Xiu et al., 2018 - Google Patents
3D semantic segmentation for high-resolution aerial survey derived point clouds using deep learningXiu et al., 2018
View PDF- Document ID
- 15796318588049635865
- Author
- Xiu H
- Vinayaraj P
- Kim K
- Nakamura R
- Yan W
- Publication year
- Publication venue
- Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
External Links
Snippet
Three-dimensional (3D) Semantic segmentation of aerial derived point cloud aims at assigning each point to a semantic class such as building, tree, road, and so on. Accurate 3D-segmentation results can be used as an essential information for constructing 3D city …
- 230000011218 segmentation 0 title abstract description 19
Classifications
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- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
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- G—PHYSICS
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- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
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- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
- G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
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