Computational Intelligence and Neuroscience Volume 2018, Article ID 4518265.
ISSN/ISBN: Not available at this time. DOI: 10.1155/2018/4518265
Abstract: 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 e ects is evolved. e 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. e 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. e 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%. e results suggest that the proposed method can generate appealing images and animations with di erent styles by choosing di erent strokes, and it would inspire graphic designers who may be interested in computer-based evolutionary art.
Bibtex:
@article {,
AUTHOR = {Tao Wu},
TITLE = {Image-Guided Rendering with an Evolutionary Algorithm Based on Cloud Model},
JOURNAL = {Computational Intelligence and Neuroscience},
YEAR = {2018},
VOLUME = {2018},
NUMBER = {},
PAGES = {},
DOI = {10.1155/2018/4518265},
URL = {https://www.hindawi.com/journals/cin/2018/4518265/},
}
Reference Type: Journal Article
Subject Area(s): Image Processing