Holger Winnemöller
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.
Supplemental Material
- Arad, N., and Gotsman, C. 1999. Enhancement by image-dependent warping. IEEE Trans. on Image Processing 8, 9, 1063--1074. Google Scholar
Digital Library
- Barash, D., and Comaniciu, D. 2004. A common framework for non-linear diffusion, adaptive smoothing, bilateral filtering and mean shift. Image and Video Computing 22, 1, 73--81.Google ScholarCross Ref
- Boomgaard, R. V. D., and de Weijer, J. V. 2002. On the equivalence of local-mode finding, robust estimation and mean-shift analysis as used in early vision tasks. 16th Internat. Conf. on Pattern Recog. 3, 927--390. Google ScholarDigital Library
- Canny, J. F. 1986. A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8, 769--798. Google ScholarDigital Library
- Collomosse, J. P., Rowntree, D., and Hall, P. M. 2005. Stroke surfaces: Temporally coherent artistic animations from video. IEEE Trans. on Visualization and Computer Graphics 11, 5, 540--549. Google ScholarDigital Library
- DeCarlo, D., and Santella, A. 2002. Stylization and abstraction of photographs. ACM Trans. Graph. 21, 3, 769--776. Google ScholarDigital Library
- Elder, J. H. 1999. Are edges incomplete? Internat. Journal of Computer Vision 34, 2-3, 97--122. Google ScholarDigital Library
- Fischer, J., Bartz, D., and Strasser, W. 2005. Stylized Augmented Reality for Improved Immersion. In Proc. of IEEE VR, 195--202. Google ScholarDigital Library
- Gooch, B., Reinhard, E., and Gooch, A. 2004. Human facial illustrations: Creation and psychophysical evaluation. ACM Trans. Graph. 23, 1, 27--44. Google ScholarDigital Library
- Hertzmann, A. 2001. Paint by relaxation. In CGI '01:Computer Graphics Internat. 2001, 47--54. Google ScholarDigital Library
- Itti, L., and Koch, C. 2001. Computational modeling of visual attention. Nature Reviews Neuroscience 2, 3, 194--203.Google ScholarCross Ref
- Loviscach, J. 1999. Scharfzeichner: Klare bilddetails durch verformung. Computer Technik 22, 236ff.Google Scholar
- Marr, D., and Hildreth, E. C. 1980. Theory of edge detection. Proc. Royal Soc. London, Bio. Sci. 207, 187--217.Google ScholarCross Ref
- Palmer, S. E. 1999. Vision Science: Photons to Phenomenology. The MIT Press.Google Scholar
- Perona, P., and Malik, J. 1991. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 12, 7. Google ScholarDigital Library
- Pham, T. Q., and Vliet, L. J. V. 2005. Separable bilateral filtering for fast video preprocessing. In IEEE Internat. Conf. on Multimedia & Expo, CD1-4.Google Scholar
- Privitera, C. M., and Stark, L. W. 2000. Algorithms for defining visual regions-of-interest: Comparison with eye fixations. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 9, 970--982. Google ScholarDigital Library
- Raskar, R., Tan, K.-H., Feris, R., Yu, J., and Turk, M. 2004. Non-photorealistic camera: depth edge detection and stylized rendering using multi-flash imaging. ACM Trans. Graph. 23, 3, 679--688. Google ScholarDigital Library
- Saito, T., and Takahashi, T. 1990. Comprehensible rendering of 3-D shapes. In Proc. of ACM SIGGRAPH 90, 197--206. Google ScholarDigital Library
- Santella, A., and DeCarlo, D. 2004. Visual interest and NPR: an evaluation and manifesto. In Proc. of NPAR '04, 71--78. Google ScholarDigital Library
- Stevenage, S. V. 1995. Can caricatures really produce distinctiveness effects? British Journal of Psychology 86, 127--146.Google ScholarCross Ref
- Tomasi, C., and Manduchi, R. 1998. Bilateral filtering for gray and color images. In Proceedings of ICCV '98, 839. Google ScholarDigital Library
- Wang, J., Xu, Y., Shum, H.-Y., and Cohen, M. F. 2004. Video tooning. ACM Trans. Graph. 23, 3, 574--583. Google ScholarDigital Library
- Winkenbach, G., and Salesin, D. H. 1994. Computer-generated pen-and-ink illustration. In Proc. of ACM SIGGRAPH 94, 91--100. Google ScholarDigital Library
- Wyszecki, G., and Styles, W. 1982. Color Science: Concepts and Methods, Quantitative Data and Formulae. Wiley, New York, NY.Google Scholar
Index Terms
- Real-time video abstraction
Recommendations
Real-time video abstraction
SIGGRAPH '06: ACM SIGGRAPH 2006 PapersWe 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 ...
Real-time saliency-aware video abstraction
Existing real-time automatic video abstraction systems rely on local contrast only for identifying perceptually important information and abstract imagery by reducing contrast in low-contrast regions while artificially increasing contrast in higher ...
Active lighting for video conferencing
In consumer video conferencing, lighting conditions are usually not ideal thus the image qualities are poor. Lighting affects image quality on two aspects: brightness and skin tone. While there has been much research on improving the brightness of the ...
Comments