NDLI: Spectral domain cross correlation function and generalized Learning Vector Quantization for recognizing and classifying Indonesian Sign Language

Content Provider	IEEE Xplore Digital Library
Author	Rakun, E. Febrian Rachmadi, M. Andros Danniswara, K.
Copyright Year	2012
Description	Author affiliation: Faculty of Computer Scien ce, University of Indonesia, Indonesia (Rakun, E.; Febrian Rachmadi, M.; Andros; Danniswara, K.)
Abstract	This paper shows the first part of the automatic Indonesian Sign Language (SIBI) into text translation system. The focus of this project is on translation of the alphabet (A to Z) and numbers 1 to 10. Using a combination of a Kinect camera, Discrete Cosine Transform (DCT), Cross Correlation Function and classifying algorithm Generalized Learning Vector Quantization (GLVQ) can create a simple system to recognize alphabet A to Z and number 1 to 10 in Indonesian Sign Language. The skeleton extraction function and depth sensor from the Kinect camera are used to capture and transfer of hand gesture movements into frames of images. DCT is used to transform spatial data of each frame of image into its spectral domain. Collection of Cross Correlation values between same rows or columns of data from two consecutive frames can be used as a signature of a character. Each signature is unique and needs a small amount of data. GLVQ is used as the classifying algorithm to recognize the character. From our experiments, the system we proposed has obtained a high degree of accuracy in the recognition of alphabet and numbers in Indonesian Sign Language.
Starting Page	213
Ending Page	218
File Size	805789
Page Count	6
File Format	PDF
ISBN	9781467330268
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-01
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Universitas Indonesia
Subject Keyword	Correlation Accuracy Handicapped aids Gesture recognition Feature extraction Skeleton Discrete cosine transforms
Content Type	Text
Resource Type	Article

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