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

Spectacularly Binocular: Exploiting Binocular Luster Effects for HCI Applications

Ph.DDOCTOR OF PHILOSOPH

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

A predictive model of colour differentiation

The ability to differentiate between colours varies from individual to individual. This variation is attributed to factors such as the presence of colour blindness. Colour is used to encode information in information visualizations. An example of such an encoding is categorization using colour (e.g., green for land, blue for water). As a result of the variation in colour differentiation ability among individuals, many people experience difficulties when using colour-encoded information visualizations. These difficulties result from the inability to adequately differentiate between two colours, resulting in confusion, errors, frustration, and dissatisfaction. If a user-specific model of colour differentiation was available, these difficulties could be predicted and corrected. Prediction and correction of these difficulties would reduce the amount of confusion, errors, frustration, and dissatisfaction experienced by users. This thesis presents a model of colour differentiation that is tuned to the abilities of a particular user. To construct this model, a series of judgement tasks are performed by the user. The data from these judgement tasks is used to calibrate a general colour differentiation model to the user. This calibrated model is used to construct a predictor. This predictor can then be used to make predictions about the user's ability to differentiate between two colours. Two participant-based studies were used to evaluate this solution. The first study evaluated the basic approach used to model colour differentiation. The second study evaluated the accuracy of the predictor by comparing its performance to the performance of human participants. It was found that the predictor was as accurate as the human participants 86.3% of the time. Using such a predictor, the colour differentiation abilities of particular users can be accurately modeled

Addressing the Needs of Students With Color Vision Deficiencies in the Elementary School Library

Color vision deficiencies affect approximately eight percent of the male population (Birch & Chisholm, 2008; Cole, 2007; Jenny & Kelso, 2007; Neitz & Neitz, 2000), yet the condition is often overlooked in the educational setting despite the pervasiveness of color in the school (Suero et al., 2004). The purpose of this study was to explore how elementary school librarians provide instruction and prepare the library environment to meet the needs of students with color vision deficiencies. This mixed methods study consisted of two components. The first component was a questionnaire administered to elementary school librarians throughout Virginia to gather data related to their knowledge of and attitudes toward students with color vision deficiencies. The second component of the study was a case study of eight elementary school librarians in one school division within Virginia. The case study participants were given the same questionnaire as the state group to assess their knowledge of and attitudes toward students with color vision deficiencies. Then they participated in observations, interviews, and a color vision deficiencies awareness training designed to inform school librarians about issues related to color vision deficiencies. The training was followed by a series of journal prompts through a blog and additional observations to gather information about changes in behaviors. After the training, a post-test was administered to the case study participants to gather data about changes in knowledge, attitudes, and behaviors. Findings indicate that elementary school librarians did not feel knowledgeable about color vision deficiencies but were interested in knowing more and expressed a desire to make changes based on participating in the questionnaire alone. The case study participants\u27 increase in knowledge from pretest to post-test was statistically significant. While the change in attitude on the questionnaire was not found to be statistically significant for the case study group, there were noticeable changes in beliefs and desires to change behaviors as evidenced through the qualitative data. The changes in the case study lend support to the need for color vision deficiency awareness training for elementary school educators

Colour and Colorimetry Multidisciplinary Contributions Vol. XIb

It is well known that the subject of colour has an impact on a range of disciplines. Colour has been studied in depth for many centuries, and as well as contributing to theoretical and scientific knowledge, there have been significant developments in applied colour research, which has many implications for the wider socio-economic community. At the 7th Convention of Colorimetry in Parma, on the 1st October 2004, as an evolution of the previous SIOF Group of Colorimetry and Reflectoscopy founded in 1995, the "Gruppo del Colore" was established. The objective was to encourage multi and interdisciplinary collaboration and networking between people in Italy that addresses problems and issues on colour and illumination from a professional, cultural and scientific point of view. On the 16th of September 2011 in Rome, in occasion of the VII Color Conference, the members assembly decided to vote for the autonomy of the group. The autonomy of the Association has been achieved in early 2012. These are the proceedings of the English sessions of the XI Conferenza del Colore

Doctor of Philosophy

dissertationConfocal microscopy has become a popular imaging technique in biology research in recent years. It is often used to study three-dimensional (3D) structures of biological samples. Confocal data are commonly multichannel, with each channel resulting from a different fluorescent staining. This technique also results in finely detailed structures in 3D, such as neuron fibers. Despite the plethora of volume rendering techniques that have been available for many years, there is a demand from biologists for a flexible tool that allows interactive visualization and analysis of multichannel confocal data. Together with biologists, we have designed and developed FluoRender. It incorporates volume rendering techniques such as a two-dimensional (2D) transfer function and multichannel intermixing. Rendering results can be enhanced through tone-mappings and overlays. To facilitate analyses of confocal data, FluoRender provides interactive operations for extracting complex structures. Furthermore, we developed the Synthetic Brainbow technique, which takes advantage of the asynchronous behavior in Graphics Processing Unit (GPU) framebuffer loops and generates random colorizations for different structures in single-channel confocal data. The results from our Synthetic Brainbows, when applied to a sequence of developing cells, can then be used for tracking the movements of these cells. Finally, we present an application of FluoRender in the workflow of constructing anatomical atlases