JP2003329612A - Test method of object to be tested - Google Patents
Test method of object to be testedInfo
- Publication number
- JP2003329612A JP2003329612A JP2002141652A JP2002141652A JP2003329612A JP 2003329612 A JP2003329612 A JP 2003329612A JP 2002141652 A JP2002141652 A JP 2002141652A JP 2002141652 A JP2002141652 A JP 2002141652A JP 2003329612 A JP2003329612 A JP 2003329612A
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- Prior art keywords
- pattern
- color
- light
- image
- inspection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
Description
【０００１】[0001]
【発明の属する技術分野】本発明は、被検査物の検査方
法に関し、特に自動車もしくは建築物の窓等に使用され
るガラス板、またはブラウン管のガラスパネル等に生じ
た光学的欠陥（異物等の遮光性欠陥、透明体の局所的な
屈折異常による微小な欠陥、鏡面体表面の局所的な凹凸
等による反射異常欠陥）の検査に用いられる被検査物の
検査方法に関する。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method of inspecting an object to be inspected, and particularly to an optical defect (for example, foreign matter, etc.) generated in a glass plate used for windows of automobiles or buildings, a glass panel of a cathode ray tube or the like. The present invention relates to a method for inspecting an object to be inspected, which is used for inspecting a light-shielding defect, a minute defect due to local refraction abnormality of a transparent body, and a reflection abnormality defect due to local unevenness of a mirror surface.
【０００２】[0002]
【従来の技術】従来、ガラス板等の透明板状体の欠陥
は、検査員の目視による検査および識別が実施されるこ
とが多かった。このような検査を自動化する手法とし
て、カラーの照明およびカラーのカメラを用いたもの
が、特開平９−５２５３号公報（以下、文献１という）
および特開２００１−５６２９７公報（以下、文献２と
いう）に開示されている。文献１には、被検査物上面の
異なる角度から異なる色の照明を行い、検査対象である
凸部分の反射光に対する各色の成分における特徴を比較
して、欠陥の検査と識別を行うことが開示されている。
また、文献２には、平面状の欠陥を検査するために、被
検査物上面から黄色の正反射光をあて、同時に異なる角
度から青色の光線を当てることにより、検査および識別
を行うことが開示されている。2. Description of the Related Art Conventionally, defects of transparent plate-like bodies such as glass plates have often been visually inspected and identified by an inspector. As a method for automating such an inspection, a method using color illumination and a color camera is disclosed in Japanese Patent Laid-Open No. 9-5253 (hereinafter referred to as Document 1).
And Japanese Patent Laid-Open No. 2001-56297 (hereinafter referred to as Document 2). It is disclosed in Document 1 that different colors are illuminated from different angles on the upper surface of the object to be inspected, and the characteristics of the components of each color with respect to the reflected light of the convex portion to be inspected are compared to inspect and identify the defect. Has been done.
Further, Document 2 discloses that in order to inspect a planar defect, yellow specularly reflected light is applied from the top surface of the object to be inspected, and at the same time, blue light rays are applied from different angles to perform inspection and identification. Has been done.
【０００３】一方、特開平８−６１９３０号公報（以
下、文献３という）には、三次元形状およびその欠陥の
検出を目的とし、カラーパタン照明とカラーカメラを用
いた検査方法が開示されている。すなわち、この文献３
にはパタンの色と被検査物表面の角度を対応させ、三次
元形状の計測を行うことが開示されている。On the other hand, Japanese Patent Application Laid-Open No. 8-61930 (hereinafter referred to as Document 3) discloses an inspection method using a color pattern illumination and a color camera for the purpose of detecting a three-dimensional shape and its defects. . That is, this document 3
Discloses that the three-dimensional shape is measured by making the color of the pattern correspond to the angle of the surface of the inspection object.
【０００４】さらに一方、ステレオ法と呼ばれ、複数カ
メラの視差を利用して被検査物の三次元位置を測定する
手法が広く知られており、それを利用した検査機が用い
られることもある。On the other hand, a method called the stereo method, which measures the three-dimensional position of an object to be inspected by utilizing the parallax of a plurality of cameras, is widely known, and an inspection machine using it is sometimes used. .
