Abstract
In this paper, we bring the novel idea to automatically combine Non-Photorealistic Rendering (NPR) effects with real-world images based on saliency detection. Noticing that the key idea of NPR is to focus on enabling a wide variety of expressive styles for digital visual art (e.g. painting, sketch, and cartoon), such mixture of Reality and NRP effects always provides an extremely intriguing sense of art beyond the original content. Technically, given an input image, we devote a fast approach to convert it into manga or pencil sketch on-the-fly. Moreover, guided by a hierarchical saliency detection strategy, the mixture of NPR effects and Reality can be finished in the most effective way. On the other hand, the proposed ‘RealMeetsArt’ system also provides the function to let user manually select interested foreground regions. User can easily select a fine-grained area with only several stroke drawings.
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Wu, Z., Aizawa, K. (2013). Saliency Detection-Based Mixture of Reality and Non-Photorealistic Rendering Effects for Artistic Visualization. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_63
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DOI: https://doi.org/10.1007/978-3-319-03731-8_63
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03730-1
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