Abstract
We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random walks and study how their combination with a baseline mutation operator can lead to interesting image transition processes in terms of visual effects and artistic features. Using feature-based analysis we investigate the evolutionary image transition behaviour with respect to different features and evaluate the images constructed during the image transition process.
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Notes
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Images and videos are available at https://vimeo.com/anetaneumann.
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This work has been supported through Australian Research Council (ARC) grant DP140103400.
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Neumann, A., Alexander, B., Neumann, F. (2017). Evolutionary Image Transition Using Random Walks. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_16
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DOI: https://doi.org/10.1007/978-3-319-55750-2_16
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