?????????????LCD???

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?????????????LCD???
2
??

• ??,Thin film transistor liquid crystal
  display(TFT-LCD)???????????????????????????LCD????
  ???????????,??????????????????????????????,???????
  global(??)?????????TFT????????(??????)?????TFT????
  ?????????????????????????,????????????????(structu
  ral texture)???????????????????????????????????(SV
  D)???????????(It is based on a global image
  reconstruction scheme using the singular value
  decomposition (SVD) ).??????????????,?????????????
  ???????????????????????????,????????????????????,?
  ?,????????????????????,????????????????????????,??
  ?????TFT?????????,?????,????,??,?????,???????????
  ???????LCD????????

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1,??

• ???,TFT-LCD???????,???????????????,???,???????????
  ??????LCD??????,?TFT??????????????????????????????
  ?????????LCD????????,?????????????????????????????
  ??????????????????????????????,????????TFT????????
  ????????,???????????????TFT-LCD??????
• TFT???????????????,?????LCD?????????????TFT???????
  ?????2???????????????????MURA,SIMI,ZURE,MURA???
  ??????,SIMI????TFT??????,ZURE????????????????????,
  ??,????????????????????????????????????,??????????
  ??????????????????????????????????????????????????
  TFT??????????????

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• ??LCD??????,??????????????????????LCD?????????Henl
  y?Addiego?2?????????????,??????LCD?????????????Kid
  o et al.??????????????????????LCD?????????????????
  ??????????????????????????????????????????????????
  ???????????????TFT???????????????,????????????????
  ??????????????????????
• ????????????????LCD????Nakashima???LCD????????????
  ????????????LCD???
• Nakashima presented an inspection system based on
  image subtraction and optical Fourier filtering
  for detecting defects on an LCD colour filter
  panel?

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• Sokolov and Treskunov developed an automatic
  vision system for final chech of LCD output
  check.
• Slkolov?Treskunov?????LCD??????
• ????LCD??????????????????,????????????????
• ??TFT????????????????????????????,TFT?????????????
  ???????,?????????????TFT?????????????????????????T
  FT???????????????????????????????,????SVD?????????
  TFT?????????

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• In this paper, we propose a global approach that
  uses an SVD-based image reconstruction technique
  for inspecting micro defects including pinholes,
  scratches, particles and fingerprints
• on the surface of TFT panels. The proposed method
  does not
• rely on textural features to detect local
  anomalies, and does not require a reference image
  for comparison. It alleviates all limitations of
  the feature extraction schemes and template
  matching methods just mentioned.
• ????,?????????????SVD?????????????,??????,??,??,??
  ??????????????????????,??????2????????????????????
  ?????????????????
• SVD??????????????????????????????????????????????
  ????????LCD?????,??????????0?(preserve the
  smaller singular values)??????????????,???????????
  ????

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2??????
Fig. 1. The schema of a single pixel of a TFT panel ???1???????TFT?????????? At each pixel, the gate of the TFT is connected to the gate line and the source is connected to the data line. ?????,TFT?GATE???GATE??,???????????
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Fig. 2. The surface image of a TFT panel ???????TFT?????????,????,????????????
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??????

• ???????MN?????X,MgtN,??????????????????R?????????,
  ??R???X??,??RltN?
• XUSVT,??,U????????XXT??MR????V?NR??????????XTX?
  ??S?RR????,????????,??XTX?????????????a??,???????
  ???????
• SVD??????X??????????(a)???????X??????????(energe)?
  ???????????,??????X????????????,??????????????????
  ?,???????0?
• The singular values and their distribution, which
  carry useful information about the contents of X,
  vary drastically from image to image. For an
  image with orthogonal texture content such as
  horizontal and/or vertical structures, only a
  very few larger singular values will dominate,
  and yet all others have magnitudes close to zero.

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Fig. 3. a and b Two artificial lines images with different line spacing c A TFT panel image d The plot of the corresponding first ten largest singular values
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• ???a,b????????,C??????,????,???????????????????,??
  ?,????????0.
• ???????,???????????????????????????
• In most of the cases, the larger singular values
  (with lager
• magnitude) represent the global approximation of
  the original
• Image
• ?????????????????,??,???????????????

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??SVD?????

• ??????,?????????????TFT?????(??)?SVD??????TFT?????
  ???????????,??SVD????????????TFT??????????????????
  ????????????????SVD?????????TFT???????????????????
  ????????????????TFT???????????????????????????????
  
• X?UjajVTj J?k1?r?
• X???????,Uj?Vj?U?V??j???k????????????aj?S??j????
  ,r???X???

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???????a,b1,b2,b3,c1,c2,c3
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• Fig. 4. a The artificial horizontal/vertical
  lines image (the original image) b1 the
  reconstructed image from s1 b2 The reconstructed
  image from s2 b3 The reconstructed image from
  both s1 and s2 c1 The reconstructed image
  excluding s1 c2 The reconstructed image
  excluding s2 b3 The reconstructed image
  excluding both s1 and s2
• a???????????(??????),b1??a1??????,b2?a2????,b3??a1
  ,a2????,c1??????a1??????????,c2???a2????,c3????a1,
  a2?????

