3D collage: expressive non-realistic modeling
Ran Gal, Olga Sorkine, Tiberiu Popa, Alla Sheffer, Daniel Cohen-Or.
NPAR '07: Proceedings of the 5th international symposium on Non-photorealistic animation and rendering, pp. 7--14, New York, NY, USA, ACM,
2007. [BibTeX]
Action Synopsis: Pose Selection and Illustration
Jackie Assa, Yaron Caspi, Daniel Cohen-Or.
SIGGRAPH '05, Los Angeles, California, USA,
2005. [BibTeX]
Colorization by Example
Author(s): Revital Irony, Daniel Cohen-Or, Dani Lischinski.
Proceedings: Proceedings of Eurographics Symposium on Rendering (EGSR'05), pp. 201--210, Konstanz, Germany, June 29 - July 1,
2005.
[BibTeX]
Abstract:
We present a new method for colorizing grayscale images by transferring color from a segmented example image. Rather than relying on a series of independent pixel-level decisions, we develop a new strategy that attempts to account for the higher-level context of each pixel. The colorizations generated by our approach exhibit a much higher degree of spatial consistency, compared to previous automatic color transfer methods [WAM02]. We also demonstrate that our method requires considerably less manual effort than previous user-assisted colorization methods [LLW04]. Given a grayscale image to colorize, we first determine for each pixel which example segment it should learn its color from. This is done automatically using a robust supervised classification scheme that analyzes the low-level feature space defined by small neighborhoods of pixels in the example image. Next, each pixel is assigned a color from the appropriate region using a neighborhood matching metric, combined with spatial filtering for improved spatial coherence. Each color assignment is associated with a confidence value, and pixels with a sufficiently high confidence level are provided as "micro-scribbles" to the optimization-based colorization algorithm of Levin et al. [LLW04], which produces the final complete colorization of the image.
Example-based Style Synthesis
Iddo Drori, Daniel Cohen-Or, Hezy Yeshurun.
Computer Vision and Pattern Recognition (CVPR '03), Vol. 2, pp. 143--150, 18-20 June,
2004. [BibTeX]