A GENETIC APROACH FOR AUTOMATED IMAGE GENERATION: GRAYSCALE IMAGE GENERATION
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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.
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