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  Automatic Image Colorization Via Multimodal Predictions

Charpiat, G., Hofmann, M., & Schölkopf, B. (2008). Automatic Image Colorization Via Multimodal Predictions. In A. Forsyth, P. Torr, & A. Zisserman (Eds.), Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008 (pp. 126-139). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C6BF-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-37D7-D
Genre: Conference Paper

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 Creators:
Charpiat, G1, 2, Author              
Hofmann, M1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We aim to color automatically greyscale images, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic colorization is nontrivial since there is usually no one-to-one correspondence between color and local texture. The contribution of our framework is that we deal directly with multimodality and estimate, for each pixel of the image to be colored, the probability distribution of all possible colors, instead of choosing the most probable color at the local level. We also predict the expected variation of color at each pixel, thus defining a nonuniform spatial coherency criterion. We then use graph cuts to maximize the probability of the whole colored image at the global level. We work in the L-a-b color space in order to approximate the human perception of distances between colors, and we use machine learning tools to extract as much information as possible from a dataset of colored examples. The resulting algorithm is fast, designed to be more robust to texture noise, and is above all able to deal with ambiguity, in contrary to previous approaches.

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 Dates: 2008-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1007/978-3-540-88690-7_10
BibTex Citekey: 5300
 Degree: -

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Title: 10th European Conference on Computer Vision (ECCV 2008)
Place of Event: Marseille, France
Start-/End Date: 2008-10-12 - 2008-10-18

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Title: Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008
Source Genre: Proceedings
 Creator(s):
Forsyth, AD, Editor
Torr, PHS, Editor
Zisserman, A, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 126 - 139 Identifier: ISBN: 978-3-540-88689-1

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 5304 Sequence Number: - Start / End Page: - Identifier: -