【０００５】[0005]
【発明が解決しようとする課題】しかしながら、上記文
献１〜３に開示されている従来技術では、平面上あるい
はその上にある凸形状の検査を行うためになされたもの
であり、全て反射光による検査を前提としている。仮に
これらを、透過検査へ適用したとしても、文献１の発明
に関しては、暗視野照明として屈折異常欠陥の検出およ
び屈折の度合いの測定は可能だが、Ｒ（赤）、Ｇ（緑）
およびＢ（青）の各画像中に明視野がないため、遮光性
欠陥の検査が難しく、欠陥の識別をする上で重要な大き
さなどの欠陥形状に関する情報を得るのが難しい。ま
た、文献３の発明に関しては、屈折異常の程度を測定す
ることは可能であるが、文献１の発明同様に、遮光性の
欠陥の検査および欠陥形状の情報を得るのが難しい。However, the conventional techniques disclosed in the above-mentioned Documents 1 to 3 are intended to inspect a flat surface or a convex shape on the flat surface, and are all based on reflected light. Inspected. Even if these are applied to the transmission inspection, with respect to the invention of Document 1, it is possible to detect a refractive error defect and measure the degree of refraction as dark field illumination, but R (red), G (green)
Since there is no bright field in each image of B and B (blue), it is difficult to inspect the light-shielding defect, and it is difficult to obtain information about the defect shape such as the size that is important for identifying the defect. Further, with respect to the invention of Document 3, although it is possible to measure the degree of refractive error, it is difficult to inspect the light-shielding defect and obtain information on the defect shape as in the invention of Document 1.
【０００６】一方、文献２の発明に関しては、Ｂは暗視
野画像、ＲとＧは明視野画像となり、遮光性の欠陥の検
査も可能で、欠陥形状の情報も得られるが、ＲとＧの画
像は同一の照明系下の画像となり、欠陥部分の色に違い
がある場合は、欠陥の識別に役立つが、屈折異常の程度
の違いを全く評価できず、透明体の欠陥候補の種類を識
別することは困難である。On the other hand, with respect to the invention of Document 2, B is a dark field image and R and G are bright field images. It is possible to inspect for light-shielding defects and obtain defect shape information. The image becomes an image under the same illumination system, and when the color of the defect part is different, it is useful for identifying the defect, but the difference in the degree of refractive error cannot be evaluated at all, and the type of defect candidate of the transparent body is identified. Is difficult to do.
【０００７】さらに一方、ステレオ法による検査は、形
状や段差の不良の検査に限られる。On the other hand, the inspection by the stereo method is limited to the inspection of defects in shape and steps.
【０００８】本発明は、上記課題を解決するためになさ
れたものであり、ガラス板等の透明体または鏡面体に生
じた欠陥候補の種類を識別できる被検査物の検査方法を
提供することを目的とする。The present invention has been made to solve the above problems, and it is an object of the present invention to provide a method for inspecting an object to be inspected, which is capable of identifying the type of a defect candidate generated in a transparent body such as a glass plate or a mirror surface body. To aim.
【０００９】[0009]
【課題を解決するための手段】このような目的を達成す
るため本発明は、光源により被検査物に対して光を照射
し、その反射光または透過光を撮像し、この撮像により
得られた画像をＲ（赤）、Ｇ（緑）およびＢ（青）の各
画像に分離し、分離した各画像の輝度分布に基づいて前
記被検査物の欠陥を検査する方法において、光の３原色
の一つである第１の色と光の３原色の一つである第２の
色との混色からなる第１のパタンと、光の３原色の一つ
である第３の色と前記第１の色との混色からなりかつ前
記第１のパタンの両側にそれぞれ隣接する第２および第
３のパタンとを前記光源の発光面に設け、前記被検査物
を透過した前記第１のパタン、または前記被検査物に反
射された前記第１のパタンの何れかを撮像し、この撮像
した画像を解析することにより、前記被検査物に存在す
る欠陥候補を抽出することを特徴とする被検査物の検査
方法を提供する。In order to achieve such an object, the present invention has been obtained by irradiating an object to be inspected with light by a light source, capturing the reflected light or transmitted light thereof, and obtaining the image. In the method of separating an image into R (red), G (green) and B (blue) images and inspecting the defect of the inspection object based on the brightness distribution of each separated image, the three primary colors of light are used. A first pattern composed of a mixture of a first color which is one and a second color which is one of the three primary colors of light, and a third color which is one of the three primary colors of light and the first The second pattern and the third pattern that are mixed with the first pattern and that are adjacent to both sides of the first pattern are provided on the light emitting surface of the light source, and the first pattern that has passed through the inspection object, or An image of any of the first patterns reflected by the inspection object is captured, and the captured image is analyzed. It allows to provide an inspection method of an inspection object and extracting a defect candidate exists in the inspection object.
