Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision
Abstract
The grasp type provides crucial information about human action. However, recognizing the grasp type in unconstrained scenes is challenging because of the large variations in appearance, occlusions and geometric distortions. In this paper, first we present a convolutional neural network to classify functional hand grasp types. Experiments on a public static scene hand data set validate good performance of the presented method. Then we present two applications utilizing grasp type classification: (a) inference of human action intention and (b) fine level manipulation action segmentation. Experiments on both tasks demonstrate the usefulness of grasp type as a cognitive feature for computer vision. This study shows that the grasp type is a powerful symbolic representation for action understanding, and thus opens new avenues for future research.
Cite
Text
Yang et al. "Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298637Markdown
[Yang et al. "Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/yang2015cvpr-grasp/) doi:10.1109/CVPR.2015.7298637BibTeX
@inproceedings{yang2015cvpr-grasp,
title = {{Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision}},
author = {Yang, Yezhou and Fermuller, Cornelia and Li, Yi and Aloimonos, Yiannis},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2015},
doi = {10.1109/CVPR.2015.7298637},
url = {https://mlanthology.org/cvpr/2015/yang2015cvpr-grasp/}
}