Development of Position and Scale-invariant Pattern Recognition Systems

• Main Authors: Ke Shun-ching, 柯順清
• Other Authors: An-Chyau Huang
• Format: Others
• Language: zh-TW
• Published: 1999
• Online Access: http://ndltd.ncl.edu.tw/handle/39950137540155197392

碩士 === 國立臺灣科技大學 === 機械工程系 === 87 === The purpose of this thesis is to develop a position/scaling-invariant pattern recognition system. Two basic methods are proposed based on global features and local features. Both methods are embedded in the traditional Hamming network where pattern recognition activities are performed. For the first method, the global feature of the pattern is input into a feature extraction network to calculate its exact position. An associate memory network is employed to perform static mapping of the input feature position and the weighting matrix of the Hamming network so that a proper position/scaling-invariant property can be obtained. For the second method, local features of the input pattern are assumed to be available for the proposed system. Three schemes are suggested for the encoding of the input local features. Each one of which utilizes properties such as feature contribution and weights on feature connectivity. Simulation results show that both methods give position/scaling-invariant properties with reasonable complexity in terms of the encoding date size, feature order, and robustness.