【００１０】また、前記第１のパタンは、Ｇ（緑）およ
びＢ（青）の混色からなるパタンであり、前記第２およ
び第３のパタンは、Ｇ（緑）およびＲ（赤）の混色から
なるパタンであることが好ましい。The first pattern is a pattern composed of a mixture of G (green) and B (blue), and the second and third patterns are a mixture of G (green) and R (red). It is preferable that the pattern consists of
【００１１】また、前記第１のパタンは、Ｇ（緑）およ
びＲ（赤）の混色からなるパタンであり、前記第２およ
び第３のパタンは、Ｇ（緑）およびＢ（青）の混色から
なるパタンであることが好ましい。The first pattern is a pattern composed of a mixture of G (green) and R (red), and the second and third patterns are a mixture of G (green) and B (blue). It is preferable that the pattern consists of
【００１２】また、Ｒ（赤）、Ｇ（緑）およびＢ（青）
の各画像における欠陥候補の中心領域、前記欠陥候補の
輪郭近傍の内側領域、および前記欠陥候補の輪郭近傍の
外側領域における各画像信号を取得し、これらの画像信
号の特徴量に基づいて前記欠陥候補の種類を識別するこ
とが好ましい。Also, R (red), G (green) and B (blue)
Each image signal in the central region of the defect candidate in each image, the inner region near the contour of the defect candidate, and the outer region near the contour of the defect candidate is acquired, and the defect is determined based on the feature amount of these image signals. It is preferable to identify the type of candidate.
【００１３】また、視差のある複数の画像を取得し、こ
れら視差のある複数の画像に基づいて前記欠陥候補の三
次元位置を求め、前記欠陥候補が被検査物の表面または
内部の何れにあるかを判定することが好ましい。Further, a plurality of images with parallax are acquired, the three-dimensional position of the defect candidate is obtained based on the plurality of images with parallax, and the defect candidate is present on either the surface or the inside of the inspection object. It is preferable to determine whether or not
【００１４】さらに、前記被検査物は、透明体であり、
この透明体の表面および裏面に光学的にマーキングを施
し、測定されたそれらの三次元位置を基準として欠陥候
補が前記透明体の内部、前記透明体の表面、または前記
透明体の裏面の何れにあるかを判定することが好まし
い。Further, the object to be inspected is a transparent body,
Optically marking the front surface and the back surface of this transparent body, the defect candidate based on the measured three-dimensional position of the inside of the transparent body, the surface of the transparent body, or on the back surface of the transparent body. It is preferable to determine whether there is.
【００１５】[0015]
【発明の実施の形態】以下、本発明の実施の形態に関し
て図面を参照して説明する。図１は、被検査物の欠陥検
査方法を実施するための検査装置の一実施形態を示す構
成図であり、同図（ａ）は側面図、同図（ｂ）はＡ−
Ａ’線矢視図を示す。同図（ａ）に示すように、被検査
物であるガラス板２の裏面に対して光源３から光を照射
し、その透過光をガラス板２の主表面側からカラーのラ
インカメラ１により撮像する。すなわち、同図（ｂ）に
示すように、ガラス板Ｇを図の矢印方向に搬送しながら
撮像する。そして、撮像された画像信号は演算装置４に
送られ、演算装置４により画像処理に必要な各種演算処
理が実行される。また、光源３は、箱状の筐体内に蛍光
灯を設置して作られた面状の拡散照明であり、その発光
面には後述のカラーパタンが設けられている。ラインカ
メラ１は、一次元ＣＣＤセンサを備えたカメラであり、
撮像した画像を光の三原色であるＲ（赤）、Ｇ（緑）お
よびＢ（青）の各画像に分離することができる。BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings. 1A and 1B are configuration diagrams showing an embodiment of an inspection apparatus for performing a defect inspection method for an object to be inspected. FIG. 1A is a side view and FIG. 1B is A-.
