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https://hdl.handle.net/2440/129516
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Type: | Journal article |
Title: | Evolutionary Image Transition and Painting Using Random Walks |
Author: | Neumann, A. Alexander, B. Neumann, F. |
Citation: | Evolutionary Computation, 2020; 28(4):643-675 |
Publisher: | Massachusetts Institute of Technology Press (MIT Press) |
Issue Date: | 2020 |
ISSN: | 1063-6560 1530-9304 |
Statement of Responsibility: | Aneta Neumann, Bradley Alexander and Frank Neumann |
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. Afterwards, we investigate how modifications of our biased random walk approaches can be used for evolutionary image painting. We introduce an evolutionary image painting approach whose underlying biased random walk can be controlled by a parameter influencing the bias of the random walk and thereby creating different artistic painting effects. |
Keywords: | Humans Random Allocation Genetic Phenomena Algorithms Art Paintings Image Processing, Computer-Assisted Bias |
Rights: | © 2020 Massachusetts Institute of Technology |
DOI: | 10.1162/evco_a_00270 |
Published version: | https://doi.org/10.1162/evco_a_00270 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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