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

Acquisition of Surface Light Fields from Videos

La tesi presenta un nuovo approccio per la stima di Surface Light Field di oggetti reali, a partire da sequenze video acquisite in condizioni di illuminazione fisse e non controllate. Il metodo proposto si basa sulla separazione delle due componenti principali dell'apparenza superficiale dell'oggetto: la componente diffusiva, modellata come colore RGB, e la componente speculare, approssimata mediante un modello parametrico funzione della posizione dell'osservatore. L'apparenza superficiale ricostruita permette una visualizzazione fotorealistica e in real-time dell'oggetto al variare della posizione dell'osservatore, consentendo una navigazione 3D interattiva

An Interactive App for Color Deficient Viewers

Color deficient individuals have trouble seeing color contrasts that could be very apparent to individuals with normal color vision. For example, for some color deficient individuals, red and green apples do not have the striking contrast they have for those with normal color vision, or the abundance of red cherries in a tree is not immediately clear due to a lack of perceived contrast. We present a smartphone app that enables color deficient users to visualize such problematic color contrasts in order to help them with daily tasks. The user interacts with the app through the touchscreen. As the user traces a path around the touchscreen, the colors in the image change continuously via a transform that enhances contrasts that are weak or imperceptible for the user under native viewing conditions. Specifically, we propose a transform that shears the data along lines parallel to the dimension corresponding to the affected cone sensitivity of the user. The amount and direction of shear are controlled by the user'sfinger movement over the touchscreen allowing them to visualize these contrasts. Using the GPU, this simple transformation, consisting of a linear shear and translation, is performed efficiently on each pixel and in real-time with the changing position of the user's finger. The user can use the app to aid daily tasks such as distinguishing between red and green apples or picking out ripe bananas

Aerospace medicine and biology. A continuing bibliography with indexes, supplement 206, May 1980

This bibliography lists 169 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980

Laparoscopic Image Recovery and Stereo Matching

Laparoscopic imaging can play a significant role in the minimally invasive surgical procedure. However, laparoscopic images often suffer from insufficient and irregular light sources, specular highlight surfaces, and a lack of depth information. These problems can negatively influence the surgeons during surgery, and lead to erroneous visual tracking and potential surgical risks. Thus, developing effective image-processing algorithms for laparoscopic vision recovery and stereo matching is of significant importance. Most related algorithms are effective on nature images, but less effective on laparoscopic images. The first purpose of this thesis is to restore low-light laparoscopic vision, where an effective image enhancement method is proposed by identifying different illumination regions and designing the enhancement criteria for desired image quality. This method can enhance the low-light region by reducing noise amplification during the enhancement process. In addition, this thesis also proposes a simplified Retinex optimization method for non-uniform illumination enhancement. By integrating the prior information of the illumination and reflectance into the optimization process, this method can significantly enhance the dark region while preserving naturalness, texture details, and image structures. Moreover, due to the replacement of the total variation term with two $l_2$-norm terms, the proposed algorithm has a significant computational advantage. Second, a global optimization method for specular highlight removal from a single laparoscopic image is proposed. This method consists of a modified dichromatic reflection model and a novel diffuse chromaticity estimation technique. Due to utilizing the limited color variation of the laparoscopic image, the estimated diffuse chromaticity can approximate the true diffuse chromaticity, which allows us to effectively remove the specular highlight with texture detail preservation. Third, a robust edge-preserving stereo matching method is proposed, based on sparse feature matching, left and right illumination equalization, and refined disparity optimization processes. The sparse feature matching and illumination equalization techniques can provide a good disparity map initialization so that our refined disparity optimization can quickly obtain an accurate disparity map. This approach is particularly promising on surgical tool edges, smooth soft tissues, and surfaces with strong specular highlight

Haze visibility enhancement: A Survey and quantitative benchmarking

This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are categorized into: multiple image methods, polarizing filter-based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well-known single-image methods, based on a recent dataset provided by Fattal (2014) and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method.This study is supported by an Nvidia GPU Grant and a Canadian NSERC Discovery grant. R. T. Tan’s work in this research is supported by the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centre in Singapore Funding Initiativ