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

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contributor.authorGhosh, D K-
contributor.authorAri, S-
date.accessioned2012-01-04T12:54:48Z-
date.available2012-01-04T12:54:48Z-
date.issued2011-12-
identifier.citation8Th International Conference on Information, Communications, and Signal Processing, Singapore, 13 - 16 December, 2011en
identifier.urihttp://hdl.handle.net/2080/1583-
descriptionCopyright belongs to proceeding publisheren
description.abstractThe 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 contour sequence (LCS) based feature is used here to classify the hand gestures. A k-mean based radial basis function neural network (RBFNN) is also proposed here for classification of hand gestures from LCS based feature set. The experiment is conducted on 500 train images and 500 test images of 25 class grayscale static hand gesture image dataset of Danish/international sign language hand alphabet. The proposed method performs with 99.6% classification accuracy which is better than earlier reported technique.en
format.extent224534 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherICICS 2011en
subjectLocalized Contour Sequence (LCS)en
subjectMorphological filteren
subjectMultiple Layer Perceptron Back Propagation Neural Network (MLPBPNN)en
subjectRadial Basis Function Neural Network (RBFNN)en
subjectSign Languageen
titleA Static Hand Gesture Recognition Algorithm Using K-Mean Based Radial Basis Function Neural Networken
typeArticleen
Appears in Collections:Conference Papers

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