The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Real-time video abstraction
Abstract
We present an automatic, real-time video and image abstraction framework that abstracts imagery by modifying the contrast of visually important features, namely luminance and color opponency. We reduce contrast in low-contrast regions using an approximation to anisotropic diffusion, and artificially increase contrast in higher contrast regions with difference-of-Gaussian edges. The abstraction step is extensible and allows for artistic or data-driven control. Abstracted images can optionally be stylized using soft color quantization to create cartoon-like effects with good temporal coherence. Our framework design is highly parallel, allowing for a GPU-based, real-time implementation. We evaluate the effectiveness of our abstraction framework with a user-study and find that participants are faster at naming abstracted faces of known persons compared to photographs. Participants are also better at remembering abstracted images of arbitrary scenes in a memory task.
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Cites
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Cited by
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
Image smoothing via L0gradient minimization
Xu, L., Lu, C., Xu, Y., & Jia, J. (2011) · Proceedings of the 2011 SIGGRAPH Asia Conference · doi:10.1145/2024156.2024208 · Cited by 873
Ge, Q., Ruan, F., Qiao, B., Zhang, Q., Zuo, X., & Dang, L. (2021) · Electronics · doi:10.3390/electronics10151823 · Cited by 50
Metadata
- Authors
- Winnemöller, H., Olsen, S. C., & Gooch, B.
- Venue
- ACM Transactions on Graphics
- Published
- 2006-07
- Volume
- 25
- Issue
- 3
- Pages
- 1221-1226
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2146578165
- Cited by
- 533
- References
- 21
- FWCI
- 30.19
- Type
- journal article
- Language
- en
- Open access
- closed
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