Study on classification and detection method for off-normal images in ICF automatic alignment system.

Due to off-normal images, the number of alignment iterations will be increased and the collimation efficiency will be decreased. To overcome this difficulty, by analyzing the characteristics of off-normal images, a Bayesian classifier is designed to achieve image classification, and the distortion images are detected by the shape factor of beam image. Experimental results show that: by setting reasonable class condition and shape determination standard, the off-normal image classification and examination filtration can be effectively realized. As a consequence, the collimation cycle-index and alignment defeat's probability due to the off-normal images is decreased, i.e. the total alignment efficiency is dramatically increased. In addition, the preliminary reason causing the off-normal images is analyzed, which supports the breakdown fast localization. [ABSTRACT FROM AUTHOR]