Random paintbrush transformation (original) (raw)
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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
EURASIP Journal on Image and Video Processing, 2014
The automatic extraction of objective features from paintings, like brushstroke distribution, orientation, and shape, could be particularly useful for different artwork analyses and management tasks. In fact, these features contribute to provide a unique signature of the artists’ style and can be effectively used for artist identification and classification, artwork examination and retrieval, etc. In this paper, an automatic technique for unsupervised extraction of individual brushstrokes from digital reproductions of van Gogh’s paintings is presented. Through the iterative application of segmentation, characterization, and validation steps, valid brushstrokes complying with specific area and shape constraints are identified. On the extracted brushstrokes, several representative features can then be calculated, like orientation, length, and width. The accuracy of the devised method is evaluated by comparing numerical results obtained on a dataset of digital reproductions of van Gogh...
Painterly Rendering by Using K-mean Segmentation
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.