A Global-Local-Color based Object Detection System Using Fuzzy Neural Networks With Support Vector Learning
碩士 === 國立中興大學 === 電機工程學系所 === 96 === 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 antecede...
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| Format: | Others |
| Language: | en_US |
| Published: |
2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/42024194836296031108 |
| Summary: | 碩士 === 國立中興大學 === 電機工程學系所 === 96 === 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.
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