The A'line arrow view is shown. As shown in FIG. 3A, the back surface of the glass plate 2 as the inspection object is irradiated with light from the light source 3, and the transmitted light is imaged from the main surface side of the glass plate 2 by the color line camera 1. To do. That is, as shown in FIG. 4B, the glass plate G is imaged while being conveyed in the direction of the arrow. Then, the captured image signal is sent to the arithmetic unit 4, and the arithmetic unit 4 executes various arithmetic processes necessary for image processing. Further, the light source 3 is a planar diffused illumination made by installing a fluorescent lamp in a box-shaped housing, and a color pattern to be described later is provided on its light emitting surface. The line camera 1 is a camera equipped with a one-dimensional CCD sensor,
The captured image can be separated into R (red), G (green), and B (blue) images that are the three primary colors of light.
【００１６】図２は、光源３の発光面に設けられたカラ
ーパタンを示す平面図である。同図に示すように、パタ
ン３ａはストライプ状の３つの領域を有し、中央に位置
する被検査領域３ａ−２は水色に着色され、それに隣接
する周辺領域３ａ−１および３ａ−３は黄色に着色さ
れ、ラインカメラ１の視野は被検査領域３ａ−２を縦断
する一点鎖線上に合わせてある。なお、パタン３ａは、
図２のように着色したフィルムを光源３の発光面に貼付
したものであり、例えば透明樹脂フィルムにアルミナゾ
ル等（旭硝子（株）社製のピクトリコなど）を塗布し、
その上にインクジェット印刷することにより作られる。FIG. 2 is a plan view showing a color pattern provided on the light emitting surface of the light source 3. As shown in the figure, the pattern 3a has three stripe-shaped regions, the inspection region 3a-2 located at the center is colored in light blue, and the peripheral regions 3a-1 and 3a-3 adjacent thereto are yellow. The field of view of the line camera 1 is aligned with the alternate long and short dash line that crosses the inspection region 3a-2. The pattern 3a is
A colored film as shown in FIG. 2 is attached to the light emitting surface of the light source 3. For example, a transparent resin film is coated with alumina sol or the like (Pictrico manufactured by Asahi Glass Co., Ltd.),
It is made by inkjet printing on it.
【００１７】ここで、本発明の検査原理について説明す
る。被検査領域３ａ−２は、ＧとＢの混色である水色に
着色され、Ｒの光を一切含まない。一方、周辺領域３ａ
−１および３ａ−３は、ＧとＲの混色である黄色に着色
され、Ｂの光を一切含まない。したがって、ラインカメ
ラ１によって撮像された画像をＲ、ＧおよびＢの各色に
分離し、ガラス板２に欠陥が一切ないものと仮定する
と、Ｒ画像は暗視野（輝度が最低の状態）、ＧおよびＢ
画像は明視野（輝度が最大の状態）となる。Now, the inspection principle of the present invention will be described. The region to be inspected 3a-2 is colored light blue, which is a mixture of G and B, and does not include R light at all. On the other hand, the peripheral area 3a
-1 and 3a-3 are colored yellow, which is a mixture of G and R, and do not include any B light. Therefore, assuming that the image captured by the line camera 1 is separated into R, G, and B colors and that the glass plate 2 has no defects, the R image has a dark field (state where brightness is the lowest), G and B
The image is in bright field (state of maximum brightness).
【００１８】ところが、ガラス板２の被検査領域３ａ−
２に相当する領域に、気泡や傷、凹凸等の欠陥が存在し
た場合、ガラス板２内の局所的な屈折異常により、周辺
領域３ａ−１または３ａ−３中のＲの光が被検査領域３
ａ−２に混入するため、上記分離したＲ画像中に欠陥が
映し出される。また、異物の混入等により光の透過が遮
られた場合、ＧおよびＢ画像の明視野中に局所的に光を
透過しない部分が撮像されることになる。However, the inspection area 3a-of the glass plate 2
When there are defects such as bubbles, scratches, and unevenness in the region corresponding to 2, the R light in the peripheral region 3a-1 or 3a-3 causes the inspected region due to the local refractive error in the glass plate 2. Three
Since it is mixed in a-2, a defect appears in the separated R image. Further, when the transmission of light is blocked due to the inclusion of foreign matter or the like, a portion that does not locally transmit light is imaged in the bright field of the G and B images.
