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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1583

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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