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Temporally coherent 3D point cloud video segmentation in generic scenes

Author
Lin, X.; Casas, J.; Pardas, M.
Type of activity
Journal article
Journal
IEEE transactions on image processing
Date of publication
2018-03-02
Volume
27
Number
6
First page
3087
Last page
3099
DOI
https://doi.org/10.1109/TIP.2018.2811541 Open in new window
Project funding
Heterogeneous information and graph signal processing for the Big Data era. Application to high-throughput, remote sensing, multimedia and human computer interfaces
Multimodal Signal Processing and Machine Learning on Graphs
Repository
http://hdl.handle.net/2117/120434 Open in new window
URL
https://ieeexplore.ieee.org/document/8306148/ Open in new window
Abstract
Video segmentation is an important building block for high level applications, such as scene understanding and interaction analysis. While outstanding results are achieved in this field by the state-of-the-art learning and model-based methods, they are restricted to certain types of scenes or require a large amount of annotated training data to achieve object segmentation in generic scenes. On the other hand, RGBD data, widely available with the introduction of consumer depth sensors, provide ac...
Citation
Lin, X., Casas, J., Pardas, M. Temporally coherent 3D point cloud video segmentation in generic scenes. "IEEE transactions on image processing", 2 Març 2018, vol. 27, núm. 6, p. 3087-3099.
Keywords
3D connectivity, Hierarchical segmentation, Point clouds, RGBD data, Video segmentation
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

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