A GENETIC APROACH FOR AUTOMATED IMAGE GENERATION: GRAYSCALE IMAGE GENERATION
Main Article Content
Abstract
Non photorealistic rendering is a new research field in the areas of computer graphics. The goal is to give a more natural feel to computer generated images, by simulating various artistic techniques and to give the sense of an image without reproducing it. In this paper, we present a new evolutionary approach to non-photorealistic rendering of 2D black/white and grayscale images. The goal is to generate a painting that is close to a given input image. This problem can be formalized as a high-dimensional optimization problem, with local minima. We have developed a genetic algorithm that modifies the traditional uniform crossover to spread out vital genes at the expense of lethal genes rather than exchanging them between matting parents. A vital or lethal gene can be determined via a threshold field associated with each pixel gene that indicates the distance between a chromosome gene and the corresponding input image pixel. The proposed evolutionary painting framework demonstrates good results and achieves reasonable convergence.
Article Details
Section
How to Cite
References
P Haeberli. (1990), Painting by Numbers: Abstract Image Representations, SIGGRAPH 90 Conference Proceedings, Annual Conference Scries, PP.207-214. ACM SIGGRAPH, ACM press, Angust.
P.Litwinowicz. (1997). Processing Images and Video for An Impressionist Effect", SIGGRAPH 97 Conference Proceedings, Annual Conference Series, PP 407-414. ACM SIGGRAPH, ACM Press. August
P.Salishury. P. Michael etal. (1997). Orientable Textures for Image-Based pen-and-Ink Illustration, SIGGRAPH 97 Conference proceedings, Annual Conference series, PP. 401-405. ACM SIGGraph.ACM press, August.
N. Scapel, Genetic Painter, available at research report, Computer Graphics Lab, Department of Computer Sience, Stanford University. D.E. Goldberg. (1989), Genetic Algorithms in Search, Optimization, and Machine learning Reading, MA:Addison-Wesley,
M.Mitchell, (1998), An Introduction to Genetic Algorithms", MIT Press,
Z Michalewicz, (1999), Genetic Algorithms Data structures-Evolution Programs, Springer
D.L. Ruderman, (1998), Statistics of cone responses to natural images implementations for visual coding 1.Opt Sac.Am.A/Vol.15,No 8/August pp.2036-2045,
T.Tasdizen,L.Akarun, and C.Ersoy, (1998), Color Quantization with Ggenetic Algorithms, Signal Processing: Image Communication 12,pp.49-57. Li-Yi Wei. (2001), Texture Synthesis By Fixed Neighborhood Searching, Ph.D.dissertation, Department of Electrical Engineering and the committee on graduate studies, Standford University
TWelsh, M.Ashikhmin. and K.Muller. Transferring color to Grayscal Images, available at
hipww.us.sumvsh.edu-twelsh/ colorize.