adaptação. Esta perspectiva abre uma janela de oportunidade para futura investigação em acessibilidade da cor no domínio da interacção humano-computador (HCI)

Individualized Models of Colour Differentiation through Situation-Specific Modelling

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

Computer-Based Solutions to Support Those With Colour Vision Deficiency to Access Day-to-Day Information

In modern-day society, we are bombarded with vast amounts of electronic information which we may be expected to make decisions from. Many people have difficulties in interpreting such information due to either physical or cognitive difficulties in using electronic devices, or an inability to identify information as intended by the author. Colour Vision Deficiency (CVD) is one such problem that can cause considerable difficulty in the interpretation of diagrammatical information. This is because a Colour Vision Deficient (CVDt) person has difficulty in seeing: colour boundaries, different shades of colour and different hues. There has been some research to aid the CVDt, where the majority of the research in image processing changes or transforms colours in any given image. Such transformations use a number of different algorithms to create a CVDt friendly post-processed image from the pre-processed image. A major problem of current transformation algorithms is that they are aimed for specific contexts and cannot be used in generic contexts. For example, the transformation algorithm may be aimed at aiding the CVDt to view postprocessed images of weather maps only. The aim of this dissertation is to provide an improved post-processed image algorithm. The algorithm is intended to provide the CVDt with greater benefit by being able to interpret the information in the post-processed image correctly. The algorithm used in this dissertation is not a colour transformation algorithm instead it is a colour separation algorithm. This concept of colour separation is novel. The colour separation algorithm, which is called the Halo-Effect Algorithm (HEA), parses a given image row-by-row and pixel-by-pixel until the end of file-marker is reached and a CVDt friendly post-processed image is furnished. When there is a colour change between two identified pixels then a colour boundary has been identified within the pre-processed image and a differently coloured pixel is inserted between two, furnishing the post-processed image. As the pre-processed image is parsed row-by-row then the colour the boundary builds up to form a colour boundary interface where the different coloured pixel are inserted in the post-processed image. In this dissertation the separation pixel is always white. The build-up of inserted white pixels at the colour boundary interface of the pre-processed image produces a halo like effect in the post-processed image which is CVDt friendly. To demonstrate the efficacy of the colour separation concept, the HEA has been developed and implemented. A number of surveys have been conducted using participant responses to questions within each survey. The responses that each participant gave were then collated and analysed statistically. Two statistical techniques were used to test a number of hypotheses around the mean of a sample drawn from a normally distributed population. In this dissertation the normally distributed populations were the survey participants. From the analyses of the responses, the survey population was divided into two groups. One group was identified to have no problem with identification of pre-processed colour boundaries and were called the non-CVDt. A second group was identified to be those who had some problems with the identification of pre-processed colour boundaries and were called the indicative- CVDt. Responses from the two groups were collated and statistical analyses were then conducted to test the significance of any results obtained and also to test the validity of the algorithms under investigation. In this dissertation two currently available, but different, colour transformation algorithms were compared with the colour separation algorithm of the HEA. Each of the two transformation algorithms were originally intended for specific use. One was aimed for spectra maps and the other was aimed for background text. Statistical analyses showed that each of the transformation algorithms provided benefit to the indicative-CVDt for their specific context only. However, statistical analyses also showed that HEA fared well in each of the two specific contexts. Thus, hinting that colour separation of HEA could be used in more general contexts. To confirm that colour separation can provide greater benefit to the indicative-CVDt in more generic contexts than colour transformations further surveys were undertaken. In each survey participants were asked a number of questions about a given image where colour boundaries are expected to occur frequently. One was a map of the provinces of Australia and the other a number of differently coloured geometric shapes. Statistical analyses showed that the colour separation algorithm of HEA provided greater benefit to the indicative-CVDt than the two colour transformation algorithms in both cases. Hence, confirming that colour separation of HEA is beneficial to the indicative-CVDt in generic contexts. Colour separation of the HEA is still in its infancy and a great deal more research is required to determine how great its efficacy is. For example, clinical studies could be undertaken using two sets from one population. One set of participants who would have been diagnosed as non-CVDt, which would be identified as a control group, and a second set who would have been diagnosed as CVDt, which would be identified as a test set

Catch the Thief: An Approach to an Accessible Video Game with Unity

Today, the video game industry is one of the most profitable business markets in the world. Video games are not only being used as a means of entertainment but also to reinforce education. Even though there are unbroken barriers for disabled people to use this kind of applications. Lack of accessible technologies and functions are real problems and a way of discrimination. It is a challenge for every software development organization, even for those who focuses in video game line of work. Many impaired people enjoy playing games in despite of their disabilities; however, some limitations appear when they start to play. This article presents an approach for an accessible video game developing, using Unity Engine and some of its accessibility complements to implement some functions to get better the player experience. This way, people who suffer of visual and hearing disability can be able to play and learn. Within the spectrum of disabilities this project covers are; visual and hearing, multiple variants of color-blindness and reduced vision problems. A series of settings options will have implemented with the final purpose of giving users an easier way to interact with the video game. It should be emphasized that game mechanics are based on various parameters to offer accessibility as brightness reduction, contrast and font-size adjustment, and more. Disability simulation tests will have done in order to prove the video game functionality. This research tries to increase the accessibility for people with impairments in the world of video games

Color transformation for protanopia color vision deficiency using integration of image processing and artificial neural network

Color blindness deficiency is inability to distinguish colors with each other. Nowadays, the individual who are not being able to recognize color may be crucial in some day life situation because many common activities depend on signals with color-coded such as road sign, traffic light, electric wire, resistor and many more. There are many forms of color blindness such Monochromacy (total color blindness), Dichromacy (Red/ Green/Blue blindness) and Trichromacy and etc. Most types of defective color blindness can be classified into two categories which are green color defective and red color defective. The objective of this project is to improve the ability of color discrimination for Protanopia which a type of dichromacy where the patients does not naturally develop red color or Long wavelength cones in their eyes. This project proposed a method using image processing to improve the ability of color discrimination for Protanopia as well as adjusting images such that a person suffering from Protanopia is able perceive image detail and color dynamics. This method is first developed by simulating an image through the eyes of a person suffering from protanopia by converting RGB space to LMS (long, medium, short) color space based on cone response and then modifies the response of the deficient cones. The linear multiplication matrix is derived by referring to CIE color matching functions. ANN is then set up by using the input/output from matrix conversion. For this research, the ANN is introduced to reduce simulation time in image processing. The transformation technique used is RGB Color Contrasting where this step is to enhance contrast between red and green which in general, make green pixels appear to be bluer. Based on the result, the objectives are successfully achieved. ANN gives the minimum computational time than conventional matrix conversion which is 36% increment. The changes of the image drastically for both color blind and non-color blind viewers. The result shows that the reds become redder and greens become greener from the image before being adjusted