29 April 2013 Optical image processing and pattern recognition algorithms for optimal optical data retrieval
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Automatic pattern recognition algorithms are implemented to correct distortion and remove noise from the optical medium in the multi-channel optical communication systems. The post-processing involves filtering and correlation to search for accurate location of every optical data element. Localized thresholding and neural network training methods are used to accurately digitize the analog optical images into digital data pages. The goal is to minimize the bit-errorrate (BER) in the optical data transmission and receiving process. Theoretical analysis and experimental tests have been carried out to demonstrate the improved optical data retrieval accuracy.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian Walker, Brian Walker, Thomas Lu, Thomas Lu, Sean Stuart, Sean Stuart, George Reyes, George Reyes, Tien-Hsin Chao, Tien-Hsin Chao, } "Optical image processing and pattern recognition algorithms for optimal optical data retrieval", Proc. SPIE 8748, Optical Pattern Recognition XXIV, 87480L (29 April 2013); doi: 10.1117/12.2018264; https://doi.org/10.1117/12.2018264


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