Painterly Rendering by Using K-mean Segmentation (original) (raw)

Iraqi Journal of Science

The problem statement discussed in this paper is a new technique for the presentation of painterly rendering that uses a K-mean segmentation to divide the input image into a set of regions (depending on the grayscale of the regions). Segmenting the input image helps users use different brush strokes and easily change the strokes' shape, size, or orientation for different regions. Every region is painted using different brush kinds. The properties of the brush strokes are chosen depending on the region's details. The brush stroke properties, such as size, color, shape, location, and orientation, are extracted from the source image using statistical tools. The number of regions is set up manually and depends on the input image. This method allows the user to apply different painting styles to different regions and create a painterly rendering. MATLAB is used to render the images into paintings.

Automatic Painting with Economized Strokes

2012

We present a method that takes a raster image as input and produces a painting-like image composed of strokes rather than pixels. Unlike previous automatic painting methods, we attempt to use very few brush-strokes. This is accomplished by first segmenting the image into features, finding the medial axes points of these features, converting the medial axes points into ordered lists of image tokens, and finally rendering these lists as brush strokes. Our process creates images reminiscent of modern realist painters who often want an abstract or sketchy quality in their work. CR Categories: I.3.7 [Computing Methodologies ]: Computer Graphics-2D Graphics

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