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Segmentation and Classification of Range Images
Abstract
The recognition of objects in three-dimensional space is a desirable capability of a computer vision system. Range images, which directly measure 3-D surface coordinates of a scene, are well suited for this task. In this paper we report a procedure to detect connected planar, convex, and concave surfaces of 3-D objects. This is accomplished in three stages. The first stage segments the range image into ``surface patches'' by a square error criterion clustering algorithm using surface points and associated surface normals. The second stage classifies these patches as planar, convex, or concave based on a non-parametric statistical test for trend, curvature values, and eigenvalue analysis. In the final stage, boundaries between adjacent surface patches are classified as crease or noncrease edges, and this information is used to merge compatible patches to produce reasonable faces of the object(s). This procedure has been successfully applied to a large number of real and synthetic images, four of which we present in this paper.
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Cited by
Multimodal 2D+3D Facial Expression Recognition With Deep Fusion Convolutional Neural Network
Li, H., Sun, J., Xu, Z., & Chen, L. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2713408 · Cited by 211
Metadata
- Authors
- Hoffman, R., & Jain, A. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1987-09
- Volume
- PAMI-9
- Issue
- 5
- Pages
- 608-620
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2030129920
- Cited by
- 418
- References
- 0
- FWCI
- 12.00
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hoffman, R., & Jain, A. K. (1987). Segmentation and Classification of Range Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.1987.4767955
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