Xianqiang Lv

dblp:71/763 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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
Computational photography and imaging · 50% Image and video processing · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › image segmentation
interactive segmentation
0.512021
4D Light Field Segmentation From Light Field Super-Pixel Hypergraph Representation · IEEE Trans. Vis. Comput. Graph. 2021
Computational photography and imaging › light field imaging
light field segmentation
0.512021
4D Light Field Segmentation From Light Field Super-Pixel Hypergraph Representation · IEEE Trans. Vis. Comput. Graph. 2021

Methods — techniques the papers use, named apart from their topics

superpixel · 0.5hypergraph representation · 0.5graph-cut optimization · 0.5
YearPublicationVenuePosition
2021 4D Light Field Segmentation From Light Field Super-Pixel Hypergraph Representation
abstract
Efficient and accurate segmentation of full 4D light fields is an important task in computer vision and computer graphics. The massive volume and the redundancy of light fields make it an open challenge. In this article, we propose a novel light field hypergraph (LFHG) representation using the light field super-pixel (LFSP) for interactive light field segmentation. The LFSPs not only maintain the light field spatio-angular consistency, but also greatly contribute to the hypergraph coarsening. These advantages make LFSPs useful to improve segmentation performance. Based on the LFHG representation, we present an efficient light field segmentation algorithm via graph-cut optimization. Experimental results on both synthetic and real scene data demonstrate that our method outperforms state-of-the-art methods on the light field segmentation task with respect to both accuracy and efficiency.
Xianqiang Lv, Xue Wang 0006, Qing Wang 0006, Jingyi Yu 0001
IEEE Trans. Vis. Comput. Graph.1