Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9130
標題: 使用支持向量學習之第二型類神經模糊分類器及人體姿態辨識應用
Reduced Type-2 Neural Fuzzy Classifers With Support Vector Learning And Human Body Posture Recongnition Applications
作者: 王柏軒
Wang, Po-Hsuan
關鍵字: 類神經模糊分類器
Neural Fuzzy Classifers
支持向量機學習
姿態辨識
Support Vector Machine Learning
Body Posture Recongnition
出版社: 電機工程學系所
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摘要: 本論文提出精簡區間第二型類神經模糊分類器,前後件部分別使用邊界篩選梯度下降(MSGD)以及支持向量機學習。此分類器簡稱為RIT2BFC-MS.。RIT2BFC-MS前件部使用區間第二型模糊集合(IT2FSs),後件部為零階Takagi-Sugeno-Kang (TSK)形式,可採用單一數值或區間值。RIT2BFC-MS使用加權集合邊界去簡化降階運算且減少軟體的訓練時間。RIT2BFC-MS透過線上架構學習。前件部參數學習透過MSGD去減少訓練樣本與避免過度訓練。後件部參數學習透過支持向量機去賦予分類器高辨識能力。RIT2BFC-MS應用在基於視覺人體姿態分類問題。此分類系統使用兩組攝影機且從人體剪影擷取特徵值去分類四種姿態,站、彎、坐、躺。特徵值包含從人體乾淨剪影做水平與垂直投影的離散傅立葉轉換(DFT)係數與人體長寬比值。此外,將包住人體剪影的最小正方形網格均勻切割,剪影在每個均勻分割網格的面積比例做為特徵值。RIT2BFC-MS的分類性能透過姿態分類問題和幾個基準分類問題跟各個第一型模糊系統比較得到驗證。
This thesis proposes a reduced interval type-2 neural fuzzy classifier with margin-selective gradient descent (MSGD) and support vector learning for antecedent and consequent part learning, respectively, and the classifier is called RIT2BFC-MS. The antecedent part in the RIT2BFC-MS uses interval type-2 fuzzy sets (IT2FSs) and the consequent part uses zero-order Takagi-Sugeno-Kang (TSK)-type rules with crisp or interval values. The RIT2BFC-MS uses weighted bound-set boundaries to simplify type-reduction operation and reduce training time. The RIT2BFC-MS is built through online structure learning. The antecedent parameters are tuned through MSGD learning to reduce training samples and avoid overtraining. The consequent parameters are learned through support vector machine to endow the classifier high generalization ability. The RIT2BFC-MS is applied to a vision-based human body posture classification problem. The classification system uses two cameras and novel features extracted from segmented human silhouettes to classify the four postures of standing, bending, sitting, and lying. The novel features include discrete Fourier transform (DFT) coefficients calculated from horizontal and vertical projections of the body silhouette and length-width ratio of human body. In addition, the minimum enclosing square of the body silhouette is uniformly partitioned and the area ratio of the silhouette in each partitioned grid is calculated used as another set of features. Classification performance of the RIT2BFC-MS is verified through the posture classification problem and several benchmark classification problems and comparisons with various type-1 and type-1 fuzzy classifiers.
URI: http://hdl.handle.net/11455/9130
其他識別: U0005-1608201315021000
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1608201315021000
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