Effective and efficient optics inspection approach using machine learning algorithms

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7842, Issue , 2010, Pages

  Abdulla, Ghaleb M ^a   Kegelmeyer, Laura Mascio ^a   Liao, Zhi M ^a   Carr, Wren ^a  

^a LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CROSS CORRELATIONS; DATA MINING TECHNIQUES; DATA SETS; IMAGE DATA; INSPECTION CYCLES; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; MANUAL PROCESS; NATIONAL IGNITION FACILITY; NUMBER OF FALSE ALARMS; OPTICS DAMAGE; SELF-CONFIGURING; SUPERVISED CLASSIFICATION; TRAINING DATA;

DATA MINING; DEBRIS; FILTRATION; IGNITION; INSPECTION; LASER DAMAGE; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPTICAL MATERIALS;

NEODYMIUM LASERS;

EID: 79751503100     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.867648     Document Type: Conference Paper

References (8)

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3. Kegelmeyer, LM. Machine Learning to Reduce False Alarms in Shotcycle Inspections, presented at the LLNL CASTS workshop (November 16, 2007).
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