Single hand Indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction

Hand gestures in Bharatanatyam dance carry valuable information. Learning the meaning of hand gesture, mimic and practice them with the best way and high matching for the people who want to be expert in this field is necessary. Therefore,hand gesture recognition system can be implemented to help peo...

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主要作者:	Jaganathan, Kavitha
格式:	Thesis
語言:	English
出版:	2015
主題:	Human-computer interaction User interfaces (Computer systems) Gesture in dance
在線閱讀:	http://psasir.upm.edu.my/id/eprint/65377/1/FSKTM%202015%2024IR.pdf
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id	my-upm-ir.65377
record_format	uketd_dc
spelling	my-upm-ir.653772018-09-07T03:14:13Z Single hand Indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction 2015-09 Jaganathan, Kavitha Hand gestures in Bharatanatyam dance carry valuable information. Learning the meaning of hand gesture, mimic and practice them with the best way and high matching for the people who want to be expert in this field is necessary. Therefore,hand gesture recognition system can be implemented to help people to learn it effectively and efficiently. In this thesis, a combined feature extraction method based on the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is proposed. DWT with two level decompositions is applied to the image by size of 128 × 128. Two-dimensional DCT is then applied and convert the coefficients of DCT to vector. Finally, neuro fuzzy classifier is used to classify the given images in somegiven classes. The results are shown in a graphical format to prepare a good understanding in final stage for the user. A suitable number of images with good illumination for different applications have been created. Many types of image processing techniques like scaling and translation can also be applied to the original database and make ready more options for any future study. The experimental results show that the proposed method has good performance in most of single hand gestures. The dataset of single hand gesture in Bharatanatyam dance has been successfully created and it could serve as a benchmark data set as well. Our proposed system is able to recognize single hand gesture with the accuracy of 93%. At the testing stage, 130 out of 140 images of single hand gestures are correctly classified by the proposed graphic user interface GUI. This is because the parameters identified were the right signal, which gave the best 70 features to be classified and recognized. Human-computer interaction User interfaces (Computer systems) Gesture in dance 2015-09 Thesis http://psasir.upm.edu.my/id/eprint/65377/ http://psasir.upm.edu.my/id/eprint/65377/1/FSKTM%202015%2024IR.pdf text en public masters Universiti Putra Malaysia Human-computer interaction User interfaces (Computer systems) Gesture in dance
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Human-computer interaction User interfaces (Computer systems) Gesture in dance
spellingShingle	Human-computer interaction User interfaces (Computer systems) Gesture in dance Jaganathan, Kavitha Single hand Indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction
description	Hand gestures in Bharatanatyam dance carry valuable information. Learning the meaning of hand gesture, mimic and practice them with the best way and high matching for the people who want to be expert in this field is necessary. Therefore,hand gesture recognition system can be implemented to help people to learn it effectively and efficiently. In this thesis, a combined feature extraction method based on the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is proposed. DWT with two level decompositions is applied to the image by size of 128 × 128. Two-dimensional DCT is then applied and convert the coefficients of DCT to vector. Finally, neuro fuzzy classifier is used to classify the given images in somegiven classes. The results are shown in a graphical format to prepare a good understanding in final stage for the user. A suitable number of images with good illumination for different applications have been created. Many types of image processing techniques like scaling and translation can also be applied to the original database and make ready more options for any future study. The experimental results show that the proposed method has good performance in most of single hand gestures. The dataset of single hand gesture in Bharatanatyam dance has been successfully created and it could serve as a benchmark data set as well. Our proposed system is able to recognize single hand gesture with the accuracy of 93%. At the testing stage, 130 out of 140 images of single hand gestures are correctly classified by the proposed graphic user interface GUI. This is because the parameters identified were the right signal, which gave the best 70 features to be classified and recognized.
format	Thesis
qualification_level	Master's degree
author	Jaganathan, Kavitha
author_facet	Jaganathan, Kavitha
author_sort	Jaganathan, Kavitha
title	Single hand Indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction
title_short	Single hand Indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction
title_full	Single hand Indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction
title_fullStr	Single hand Indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction
title_full_unstemmed	Single hand Indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction
title_sort	single hand indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction
granting_institution	Universiti Putra Malaysia
publishDate	2015
url	http://psasir.upm.edu.my/id/eprint/65377/1/FSKTM%202015%2024IR.pdf
_version_	1747812333360513024

Single hand Indian classical gesture recognition based on discrete wavelet transform and discrete cosine transform feature extraction

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