Animating Typescript Using Aesthetically Evolved Images

Mills, Ashley

doi:10.1007/978-3-319-31008-4_9

Cited by 3 publications

(1 citation statement)

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“…Evolutionary design is an area in which artificial evolution is applied to problems in engineering, design, and the fine arts. One active research topic is the generation of images using evolutionary algorithms [ 1 – 7 ]. A specialized application of evolutionary design is the evolution of nonphotorealistic rendering (NPR) effects [ 8 ].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%