The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Colorization using optimization
Abstract
Colorization is a computer-assisted process of adding color to a monochrome image or movie. The process typically involves segmenting images into regions and tracking these regions across image sequences. Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious, time-consuming, and expensive task.In this paper we present a simple colorization method that requires neither precise image segmentation, nor accurate region tracking. Our method is based on a simple premise; neighboring pixels in space-time that have similar intensities should have similar colors. We formalize this premise using a quadratic cost function and obtain an optimization problem that can be solved efficiently using standard techniques. In our approach an artist only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence. We demonstrate that high quality colorizations of stills and movie clips may be obtained from a relatively modest amount of user input.
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Cites
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,119
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Transferring color to greyscale images
Welsh, T. F., Ashikhmin, M., & Mueller, K. D. (2002) · ACM Transactions on Graphics · doi:10.1145/566654.566576 · Cited by 537
Color image enhancement via chromaticity diffusion
Tang, B., Sapiro, G., & Caselles, V. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.918563 · Cited by 198
Cited by
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
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
Random Walks for Image Segmentation
Grady, L. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.233 · Cited by 2,624
A Closed-Form Solution to Natural Image Matting
Levin, A., Lischinski, D., & Weiss, Y. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1177 · Cited by 1,616
Self-citationA Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting
Bai, X., & Sapiro, G. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408931 · Cited by 438
Metadata
- Authors
- Levin, A., Lischinski, D., & Weiss, Y.
- Venue
- ACM SIGGRAPH 2004 Papers
- Publisher
- ACM
- Published
- 2004-08
- Pages
- 689-694
- OpenAlex
- W4246399762
- Cited by
- 478
- References
- 4
- FWCI
- 8.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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