Pixelated image abstraction (original) (raw)
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ACM Transactions on Applied Perception, 2005
We describe a new computational approach to stylize the colors of an image by using a reference image. During processing, we take the characteristics of human color perception into account to generate more appealing results. Our system starts by classifying each pixel value into one of the basic color categories, derived from our psychophysical experiments. The basic color categories are perceptual categories that are universal to everyone, regardless of nationality or cultural background. These categories are used to provide restrictions on color transformations to avoid generating unnatural results. Our system then renders a new image by transferring colors from a reference image to the input image, based on these categorizations. To avoid artifacts due to the explicit clustering, our system defines fuzzy categorization when pseudocontours appear in the resulting image. We present a variety of results and show that our method performs a large, yet natural, color transformation wit...
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Image Abstraction through Overlapping Region Growth
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We propose a region-based abstraction of a photograph, where the image plane is covered by overlapping irregularly shaped regions that approximate the image content. We segment regions using a novel region growth algorithm intended to produce highly irregular regions that still respect image edges, different from conventional segmentation methods that encourage compact regions. The final result has reduced detail, befitting abstraction, but still contains some small structures such as highlights; thin features and crooked boundaries are retained, while interior details are softened, yielding a painting-like abstraction effect.
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Texture-preserving abstraction
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Image abstraction traditionally eliminates texture, but doing so ig-nores the more elegant alternative of texture indication, e.g., sug-gesting the presence of texture through irregular silhouettes and lo-cally chosen details. We propose a variant of geodesic image filter-ing which preserves the locally strongest edges, leading to preser-vation of both strong edges and weak edges depending on the sur-rounding context. Our contribution is to introduce cumulative range geodesic filter-ing, where the distance in the image plane is lengthened propor-tional to the color distance. We apply the new filtering scheme to abstraction applications and demonstrate that it has powerful structure-preserving capabilities, especially regarding preservation and indication of textures.
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ACM Transactions on Graphics, 2008
Example-based texture synthesis algorithms have gained widespread popularity for their ability to take a single input image and create a perceptually similar non-periodic texture. However, previous methods rely on single input exemplars that can capture only a limited band of spatial scales. For example, synthesizing a continent-like appearance at a variety of zoom levels would require an impractically high input resolution. In this paper, we develop a multiscale texture synthesis algorithm. We propose a novel example-based representation, which we call an exemplar graph, that simply requires a few low-resolution input exemplars at different scales. Moreover, by allowing loops in the graph, we can create infinite zooms and infinitely detailed textures that are impossible with current example-based methods. We also introduce a technique that ameliorates inconsistencies in the user's input, and show that the application of this method yields improved interscale coherence and highe...
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