Computational Ecosystems in Evolutionary Art, and Their Potential for the Future of Virtual Worlds

Antunes, Rui Filipe; Leymarie, Frederic Fol; Latham, William

doi:10.1007/978-3-319-22041-3_16

Cited by 1 publication

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References 31 publications

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“…There are a couple of issues that will be considered in the future research: (1) As mentioned by [ 4 ], introducing other fitness functions to explore the new visual effect of evolved images, especially for more pleasuring texture, is currently under investigation and will be reported later; (2) More alternative evolutionary algorithms based on cloud model may be provided just as or more interesting than result images; thus the extension of the proposed method to improve the performance is well worth further studying on, for example, introducing computational ecosystems [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Image-Guided Rendering with an Evolutionary Algorithm Based on Cloud Model

2018

Computational Intelligence and Neuroscience

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The process of creating nonphotorealistic rendering images and animations can be enjoyable if a useful method is involved. We use an evolutionary algorithm to generate painterly styles of images. Given an input image as the reference target, a cloud model-based evolutionary algorithm that will rerender the target image with nonphotorealistic effects is evolved. The resulting animations have an interesting characteristic in which the target slowly emerges from a set of strokes. A number of experiments are performed, as well as visual comparisons, quantitative comparisons, and user studies. The average scores in normalized feature similarity of standard pixel-wise peak signal-to-noise ratio, mean structural similarity, feature similarity, and gradient similarity based metric are 0.486, 0.628, 0.579, and 0.640, respectively. The average scores in normalized aesthetic measures of Benford's law, fractal dimension, global contrast factor, and Shannon's entropy are 0.630, 0.397, 0.418, and 0.708, respectively. Compared with those of similar method, the average score of the proposed method, except peak signal-to-noise ratio, is higher by approximately 10%. The results suggest that the proposed method can generate appealing images and animations with different styles by choosing different strokes, and it would inspire graphic designers who may be interested in computer-based evolutionary art.

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Section: Discussionmentioning

confidence: 99%

Image-Guided Rendering with an Evolutionary Algorithm Based on Cloud Model

2018

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Computational Ecosystems in Evolutionary Art, and Their Potential for the Future of Virtual Worlds

Cited by 1 publication

References 31 publications

Image-Guided Rendering with an Evolutionary Algorithm Based on Cloud Model

Image-Guided Rendering with an Evolutionary Algorithm Based on Cloud Model

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