×

Image-based painterly rendering by evolutionary algorithm. (English) Zbl 1111.68736

Summary: This paper presents an effective method based on genetic algorithm for optimizing the rendering quality in image-based painterly rendering. Based on a multi-level evolutionary approach, the proposed method produces, for a variety of input images, results that are better in a statistically significant way than previous methods.

MSC:

68U10 Computing methodologies for image processing
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Goldberg D. E., Genetic Algorithms in Search, Optimization, and Machine Learning (1989) · Zbl 0721.68056
[2] DOI: 10.1007/978-3-662-03315-9 · doi:10.1007/978-3-662-03315-9
[3] Strothotte T., Non-Photorealistic Computer Graphics: Modeling, Rendering and Animation (2002)
[4] DOI: 10.1109/MCG.2003.1210867 · Zbl 05086162 · doi:10.1109/MCG.2003.1210867
[5] Hertzmann A., Computer Graphics International pp 47–
[6] DOI: 10.1142/S0218213006002813 · Zbl 05421392 · doi:10.1142/S0218213006002813
[7] Canny J., IEEE Transactions on Pattern Analysis and Machine Intelligence 8 pp 679–
[8] Xu C., IEEE Transactions on Image Processing 7 pp 359–
[9] DOI: 10.1162/evco.1996.4.2.133 · Zbl 05412746 · doi:10.1162/evco.1996.4.2.133
[10] Miller I., Probability and Statistics for Engineers (1965)
[11] DOI: 10.1162/1054746043280556 · Zbl 05438918 · doi:10.1162/1054746043280556
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.