Color scheme adjustment by fuzzy constraint satisfaction for color vision deficiencies

Yanagida, Takuto; Okajima, Katsunori; Mimura, Hidenori

doi:10.1002/col.21913

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…However, the efficiency of the technique depends on the size of problems, i.e., the numbers of classes and responsibilities. Generally, it is hard to obtain a solution from an FCSP whose domain is large [5]. How the efficiency of our technique differs according to the size of problems should be clarified in the future.…”

Section: Discussionmentioning

confidence: 99%

Class Responsibility Assignment as Fuzzy Constraint Satisfaction

Hayashi

Yanagida

Saeki

et al. 2014

2014 6th International Workshop on Empirical Software Engineering in Practice

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We formulate the class responsibility assignment (CRA) problem as the fuzzy constraint satisfaction problem (FCSP) for automating CRA of high quality. Responsibilities are contracts or obligations of objects that they should assume; by aligning them to classes appropriately, quality designs realize. Typical conditions of a desirable design are having a low coupling between highly cohesive classes. However, because of a trade-off among such conditions, solutions that satisfy the conditions moderately are desired, and computer assistance is needed. Additionally, if we have an initial assignment, the improved one by our technique should keep the original assignment as much as possible because it involves with the intention of human designers. We represent such conditions as fuzzy constraints, and formulate CRA as FCSP. That enables us to apply common FCSP solvers to the problem and to derive solution representing a CRA. The conducted preliminary evaluation indicates the effectiveness of our technique.

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Section: Discussionmentioning

confidence: 99%

Class Responsibility Assignment as Fuzzy Constraint Satisfaction

Hayashi

Yanagida

Saeki

et al. 2014

2014 6th International Workshop on Empirical Software Engineering in Practice

Self Cite

View full text Add to dashboard Cite

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“…Earlier work with a similar goalbut published prior to the development of perceptually-uniform color spaces-is that of Kelly [1965]. Both Troiano et al [2008] and Yanagida et al [2014] presented algorithms for modifying existing color cycles to improve their accessibility to color-vision-deficient individuals.…”

Section: Related Workmentioning

confidence: 99%

Accessible Color Sequences for Data Visualization

Petroff¹

2021

Preprint

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Fig. 1. Final color cycles derived in the present work. From left to right, the cycles containing six, eight, and ten colors are visualized as lines, ordered from top to bottom, and scatter plot points, ordered from left to right. The corresponding numeric color values are presented in Table 2.Color cycles, ordered sets of colors for data visualization, that balance aesthetics with accessibility considerations are presented. In order to model aesthetic preference, data were collected with an online survey, and the results were used to train a machine-learning model. To ensure accessibility, this model was combined with minimum-perceptual-distance constraints, including for simulated color-vision deficiencies, as well as with minimumlightness-distance constraints for grayscale printing, maximum-lightness constraints for maintaining contrast with a white background, and scores from a color-saliency model for ease of use of the colors in verbal and written descriptions. Optimal color cycles containing six, eight, and ten colors were generated using the data-driven aesthetic-preference model and accessibility constraints. Due to the balance of aesthetics and accessibility considerations, the resulting color cycles can serve as reasonable defaults in data-plotting codes.CCS Concepts: • Human-centered computing → Empirical studies in visualization; Accessibility design and evaluation methods; • Computing methodologies → Discrete space search.

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“…Such problems are usually over-constrained, and enable the use of optimization techniques such as genetic algorithms [Ichikawa et al 2003;Troiano et al 2008] and probabilistic exploration [Zhou et al 2014]. More recently, Yanagida et al [2015] present an exploration method based on Fuzzy Constraint Satisfaction Problem (F-CSP). In contrast to the CSP used in map design, F-CSP can partially satisfy the input constraints while providing an associated satisfaction degree.…”

Section: Computational Pale E Explorationmentioning

confidence: 99%

“…Even though palettes are not generated interactively, such systems enable the modeling of complex constraints, e.g. simulating color vision deciencies [Yanagida et al 2015] or decreasing energy consumption of display devices [Chuang et al 2009]. In the last strategy, probabilistic constraints are learned in pre-process from sets of segmented images and their palettes [Lin et al 2013;Ritchie et al 2015].…”

Section: Introductionmentioning

confidence: 99%

Constrained palette-space exploration

et al. 2017

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Color palettes are widely used by artists to define colors of artworks and explore color designs. In general, artists select the colors of a palette by following a set of rules, e.g. contrast or relative luminance. Existing interactive palette exploration tools explore palette spaces following limited constraints defined as geometric configurations in color space e.g. harmony rules on the color wheel. Palette search algorithms sample palettes from color relations learned from an input dataset, however they cannot provide interactive user edits and palette refinement. We introduce in this work a new versatile formulation enabling the creation of constraint-based interactive palette exploration systems. Our technical contribution is a graph-based palette representation, from which we define palette exploration as a minimization problem that can be solved efficiently and provide real-time feedback. Based on our formulation, we introduce two interactive palette exploration strategies: constrained palette exploration, and for the first time, constrained palette interpolation. We demonstrate the performances of our approach on various application cases and evaluate how it helps users finding trade-offs between concurrent constraints.

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Color scheme adjustment by fuzzy constraint satisfaction for color vision deficiencies

Cited by 8 publications

References 38 publications

Class Responsibility Assignment as Fuzzy Constraint Satisfaction

Class Responsibility Assignment as Fuzzy Constraint Satisfaction

Accessible Color Sequences for Data Visualization

Constrained palette-space exploration

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