dc.contributor.authorHou, Dajun.en_US
dc.description.abstractIn this thesis, a Computerized Auto-scoring System based on image processing and pattern recognition is presented. The scheme, which is implemented with the hardware system consisting of high-resolution digital cameras and personal computers, gains the advantages of low cost and easy maintenance that are two main requirements of an Auto-scoring System. Meanwhile, the system can achieve satisfactory accuracy and efficiency by using advanced pattern recognition technologies. Three kinds of classification methods — Statistical Classification, Radial Basis Function (RBF) neural networks and Support Vector Machines (SVM) — have been experimented for the particular problem called Bullet Hole Recognition in the system. All three methods have been tested based on the same samples and features. Experimental results show that both RBF and SVM can perform very well with error rate 1.85%. Thus, a function-well neural network based auto-scoring system for shooting range is built.en_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Applications of electronics
dc.titleNeural network based auto-scoring system for shooting rangeen_US
dc.contributor.supervisorSong, Qing.en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMASTER OF ENGINEERING (EEE)en_US

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