Demo Data from VLDB 2026 / reviewers in the wild / expert
Henry Stone
dblp:235/5203
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
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 |
Computational photography and imaging · 50% Image and video processing · 50% | |
| Artificial intelligence
1 paper |
Segmentation and scene understanding · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational photography and imaging › light field imaging
light field processing |
0.4 | 1 | 2019 | View-Consistent 4D Light Field Superpixel Segmentation · ICCV 2019 |
Image and video processing › image segmentation
superpixel segmentation |
0.4 | 1 | 2019 | View-Consistent 4D Light Field Superpixel Segmentation · ICCV 2019 |
Computer vision › Segmentation and scene understanding › object segmentation
occlusion-aware segmentation |
0.1 | 1 | 2019 | View-Consistent 4D Light Field Superpixel Segmentation · ICCV 2019 |
Methods — techniques the papers use, named apart from their topics
epipolar plane image analysis · 0.8clustering · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | View-Consistent 4D Light Field Superpixel SegmentationabstractMany 4D light field processing applications rely on superpixel segmentations, for which occlusion-aware view consistency is important. Yet, existing methods often enforce consistency by propagating clusters from a central view only, which can lead to inconsistent superpixels for non-central views. Our proposed approach combines an occlusion-aware angular segmentation in horizontal and vertical EPI spaces with an occlusion-aware clustering and propagation step across all views. Qualitative video demonstrations show that this helps to remove flickering and inconsistent boundary shapes versus the state-of-the-art approach, and quantitative metrics reflect these findings with improved boundary accuracy and view consistency scores. Numair Khan, Qian Zhang 0065, Lucas Kasser, Henry Stone, Min H. Kim 0001, James Tompkin 0001 |
ICCV | 4 |