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????????

• ???1???????????????,???2??????????????????????????
  ??????,???k??????2??????????????????????k????,
• ???????????????
• ai(ai-ua)/sa i1,2r
• ??,???????i????(normalize)????,ai ??i????,ua
  ???,s??????????(standard deviation of all
• singular values)
• ??si si -si1 ??????I???????????????????,???????
  ???????
• If ?si is larger
• than some threshold (T?s ), the additional
  singular value si1
• is considered to be significant.

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?5,?????a,b,c,d
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y

• Fig. 5ad. The artificial orthogonal image with
  scratch defects a The original image b The plot
  of the marginal gain (?s) of normalized singular
  values c The restored image d the resulting
  binary image for defect segmentation
• a??????,b??a?????,c????????,d?????????????,???,k4
  ,?4??,??????????k?????,???????????????k???????????
  ??c?????????????????????
• ??????????????,???????????????????,???????????????
  ????????????????????
• µ?X t s?X
• ??µ?X?s?X??????????????(standard deviation of
  grey levels),t??????

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• According to the Chebyshevs theorem 4, the
  probability that any random variable x will fall
  within t standard deviations of the mean is at
  least 1- 1 /2 . That is
• p(µ?X -t s?Xlt x ltµ?X t s?X) 1-1/2
• ?????????????X??????????????????
• ?TFT??????????,?????????????,??????,??????????k4,
  ?????93.75?????

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?????

• ?????,??????????,???????????????
• ???256256??????8?????6?a---c????3??????????????
• Fig. 6ac. Three defective images under fine
  image resolution (60 pixels/mm) a Pinhole b
  Scratch c Particle

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Fig. 7. A defective image with fingerprint under coarse image resolution (20pixels/mm) ?7?????????????(?????20???)
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• ?8?a?d????????s ??6??7?????????????????,????????0.
  05??,??????0?????

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• ?1???????????????4?????????????????????0.05,??,??
  ??????????0???,0.05???????????
• Defective image Pinhole Fig. 6a Scratch Fig. 6b
  Particle Fig. 6c Fingerprint Fig. 7 Singular
  value
• (si) s ?s s ?s s ?s
  s ?s
• s1 15.86 14.56 15.77 13.96 15.83 14.38
  15.89 15.29
• s2 1.30 0.85 1.81 0.98 1.45 0.90
  0.60 0.10
• s3 0.45 0.15 0.83 0.30 0.55 0.26
  0.51 0.17
• s4 0.30 0.13 0.53 0.21 0.30 0.04
  0.34 0.05
• s5 0.17 0.04 0.32 0.05 0.25 0.04
  0.29 0.10
• s6 0.13 0.04 0.27 0.10 0.21 0.04
  0.19 0.02
• s7 0.09 0.03 0.17 0.10 0.17 0.03
  0.17 0.01
• s8 0.06 0.03 0.07 0.02 0.14 0.02
  0.16 0.04
• s9 0.03 0.01 0.05 0.02 0.12 0.03
  0.12 0.01
• s10 0.02 0.02 0.04 0.01 0.09 0.01
  0.11 0.02

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• ?9?a1,b1,c1,d1????6?7?TFT?????????9a2?????5???????
  ?????????????????????????????,????????????????????
  ??????????b2?c2??????8???4?????????,??????????????
  ????D2????d1??6???????????a3d3???????????????????
  ????,????????????

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?9Fig. 9. a1-d1 The defective images with
pinhole, scratch, particle and fingerprint,
respectively a2-d2 The respective restored
images a3-d3 The resulting binary images for
defect segmentation
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• ????????????,?????TFT????????10a?????6????????,b??
  ???????

Fig. 10. a A faultless version of the image in Fig. 6 b The plot of marginal gains (?s) c The restored image d The resulting binary image
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???a--i
Fig. 11ai. The restored results of the fingerprint image in Fig. 7 from different selected numbers of singular values a The result from k 6 be The results from different selected numbers k-1, k-2, k-3 and k-4, respectively f i The results from different selected numbers k1, k2, k3 and k4, respectively
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• ??a?????6?????????????,?be????5,4,3,2????????????
  ???,??????????????,b,c????????????,??d?e??????????
  ??f?i??????7,8,9,10.??????????????,???????????????
  ??,?????k-2??????????????,

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???????

• SVD??????????????????????????????????6b???????????
  ???????????????1,2,3,4,5?

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• Fig. 12af. The restored results of the scratch
  image in Fig. 6 from various rotation angles a
  The result from the original image bf The
  results from the images with 1?-, 2?-, 3?-, 4?-
  and 5?-rotation, respectively
• ?12?bf??????1?5???,a??????,????,?????2???,??????
  ,????2??,??????????????????????,??2??????????????
  ,????2????????????????

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??

• TFT??????????????????,?????????????????,??????????
  ???????TFT???????????????????LCD?????,?????TFT????
  ?????,???????-??????????????????????,?????????????
  ???????,??????????????