TY - GEN
T1 - Two-dimensional invariant pattern recognition using a back-propagation network improved by distributed associative memory
AU - Shih, Jau Ling
AU - Chung, Pau Choo
PY - 1995
Y1 - 1995
N2 - A system combining the BackPropagation Network (BPN) and the Distributed Associative Memory (DAM) for 2D pattern recognition is proposed. In the system, a sequence of image processes and transformations, including complex transform, Laplacian, and Fourier transform are used for invariant feature extraction. Two modified neural networks are proposed for pattern recognition: (1) the DAM combined with BPN, and (2) the BPN improved by DAM. In the DAM combined with BPN, a fine training is provided by the BPN to take the pattern variations within each class into the training procedure. Experimental results indicate that this improved DAM has higher recognition rates compared to a traditional DAM. In the BPN improved by DAM, the weights of the first layer used the memory matrix of DAM as initial values. This network is compared with the BPN. Experimental results show that this network not only has slightly higher recognition rate, but also requires less training time than a BPN. Finally, the system is also tested with noisy patterns. According to the experiments results, it is found that the system also has high recognition rate even on the noisy images.
AB - A system combining the BackPropagation Network (BPN) and the Distributed Associative Memory (DAM) for 2D pattern recognition is proposed. In the system, a sequence of image processes and transformations, including complex transform, Laplacian, and Fourier transform are used for invariant feature extraction. Two modified neural networks are proposed for pattern recognition: (1) the DAM combined with BPN, and (2) the BPN improved by DAM. In the DAM combined with BPN, a fine training is provided by the BPN to take the pattern variations within each class into the training procedure. Experimental results indicate that this improved DAM has higher recognition rates compared to a traditional DAM. In the BPN improved by DAM, the weights of the first layer used the memory matrix of DAM as initial values. This network is compared with the BPN. Experimental results show that this network not only has slightly higher recognition rate, but also requires less training time than a BPN. Finally, the system is also tested with noisy patterns. According to the experiments results, it is found that the system also has high recognition rate even on the noisy images.
UR - http://www.scopus.com/inward/record.url?scp=0029222896&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029222896&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029222896
SN - 0891418587
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 140
EP - 147
BT - Proceedings of SPIE - The International Society for Optical Engineering
PB - Society of Photo-Optical Instrumentation Engineers
T2 - Visual Communications and Image Processing '95
Y2 - 24 May 1995 through 26 May 1995
ER -