Generalized Tube Model: Recognizing 3D Elongated Objects from 2D Intensity Images
Abstract
The issue of recognizing 3D elongated objects from 2D intensity images is addressed. A tube model, locally similar to generalized cones, is developed for the class of elongated objects. A recognition strategy that combines 2D contour properties and surface shading information is used to exploit the power of the 3D model. Reliable contours provide constraints for localizing the objects of interest. The theory of optimal filters is adopted in verifying the shading of hypothesized objects. Object recognition is achieved through optimizing the signal-to-noise response with respect to model parameters. A sweeping operation is proposed as a further stage of identifying objects so that the overall performance of the system does not heavily rely on the quality of local feature detection.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Huang and Stockman. "Generalized Tube Model: Recognizing 3D Elongated Objects from 2D Intensity Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993. doi:10.1109/CVPR.1993.340972Markdown
[Huang and Stockman. "Generalized Tube Model: Recognizing 3D Elongated Objects from 2D Intensity Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993.](https://mlanthology.org/cvpr/1993/huang1993cvpr-generalized/) doi:10.1109/CVPR.1993.340972BibTeX
@inproceedings{huang1993cvpr-generalized,
title = {{Generalized Tube Model: Recognizing 3D Elongated Objects from 2D Intensity Images}},
author = {Huang, Qian and Stockman, George C.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1993},
pages = {104-109},
doi = {10.1109/CVPR.1993.340972},
url = {https://mlanthology.org/cvpr/1993/huang1993cvpr-generalized/}
}