Abstract:
The detection of explosives in passengers¿ luggage is an important area in public traffic security. This paper presents a united classification system for detecting explo...Show MoreMetadata
Abstract:
The detection of explosives in passengers¿ luggage is an important area in public traffic security. This paper presents a united classification system for detecting explosives based on fuzzy rule and neural networks. Due to imaging and influence of outer environment, preprocessing is firstly needed to improve the quality of X-ray images. Then, a test pattern may be considered as several possible objects with different degrees through the multi-level fuzzy classifier. Finally, the result of multi-level fuzzy classifier will be reconsidered through the parallel neural networks classifier. From the experience results, the united classification system performs well.
Date of Conference: 17-19 November 2008
Date Added to IEEE Xplore: 30 December 2008
ISBN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Classification System ,
- Fuzzy Rules ,
- Fuzzy Neural Network ,
- Image Quality ,
- Environmental Influences ,
- Neural Network Classifier ,
- Test Pattern ,
- Parallel Network ,
- Fuzzy Classification ,
- Detection Of Explosives ,
- Typical Features ,
- Learning Rate ,
- Classification Accuracy ,
- Tertiary Education ,
- Image Classification ,
- Type Classification ,
- Fuzzy Set ,
- Feature Selection Methods ,
- Set Of Classes ,
- Bottom Level ,
- Fuzzy IF-THEN Rules
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Classification System ,
- Fuzzy Rules ,
- Fuzzy Neural Network ,
- Image Quality ,
- Environmental Influences ,
- Neural Network Classifier ,
- Test Pattern ,
- Parallel Network ,
- Fuzzy Classification ,
- Detection Of Explosives ,
- Typical Features ,
- Learning Rate ,
- Classification Accuracy ,
- Tertiary Education ,
- Image Classification ,
- Type Classification ,
- Fuzzy Set ,
- Feature Selection Methods ,
- Set Of Classes ,
- Bottom Level ,
- Fuzzy IF-THEN Rules