Smoothing rope skipping data using gaussian scale-space Method
The scale-space method has been widely used in handling image data at multiple scales. Application of Gaussian filtering in different field includes human vision problem, medical data, financial data and electroencephalogram (EEG) signal. The main purpose of this paper is to apply the Gaussian scale...
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| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Penerbit Universiti Kebangsaan Malaysia
2013
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| オンライン･アクセス: | http://journalarticle.ukm.my/6491/1/jqma-9-1-paper10.pdf http://journalarticle.ukm.my/6491/ http://www.ukm.my/jqma/ |
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| 要約: | The scale-space method has been widely used in handling image data at multiple scales. Application of Gaussian filtering in different field includes human vision problem, medical data, financial data and electroencephalogram (EEG) signal. The main purpose of this paper is to apply the Gaussian scale-space method by determining a suitable σ value in order to smooth rope skipping data. Smoothing technique using a Gaussian kernel with a selection of
bandwidth (σ) and time (x) is applied. It is found that the tolerance value of σ can be used to smooth not only one set of data, but also other biomechanical data of different anatomical body landmarks. |
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