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...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2015
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://psasir.upm.edu.my/id/eprint/65377/1/FSKTM%202015%2024IR.pdf |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
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| الملخص: | 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. |
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