VLDB 2026 Research / reviewers in the wild / expert
Yang Liu 0030
dblp:51/3710-30
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2011
0000-0002-9164-421XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% | |
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
stereo vision |
0.1 | 1 | 2011 | Statistical Modeling of 3-D Natural Scenes With Application to Bayesian Stereopsis · IEEE Trans. Image Process. 2011 |
Image and video processing › image statistics › statistical image modeling
natural image statistics |
0.1 | 1 | 2011 | Statistical Modeling of 3-D Natural Scenes With Application to Bayesian Stereopsis · IEEE Trans. Image Process. 2011 |
Image and video processing › wavelet transform
wavelet coefficient modeling |
0.0 | 1 | 2011 | Statistical Modeling of 3-D Natural Scenes With Application to Bayesian Stereopsis · IEEE Trans. Image Process. 2011 |
Methods — techniques the papers use, named apart from their topics
wavelet transform · 0.2generalized gaussian model · 0.2bayesian inference · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Statistical Modeling of 3-D Natural Scenes With Application to Bayesian StereopsisabstractWe studied the empirical distributions of luminance, range and disparity wavelet coefficients using a coregistered database of luminance and range images. The marginal distributions of range and disparity are observed to have high peaks and heavy tails, similar to the well-known properties of luminance wavelet coefficients. However, we found that the kurtosis of range and disparity coefficients is significantly larger than that of luminance coefficients. We used generalized Gaussian models to fit the empirical marginal distributions. We found that the marginal distribution of luminance coefficients have a shape parameter p between 0.6 and 0.8, while range and disparity coefficients have much smaller parameters p < 0.32, corresponding to a much higher peak. We also examined the conditional distributions of luminance, range and disparity coefficients. The magnitudes of luminance and range (disparity) coefficients show a clear positive correlation, which means, at a location with larger luminance variation, there is a higher probability of a larger range (disparity) variation. We also used generalized Gaussians to model the conditional distributions of luminance and range (disparity) coefficients. The values of the two shape parameters (p,s) reflect the observed luminance-range (disparity) dependency. As an example of the usefulness of luminance statistics conditioned on range statistics, we modified a well-known Bayesian stereo ranging algorithm using our natural scene statistics models, which improved its performance. Yang Liu 0030, Lawrence K. Cormack, Alan C. Bovik |
IEEE Trans. Image Process. | 1 |
| 2010 | Natural scene statistics at stereo fixationsabstractWe conducted eye tracking experiments on naturalistic stereo images presented through a haploscope, and found that fixated luminance contrast and luminance gradient were generally higher than randomly selected luminance contrast and luminance gradient, which agrees with previous literatures. However we also found that the fixated disparity contrast and disparity gradient were generally lower than randomly selected disparity contrast and disparity gradient. We discuss the implications of this remarkable result. Yang Liu 0030, Lawrence K. Cormack, Alan C. Bovik |
ETRA | 1 |