The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Fragment-based image completion
Abstract
We present a new method for completing missing parts caused by the removal of foreground or background elements from an image. Our goal is to synthesize a complete, visually plausible and coherent image. The visible parts of the image serve as a training set to infer the unknown parts. Our method iteratively approximates the unknown regions and composites adaptive image fragments into the image. Values of an inverse matte are used to compute a confidence map and a level set that direct an incremental traversal within the unknown area from high to low confidence. In each step, guided by a fast smooth approximation, an image fragment is selected from the most similar and frequent examples. As the selected fragments are composited, their likelihood increases along with the mean confidence of the image, until reaching a complete image. We demonstrate our method by completion of photographs and paintings.
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Cites
Stochastic completion fields: A neural model of illusory contour shape and salience.
Williams, L. R., & Jacobs, D. W. (1997) · Neural Computation · doi:10.1162/neco.1997.9.4.837 · Cited by 331
Cited by
Region Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258
Metadata
- Authors
- Drori, I., Cohen-Or, D., & Yeshurun, H.
- Venue
- ACM SIGGRAPH 2003 Papers
- Publisher
- ACM
- Published
- 2003-07
- Pages
- 303-312
- OpenAlex
- W2016331656
- Cited by
- 492
- References
- 1
- FWCI
- 17.03
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Drori, I., Cohen-Or, D., & Yeshurun, H. (2003). Fragment-based image completion. ACM SIGGRAPH 2003 Papers. https://doi.org/10.1145/1201775.882267
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
