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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)
Image Content Enhancement Through Salient Regions Segmentation for People With Color Vision Deficiencies
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.
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
Classical Planning in Deep Latent Space
Current domain-independent, classical planners require symbolic models of the
problem domain and instance as input, resulting in a knowledge acquisition
bottleneck. Meanwhile, although deep learning has achieved significant success
in many fields, the knowledge is encoded in a subsymbolic representation which
is incompatible with symbolic systems such as planners. We propose Latplan, an
unsupervised architecture combining deep learning and classical planning. Given
only an unlabeled set of image pairs showing a subset of transitions allowed in
the environment (training inputs), Latplan learns a complete propositional PDDL
action model of the environment. Later, when a pair of images representing the
initial and the goal states (planning inputs) is given, Latplan finds a plan to
the goal state in a symbolic latent space and returns a visualized plan
execution. We evaluate Latplan using image-based versions of 6 planning
domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of
LightsOut.Comment: Under review at Journal of Artificial Intelligence Research (JAIR
Bayesian Optimization for Image Segmentation, Texture Flow Estimation and Image Deblurring
Ph.DDOCTOR OF PHILOSOPH
Vision Sensors and Edge Detection
Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing
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
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