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An experimental comparison of range image segmentation algorithms
Abstract
A methodology for evaluating range image segmentation algorithms is proposed. This methodology involves (1) a common set of 40 laser range finder images and 40 structured light scanner images that have manually specified ground truth and (2) a set of defined performance metrics for instances of correctly segmented, missed, and noise regions, over- and under-segmentation, and accuracy of the recovered geometry. A tool is used to objectively compare a machine generated segmentation against the specified ground truth. Four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches.
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Cited by
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Wang, C.-C., Thorpe, C., Thrun, S., Hebert, M., & Durrant-Whyte, H. (2007) · The International Journal of Robotics Research · doi:10.1177/0278364907081229 · Cited by 596
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
Ward, J. A., Lukowicz, P., Troster, G., & Starner, T. E. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.197 · Cited by 431
Performance metrics for activity recognition
Ward, J. A., Lukowicz, P., & Gellersen, H. W. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1889681.1889687 · Cited by 182
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Metadata
- Authors
- Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P. J., Bunke, H., Goldgof, D. B., Bowyer, K., Eggert, D. W., Fitzgibbon, A., & Fisher, R. B.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1996-07
- Volume
- 18
- Issue
- 7
- Pages
- 673-689
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2075690349
- Cited by
- 790
- References
- 0
- FWCI
- 24.12
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P. J., Bunke, H., Goldgof, D. B., Bowyer, K., Eggert, D. W., Fitzgibbon, A., & Fisher, R. B. (1996). An experimental comparison of range image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.506791
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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