Abstract
Painting is done according to the artist's style. The most representative of the style is the texture and shape of the brush stroke. Computer simulations allow the artist's painting to be produced by taking this stroke and pasting it onto the image. This is called stroke-based rendering. The quality of the result depends on the number or quality of this stroke, since the stroke is taken to create the image. It is not easy to render using a large amount of information, as there is a limit to having a stroke scanned. In this work, we intend to produce rendering results using mass data that produces large amounts of strokes by expanding existing strokes through warping. Through this, we have produced results that have higher quality than conventional studies. Finally, we also compare the correlation between the amount of data and the results.
Highlights
Non-realistic rendering is a technique that simulates the techniques expressed by artists, as opposed to photorealistic rendering
Real artists can produce results using a variety of brushes at times like this, but in the case of conventional brush stroke rendering, they only work with simple changes such as rotation magnification in their database, resulting in poor quality compared to real works
The method of generating a new stroke proposed in this paper is faster, simpler, and can be generated with texture preserved than the method of obtaining a stroke by drawing it directly
Summary
Non-realistic rendering is a technique that simulates the techniques expressed by artists, as opposed to photorealistic rendering. Traditional stroke-based rendering techniques represent images using fewer DBs, leading to object distortion. It should be applicable to databases with low diversity To this end, we applied image-warping technology. Image warping applied to generation methods is the task of deformation, processing, and reproduction of input images, which can be deformed and extended in multiple directions. That is, it is the same as drawing on elastic materials, moving certain parts of the material, and fixing them. We propose an improved brush stroke judgment algorithm to minimize the omission or distortion of objects in the input image with the brush stroke database we hold. We summarize our method and conclude our results and future development
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