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http://hdl.handle.net/2080/1583
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| Title: | A Static Hand Gesture Recognition Algorithm Using K-Mean Based Radial Basis Function Neural Network |
| Authors: | Ghosh, D K
Ari, S |
| Keywords: | Localized Contour Sequence (LCS)
Morphological filter
Multiple Layer Perceptron Back Propagation Neural Network (MLPBPNN)
Radial Basis Function Neural Network (RBFNN)
Sign Language |
| Issue Date: | Dec-2011 |
| Publisher: | ICICS 2011 |
| Citation: | 8Th International Conference on Information, Communications, and Signal Processing, Singapore, 13 - 16 December, 2011 |
| Abstract: | The accurate classification of static hand gestures is a vital role to develop a hand gesture recognition system which is used for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC) application. A vision-based static hand gesture recognition algorithm consists of three stages: preprocessing, feature extraction and classification. The preprocessing stage involves following three sub-stages: segmentation which segments hand region from its background images using a histogram based thresholding algorithm and
transforms into binary silhouette; rotation that rotates segmented gesture to make the algorithm, rotation invariant; filtering that effectively removes background noise and object noise from
binary image by morphological filtering technique. To obtain a rotation invariant gesture image, a novel technique is proposed in this paper by coinciding the 1st principal component of the segmented hand gestures with vertical axes. A localized co... |
| Description: | Copyright belongs to proceeding publisher |
| URI: | http://hdl.handle.net/2080/1583 |
| Appears in Collections: | Conference Papers
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Files in This Item:
| File |
Description |
Size | Format |
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| A Static Hand Gesture.pdf | | 219Kb | Adobe PDF | View/Open |
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