Abstract
This chapter discusses several topics pertaining to ground truth data, the basis for computer vision metric analysis. We look at examples to illustrate the importance of ground truth data design and use, including manual and automated methods. We then propose a method and corresponding ground truth dataset for measuring interest point detector response as compared to human visual system response and human expectations. Also included here are example applications of the general robustness criteria and the general vision taxonomy developed in Chap. 5 as applied to the preparation of hypothetical ground truth data. Lastly, we look at the current state of the art, its best practices, and a survey of available ground truth datasets.
Buy the truth and do not sell it.
—Proverbs 23:23
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Notes
- 1.
See the “VLFeat” open-source project online (http://www.vlfeat.org).
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Krig, S. (2016). Ground Truth Data, Content, Metrics, and Analysis. In: Computer Vision Metrics. Springer, Cham. https://doi.org/10.1007/978-3-319-33762-3_7
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