The DOI matches its Crossref record.
Representing moving images with layers
Abstract
We describe a system for representing moving images with sets of overlapping layers. Each layer contains an intensity map that defines the additive values of each pixel, along with an alpha map that serves as a mask indicating the transparency. The layers are ordered in depth and they occlude each other in accord with the rules of compositing. Velocity maps define how the layers are to be warped over time. The layered representation is more flexible than standard image transforms and can capture many important properties of natural image sequences. We describe some methods for decomposing image sequences into layers using motion analysis, and we discuss how the representation may be used for image coding and other applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Prediction error as a quality metric for motion and stereo
Szeliski, R. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790301 · Cited by 118
Recovering articulated object models from 3D range data
(2012) · arXiv (Cornell University) · doi:10.5555/1036843.1036846 · Cited by 79
3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers
Shin, D., Ren, Z., Sudderth, E., & Fowlkes, C. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00226 · Cited by 57
Stereo Magnification: Learning View Synthesis using Multiplane Images
Zhou, T., Tucker, R., Flynn, J., Fyffe, G., & Snavely, N. (2018) · arXiv · doi:10.48550/arxiv.1805.09817 · Cited by 150
Metadata
- Authors
- Wang, J. Y. A., & Adelson, E. H.
- Venue
- IEEE Transactions on Image Processing
- Published
- 1994
- Volume
- 3
- Issue
- 5
- Pages
- 625-638
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2142912032
- Cited by
- 1,090
- References
- 0
- FWCI
- 56.41
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.