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Nonlinearity reduction of manifolds using Gaussian blur for handshape recognition based on multi-dimensional grids

Farouk, Mohamed, Sutherland, Alistair and Shoukry, Amin A. (2013) Nonlinearity reduction of manifolds using Gaussian blur for handshape recognition based on multi-dimensional grids. In: ICPRAM 2013 : 2nd International Conference on Pattern Recognition Applications and Methods, 15-18 Feb 2013, Barcelona, Spain.

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Abstract

This paper presents a hand-shape recognition algorithm based on using multi-dimensional grids (MDGs) to divide the feature space of a set of hand images. Principal Component Analysis (PCA) is used as a feature extraction and dimensionality reduction method to generate eigenspaces from example images. Images are blurred by convolving with a Gaussian kernel as a low pass filter. Image blurring is used to reduce the non-linearity in the manifolds within the eigenspaces where MDG structure can be used to divide the spaces linearly. The algorithm is invariant to linear transformations like rotation and translation. Computer generated images for different hand-shapes in Irish Sign Language are used in testing. Experimental results show accuracy and performance of the proposed algorithm in terms of blurring level and MDG size.

Item Type:Conference or Workshop Item (Poster)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Computer vision; Principal Component Analysis; Gaussian Blurring; Multi-dimensional grids; Multi-stage hierarchy
Subjects:Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
ID Code:19305
Deposited On:17 Sep 2013 13:23 by Alistair Sutherland . Last Modified 19 Jul 2018 15:01

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