ML Anthology

Learning-Based Hand Sign Recognition Using SHOSLIF-M

Yuntao Cui, Daniel L. Swets, Juyang Weng
ICCV 1995 pp. 631-636
doi:10.1109/ICCV.1995.466879 /iccv/1995/cui1995iccv-learning/

Abstract

We present a self-organizing framework called the SHOSLIF-M for learning and recognizing spatiotemporal events (or patterns) from intensity image sequences. The proposed framework consists of a multiclass, multivariate discriminant analysis to automatically select the most discriminating features (MDF), a space partition tree to achieve a logarithmic retrieval time complexity for a database of n items, and a general interpolation scheme to do view inference and generalization in the MDF space based on a small number of training samples. The system is tested to recognize 28 different hand signs. The experimental results show that the learned system can achieve a 96% recognition rate for test sequences that have not been used in the training phase.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Cui et al. "Learning-Based Hand Sign Recognition Using SHOSLIF-M." IEEE/CVF International Conference on Computer Vision, 1995. doi:10.1109/ICCV.1995.466879

Markdown

[Cui et al. "Learning-Based Hand Sign Recognition Using SHOSLIF-M." IEEE/CVF International Conference on Computer Vision, 1995.](https://mlanthology.org/iccv/1995/cui1995iccv-learning/) doi:10.1109/ICCV.1995.466879

BibTeX

@inproceedings{cui1995iccv-learning,
  title     = {{Learning-Based Hand Sign Recognition Using SHOSLIF-M}},
  author    = {Cui, Yuntao and Swets, Daniel L. and Weng, Juyang},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1995},
  pages     = {631-636},
  doi       = {10.1109/ICCV.1995.466879},
  url       = {https://mlanthology.org/iccv/1995/cui1995iccv-learning/}
}