NDLI: Recognition and classification of deaf signs using neural networks

Content Provider	IEEE Xplore Digital Library
Author	Susic, M.Z. Maksimovic, S.Z. Spasojevic, S.S. Durovic, Z.M.
Copyright Year	2012
Description	Author affiliation: University of Belgrade, Serbia (Durovic, Z.M.) \|\| Institute Mihailo Pupin, University of Belgrade, Serbia (Susic, M.Z.; Maksimovic, S.Z.; Spasojevic, S.S.)
Abstract	One approach for deaf signs recognition and classification is presented in the paper. It is assumed that the signs are presented in digital images. Recognition algorithm is consisted of several stages. At the beginning it is necessary to perform appropriate image processing in sense of segmentation and filtration of the input images. Aim is to detect arm position, i.e. sign of interest. For this purpose classifier for skin detection is used. Next stage has to generate feature vectors, which are used as inputs in neural network. Supervised training of neural network is performed. Reduction algorithm was used for purpose of dimension reduction of feature vectors, so the classification results can be displayed graphically.
Starting Page	65
Ending Page	70
File Size	754262
Page Count	6
File Format	PDF
ISBN	9781467315692
e-ISBN	9781467315722
DOI	10.1109/NEUREL.2012.6419965
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-20
Publisher Place	Serbia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Vectors Skin Image color analysis Support vector machine classification Image segmentation Training neural networks deaf signs feature vectors classification dimension reduction
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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