Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8256
標題: 利用具支持向量學習之模糊類神經網路及全域區域顏色為基礎之物體偵測
A Global-Local-Color based Object Detection System Using Fuzzy Neural Networks With Support Vector Learning
作者: 陳國泉
Chen, Guo-Cyuan
關鍵字: object detection
物體辨識
object recognition
principal component
support vector machine
fuzzy system
物體偵測
主分量
支持向量機
模糊系統
出版社: 電機工程學系所
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Murase, “Focused color intersection with efficient searching for object extraction,” Pattern Recognition, 30(10) (1997) 1787-1797. [13] P. Chang and J. Krumm, “Object recognition with color cooccurrence histograms,” in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2 (1999) 498-504. [14] T. Kawanishi, T. Kurozumi, K. Kashino, and S. Takagi, “Dynamic active search for quick object detection with pan-tilt-zoom camera,” in: Proc. IEEE Int. Conf. Image Processing, (2001) 716-719. [15] K. K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” IEEE Trans. Pattern Analysis and Machine Intelligence. 20 (1) (1998) 39-51. [16] H. Zhang, W. Gao, X. Chen, and D. Zhao, “Object detection using spatial histogram features,” Image and Vision Computing, 24 (2006) 327-341. [17] B. Schiele and J. L. Crowley, “Object recognition using multidimensional receptive field histograms,” in: Proc. IEEE Int. Conf. Pattern Recognition, 2 (1996) 50-54. [18] D. 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摘要: 本論文提出透過一種由模糊類神經網路與主分量為基礎的支持向量學習(FNN-PCSV) 之即時物體偵測的新方法。FNN-PCSV是一種TSK型式模糊規則所組成的模糊系統。FNN-PCSV的前件部是透過輸入數據的模糊分群而產生的,在FNN-PCSV的後件部首先是利用PCA來減少維度,然後在主分量空間利用線性的支持向量機調整後件部的參數,以提供網路較好的綜合成效。物體偵測系統包含兩階段。第一階段利用一個物體當作偵測特徵的全域色彩出現的彩色直方圖送入一個FNN-PCSV分類器。要能夠準確的描述彩色資訊利用直方圖是困難的,因此提出了色彩空間的非均勻切割。一個有效率的方法針對直方圖的擷取在影像的掃瞄過程中提出了即時系統的應用。第二階段利用幾何相依的區域色彩出現的色彩特徵送入另一個FNN-PCSV分類器中。候選區產生在第一階段,而這階段是減少錯誤個數的產生。為了要驗證所提出方法的成效,因此做了兩個特定物體的實驗。為了能加以比較,其他型式的偵測方法和分類器皆被應用在相同的問題。結果發現我們提出的FNN-PCSV 為基礎偵測的方法和其他方法比較有最好偵測結果。
A new method for real-time object detection by a Fuzzy Neural Network with Principal Component-based Support Vector learning (FNN-PCSV) is proposed in this thesis. FNN-PCSV is a fuzzy system that consists of Takagi-Sugeno-Kang (TSK) type fuzzy rules. The antecedent part of FNN-PCSV is generated via fuzzy clustering of the input data. The dimension of free parameter vector in the consequent part of FNN-PCSV is first reduced by the PCA. A linear support vector machine is then used to tune the consequent parameters on the principal component space to give the network better generalization performance. The object detection system consists of two stages. The first stage uses color histogram of the global color appearance of an object as detection feature for a FNN-PCSV classifier. To represent color information by histograms as accurately as possible, a non-uniform partition of color space is proposed. An efficient method for histogram extraction during the image scanning process is proposed for real-time implementation. The second stage uses geometry-dependent local color appearance as color feature for another FNN-PCSV classifier. Candidates generated in stage one are filtered in this stage to reduce the number of false alarms. To verify performance of the proposed method, experiments on detection of two specific objects are performed. For comparison, other types of detection methods and classifiers are also applied to the same detection task. Results show the proposed FNN-PCSV-based detection system achieves better results than compared methods.
URI: http://hdl.handle.net/11455/8256
其他識別: U0005-2107200816491600
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2107200816491600
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