Grasp type revisited: A modern perspective on a classical feature for vision

Yezhou Yang; Cornelia Fermüller; Yi Li; Yiannis Aloimonos

doi:10.1109/CVPR.2015.7298637

Grasp type revisited: A modern perspective on a classical feature for vision

Yezhou Yang, Cornelia Fermüller, Yi Li, Yiannis Aloimonos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

56 Scopus citations

Abstract

The grasp type provides crucial information about human action. However, recognizing the grasp type from 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.

Original language	English (US)
Title of host publication	IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Publisher	IEEE Computer Society
Pages	400-408
Number of pages	9
ISBN (Electronic)	9781467369640
DOIs	https://doi.org/10.1109/CVPR.2015.7298637
State	Published - Oct 14 2015
Externally published	Yes
Event	IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States Duration: Jun 7 2015 → Jun 12 2015

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	07-12-June-2015
ISSN (Print)	1063-6919

Other

Other	IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Country/Territory	United States
City	Boston
Period	6/7/15 → 6/12/15

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR.2015.7298637

Cite this

Yang, Y., Fermüller, C., Li, Y., & Aloimonos, Y. (2015). Grasp type revisited: A modern perspective on a classical feature for vision. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 (pp. 400-408). Article 7298637 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 07-12-June-2015). IEEE Computer Society. https://doi.org/10.1109/CVPR.2015.7298637

Grasp type revisited: A modern perspective on a classical feature for vision. / Yang, Yezhou; Fermüller, Cornelia; Li, Yi et al.
IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015. IEEE Computer Society, 2015. p. 400-408 7298637 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 07-12-June-2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yang, Y, Fermüller, C, Li, Y & Aloimonos, Y 2015, Grasp type revisited: A modern perspective on a classical feature for vision. in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015., 7298637, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 07-12-June-2015, IEEE Computer Society, pp. 400-408, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, United States, 6/7/15. https://doi.org/10.1109/CVPR.2015.7298637

Yang Y, Fermüller C, Li Y, Aloimonos Y. Grasp type revisited: A modern perspective on a classical feature for vision. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015. IEEE Computer Society. 2015. p. 400-408. 7298637. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). doi: 10.1109/CVPR.2015.7298637

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Grasp type revisited: A modern perspective on a classical feature for vision

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