書誌事項
- タイトル別名
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- Model Based Defect Detection in Multi-Dimensional Vector Spaces
- タジゲン クウカン ニ オケル モデル ベースド ケッカン ケンシュツ
- Model‐based defect detection in multidimensional vector spaces
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説明
A highly sensitive inspection algorithm is proposed that extracts defects in multidimensional vector spaces from multiple images. The proposed algorithm projects subtraction vectors calculated from test and reference images to control the noise by reducing the dimensionality of vector spaces. The linear projection vectors are optimized using a physical defect model, and the noise distribution is calculated from the images. Because the noise distribution varies with the intensity or texture of the pixels, the target image is divided into small regions and the noise distribution of the subtraction images are calculated for each divided region. The bidirectional local perturbation pattern matching (BD-LPPM) which is an enhanced version of the LPPM, is proposed to increase the sensitivity when calculating the subtraction vectors, especially when the reference image contains more high-frequency components than the test image. The proposed algorithm is evaluated using defect samples for three different scanning electron microscopy images. The results reveal that the proposed algorithm increases the signal-to-noise ratio by a factor of 1.32 relative to that obtained using the Mahalanobis distance algorithm.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 131 (9), 1633-1641, 2011
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390001204607488896
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- NII論文ID
- 10030527565
- 10031118513
- 210000182254
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 19429541
- 03854221
- 19429533
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- NDL書誌ID
- 11218895
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDLサーチ
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- 抄録ライセンスフラグ
- 使用不可