Distance Metric Between 3D Models and 2D Images for Recognition and Classification

Weinshall, Daphna; Basri, Ronen

doi:10.1109/CVPR.1993.340986

Distance Metric Between 3D Models and 2D Images for Recognition and Classification

Daphna Weinshall, Ronen Basri

CVPR 1993 pp. 220-225

doi:10.1109/CVPR.1993.340986 /cvpr/1993/weinshall1993cvpr-distance/

Abstract

A transformation metric to measure the similarity between 3-D models and 2-D images is proposed. The transformation metric measures the amount of affine deformation applied to the object to produce the given image. A simple, closed-form solution for this metric is presented. This solution is optimal in transformation space, and it is used to bound the image metric from both above and below. The transformation metric can be used in several different ways in recognition and classification tasks.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

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Cite

Text

Weinshall and Basri. "Distance Metric Between 3D Models and 2D Images for Recognition and Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993. doi:10.1109/CVPR.1993.340986

Markdown

[Weinshall and Basri. "Distance Metric Between 3D Models and 2D Images for Recognition and Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993.](https://mlanthology.org/cvpr/1993/weinshall1993cvpr-distance/) doi:10.1109/CVPR.1993.340986

BibTeX

@inproceedings{weinshall1993cvpr-distance,
  title     = {{Distance Metric Between 3D Models and 2D Images for Recognition and Classification}},
  author    = {Weinshall, Daphna and Basri, Ronen},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1993},
  pages     = {220-225},
  doi       = {10.1109/CVPR.1993.340986},
  url       = {https://mlanthology.org/cvpr/1993/weinshall1993cvpr-distance/}
}