Recoloração de imagens para dicromatas baseada em mapas elásticos

TCC(graduação) - Universidade Federal de Santa Catarina. Campus Araranguá. Engenharia da Computação.A deficiência na percepção de cores (DPC) afeta 8% da população caucasiana masculina, causada pela falha ou ausência de células fotorreceptoras do tipo cone na retina, e proveniente de causa genética, alguma lesão no olho, ou também devido a outras doenças, como diabetes, leucemia, etc. O indivíduo com DPC tem dificuldades na percepção de cores, que variam dependendo do tipo de deficiência. Dicromatas são os indivíduos com DPC causada pela ausência de um dos tipos de fotorreceptores cone, causando dificuldades na percepção das cores. A DPC causa dificuldades na realização de tarefas que necessitam da distinção de cores, o que pode prejudicar o indivíduo tanto na vida pessoal quanto profissional. Este trabalho propõe uma técnica de recoloração de imagens para dicromatas baseada na técnica de redução de dimensionalidade Mapas Elásticos, onde o objetivo é proporcionar aos indivíduos imagens que preservam detalhes da imagem original, como contrastes entre cores, os quais, os dicromatas não percebem. A técnica foi implementada tanto para CPU como para GPU, apresentando bons tempos de execução, além de apresentar bons resultados no aspecto da preservação de contrastes após a recoloração, a técnica também se propõe a preservar o aspecto de naturalidade da imagem, escolhendo o mapeamento final que minimiza a soma total das distância entre a cor original e o mapeamento dela no plano de percepção dos dicromatas.Color Vision Deficiency (CVD) affects 8% of caucasian male populations, caused by failure or absence of cone-like photorreceptor cells in the retina. CVD may be from genetic cause, some eye injury, or from other diseases such as diabetes, leukemia, etc. Individuals with CVD have difficulty in color perception, whose variation depends on the type of disability. Dichromats are individuals with CVD caused by the abscence of one of the types of cone photoreceptors, causing difficulties in the perception of colors. CVD causes difficulties in performing tasks that require color distinction, which can harm the individual in both personal and professional life. This work proposes an image recoloring technique for dichromats based on the Elastic Maps dimensionality reduction technique, where the objective is to provide images that preserve details of the original image, such as color contrasts. The technique was implemented both CPU and GPU, presenting good execution times, and good results in the aspect of preservation of contrasts after recoloring, the technique also proposes to preserve the aspect of naturality of image, choosing the final mapping that minimizes the total sum of the distance between the original color and the mapping of it in the plane of dichromat perception

Increasing Accessibility for Map Readers with Acquired and Inherited Color Vision Deficiencies: A Re-Coloring Algorithm for Maps

Approximately 8% of the male population suffer from an inherited form of color vision deficiency (CVD). Age, diabetes, macular degeneration, cataracts and glaucoma result in eye defects including an acquired form of CVD. Inherited CVD is marked by a difficulty in discerning red from green, while acquired CVD is marked by a difficulty in discerning blue from green. A recent review of the cartographic literature revealed a deficit in studies on accessible maps for readers with the acquired form of CVD. In addition, research on accessible maps for readers with the inherited form of CVD was restricted to the design or pre-publication stage. An approach is needed to render maps already in circulation accessible to an audience with CVD. The purpose of this research is to improve the accessibility of maps post-publication. Image re-coloring is a method of altering an image\u27s color composition in such a way as to make it accessible to a color vision deficient audience. An innovative algorithm is presented that produces a re-colored map that can be perceived by individuals with red-green (inherited) CVD, blue-green CVD (acquired) and normal color vision alike. The algorithm was tested on a control group of participants with normal color vision and a case group of participants with impaired color vision through a series of matching, content and personal preference questions about six pairs of maps. Each map pair represented one of the following color schemes: balance, diverging, qualitative area, qualitative dot, sequential polychrome, and two variable. Each map pair is composed of two renditions: a map using a color palette that is potentially confusing to viewers with impaired color vision (original rendition) and a map where the original color palette has been re-colored by the algorithm (re-colored rendition). According to the results of a Wilcoxon signed-rank test, the performance of the case group improved when using the re-colored renditions compared to when using the original renditions while the performance of the control group was the same for both renditions. A Mann-Whitney rank sum test revealed that while the scores of the case group were lower than the control group when using the original renditions, they were the same when using the re-colored renditions. A binomial test revealed that subjects in the case group displayed a preference towards all the re-colored renditions while subjects in the control group displayed a preference to two of the six original renditions

