Demo Data from EDBT 2026 / reviewers in the wild / expert
Yun-Chen Lin
dblp:366/3373
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0001-5819-2541ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
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 |
Visual content generation and editing · 50% Geometric modeling and processing · 50% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visual content generation and editing › image editing › image compositing
image collage |
0.8 | 1 | 2024 | Image Collage on Arbitrary Shape via Shape-Aware Slicing and Optimization · IEEE Trans. Vis. Comput. Graph. 2024 |
Geometric modeling and processing
shape decomposition |
0.8 | 1 | 2024 | Image Collage on Arbitrary Shape via Shape-Aware Slicing and Optimization · IEEE Trans. Vis. Comput. Graph. 2024 |
Mathematical optimization
combinatorial optimization |
0.2 | 1 | 2024 | Image Collage on Arbitrary Shape via Shape-Aware Slicing and Optimization · IEEE Trans. Vis. Comput. Graph. 2024 |
Mathematical optimization › optimization
layout optimization |
0.2 | 1 | 2024 | Image Collage on Arbitrary Shape via Shape-Aware Slicing and Optimization · IEEE Trans. Vis. Comput. Graph. 2024 |
Methods — techniques the papers use, named apart from their topics
saliency analysis · 1.5medial axis transform · 1.5binary slicing tree · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Image Collage on Arbitrary Shape via Shape-Aware Slicing and OptimizationabstractImage collage is a very useful tool for visualizing an image collection. Most of the existing methods and commercial applications for generating image collages are designed on simple shapes, such as rectangular and circular layouts. This greatly limits the use of image collages in some artistic and creative settings. Although there are some methods that can generate irregularly-shaped image collages, they often suffer from severe image overlapping and excessive blank space. This prevents such methods from being effective information communication tools. In this article, we present a shape slicing algorithm and an optimization scheme that can create image collages of arbitrary shapes in an informative and visually pleasing manner given an input shape and an image collection. To overcome the challenge of irregular shapes, we propose a novel algorithm, called Shape-Aware Slicing, which partitions the input shape into cells based on medial axis and binary slicing tree. Shape-Aware Slicing,which is designed specifically for irregular shapes, takes human perception and shape structure into account to generate visually pleasing partitions. Then, the layout is optimized by analyzing input images with the goal of maximizing the total salient regions of the images. To evaluate our method, we conduct extensive experiments and compare our results against previous work. The evaluations show that our proposed algorithm can efficiently arrange image collections on irregular shapes and create visually superior results than prior work and existing commercial tools. Dong-Yi Wu, Thi Ngoc Hanh Le, Sheng-Yi Yao, Yun-Chen Lin, Tong-Yee Lee |
IEEE Trans. Vis. Comput. Graph. | 4 |