Contours and contrast

Contrast in photographic and computer-generated imagery communicates colour and lightness differences that would be perceived when viewing the represented scene. Due to depiction constraints, the amount of displayable contrast is limited, reducing the image's ability to accurately represent the scene. A local contrast enhancement technique called unsharp masking can overcome these constraints by adding high-frequency contours to an image that increase its apparent contrast. In three novel algorithms inspired by unsharp masking, specialized local contrast enhancements are shown to overcome constraints of a limited dynamic range, overcome an achromatic palette, and to improve the rendering of 3D shapes and scenes. The Beyond Tone Mapping approach restores original HDR contrast to its tone mapped LDR counterpart by adding highfrequency colour contours to the LDR image while preserving its luminance. Apparent Greyscale is a multi-scale two-step technique that first converts colour images and video to greyscale according to their chromatic lightness, then restores diminished colour contrast with high-frequency luminance contours. Finally, 3D Unsharp Masking performs scene coherent enhancement by introducing 3D high-frequency luminance contours to emphasize the details, shapes, tonal range and spatial organization of a 3D scene within the rendering pipeline. As a perceptual justification, it is argued that a local contrast enhancement made with unsharp masking is related to the Cornsweet illusion, and that this may explain its effect on apparent contrast.Seit vielen Jahren ist die realistische Erzeugung von virtuellen Charakteren ein zentraler Teil der Computergraphikforschung. Dennoch blieben bisher einige Probleme ungelöst. Dazu zählt unter anderem die Erzeugung von Charakteranimationen, welche unter der Benutzung der traditionellen, skelettbasierten Ansätze immer noch zeitaufwändig sind. Eine weitere Herausforderung stellt auch die passive Erfassung von Schauspielern in alltäglicher Kleidung dar. Darüber hinaus existieren im Gegensatz zu den zahlreichen skelettbasierten Ansätzen nur wenige Methoden zur Verarbeitung und Veränderung von Netzanimationen. In dieser Arbeit präsentieren wir Algorithmen zur Lösung jeder dieser Aufgaben. Unser erster Ansatz besteht aus zwei Netz-basierten Verfahren zur Vereinfachung von Charakteranimationen. Obwohl das kinematische Skelett beiseite gelegt wird, können beide Verfahren direkt in die traditionelle Pipeline integriert werden, wobei die Erstellung von Animationen mit wirklichkeitsgetreuen Körperverformungen ermöglicht wird. Im Anschluss präsentieren wir drei passive Aufnahmemethoden für Körperbewegung und Schauspiel, die ein deformierbares 3D-Modell zur Repräsentation der Szene benutzen. Diese Methoden können zur gemeinsamen Rekonstruktion von zeit- und raummässig kohärenter Geometrie, Bewegung und Oberflächentexturen benutzt werden, die auch zeitlich veränderlich sein dürfen. Aufnahmen von lockerer und alltäglicher Kleidung sind dabei problemlos möglich. Darüber hinaus ermöglichen die qualitativ hochwertigen Rekonstruktionen die realistische Darstellung von 3D Video-Sequenzen. Schließlich werden zwei neuartige Algorithmen zur Verarbeitung von Netz-Animationen beschrieben. Während der erste Algorithmus die vollautomatische Umwandlung von Netz-Animationen in skelettbasierte Animationen ermöglicht, erlaubt der zweite die automatische Konvertierung von Netz-Animationen in so genannte Animations-Collagen, einem neuen Kunst-Stil zur Animationsdarstellung. Die in dieser Dissertation beschriebenen Methoden können als Lösungen spezieller Probleme, aber auch als wichtige Bausteine größerer Anwendungen betrachtet werden. Zusammengenommen bilden sie ein leistungsfähiges System zur akkuraten Erfassung, zur Manipulation und zum realistischen Rendern von künstlerischen Aufführungen, dessen Fähigkeiten über diejenigen vieler verwandter Capture-Techniken hinausgehen. Auf diese Weise können wir die Bewegung, die im Zeitverlauf variierenden Details und die Textur-Informationen eines Schauspielers erfassen und sie in eine mit vollständiger Information versehene Charakter-Animation umwandeln, die unmittelbar weiterverwendet werden kann, sich aber auch zur realistischen Darstellung des Schauspielers aus beliebigen Blickrichtungen eignet