【００１９】なお、被検査領域３ａ−２をＲとＧの混
色、周辺領域３ａ−１および３ａ−３をＧとＢの混色と
してもよく、さらにその他の組み合わせでも上記同様の
効果が得られる。また、透明体中の透明な異物や、微小
な表面凹凸などの比較的弱い屈折異常欠陥に対する検出
感度は、被検査領域３ａ−２の幅（短辺の長さ）が狭い
ほど高くなる。The region 3a-2 to be inspected may be a color mixture of R and G, the peripheral regions 3a-1 and 3a-3 may be a color mixture of G and B, and the same effect as above can be obtained by other combinations. Further, the detection sensitivity for a transparent foreign substance in a transparent body or a relatively weak refractive abnormality defect such as minute surface irregularities becomes higher as the width (length of the short side) of the inspection region 3a-2 becomes narrower.
【００２０】これらの欠陥と透明体中の気泡等の強い屈
折異常欠陥の識別をする場合、Ｒの画像に相当する暗視
野とＢの画像に相当する周辺部に暗い部分がある明視野
においては、どちらの種類の欠陥でも信号が得られる
が、Ｇの画像においては、強い屈折異常では信号が得ら
れるのに対し、弱い屈折異常では信号が得られない。し
たがって、Ｇ信号の有無という特徴により両者を区別で
きる。When distinguishing these defects from strong refractive error defects such as bubbles in a transparent body, in the dark field corresponding to the R image and the bright field having a dark portion in the peripheral portion corresponding to the B image, A signal can be obtained with either type of defect, but in the G image, a signal can be obtained with strong refractive error, whereas a signal cannot be obtained with weak refractive error. Therefore, the two can be distinguished by the characteristic of the presence or absence of the G signal.
【００２１】図３は、被検査物の表面検査装置を示す側
面図である。同図に示すように、表面が鏡面状の被検査
物２ａの表面を検査する場合、光源３の反射光を撮像す
るとよい。また、撮像手段として、カラーのエリアカメ
ラ、あるいは色をＲＧＢに分解して出力できる点状のセ
ンサを走査する構成を用いてもよい。FIG. 3 is a side view showing a surface inspection device for an object to be inspected. As shown in the figure, when inspecting the surface of the inspection object 2a whose surface is a mirror surface, the reflected light of the light source 3 may be imaged. Further, as the image pickup means, a color area camera or a structure in which a dot sensor capable of separating the color into RGB and outputting the color may be used.
【００２２】次に、欠陥候補の種類の識別方法について
説明する。ガラス板には種々の欠陥が発生し、例えば肉
厚が局所的に変化してレンズ効果を生ずる欠陥、表面に
形成された傷や凹凸、内部に混入した異物や気泡等があ
る。そして、これらの欠陥候補の種類を、図１に示した
検査装置により画像を分析すると、それぞれ固有の特徴
を有する。Next, a method of identifying the types of defect candidates will be described. Various defects are generated on the glass plate, and for example, there are defects in which the wall thickness locally changes to cause a lens effect, scratches and irregularities formed on the surface, foreign substances and bubbles mixed in the inside, and the like. Then, when the images of these types of defect candidates are analyzed by the inspection apparatus shown in FIG. 1, they have their own characteristics.
【００２３】例えば、Ｒ画像における気泡欠陥は、屈折
により中心部は光るが周辺部は光らない。それに対し
て、異物等の遮光性欠陥は、遮光により中心部は光らな
いのに対し、周辺部分は反射や散乱により光るといった
特徴を持つ。そこで、本実施の形態では欠陥候補の輪郭
に沿って環状に領域を分割することにより、領域毎の輝
度分布の違いに基づいて欠陥候補の種類を識別する。ま
た、ガラス板２に付着した単なる埃であるのか、ガラス
板２に生じた欠陥であるのかを識別することができる。For example, a bubble defect in an R image shines in the central portion but not in the peripheral portion due to refraction. On the other hand, a light-shielding defect such as a foreign substance has a characteristic that the central portion does not shine due to light shielding, but the peripheral portion shines due to reflection or scattering. In view of this, in the present embodiment, the type of defect candidate is identified based on the difference in the luminance distribution for each region by dividing the region in an annular shape along the contour of the defect candidate. Further, it is possible to discriminate whether it is just dust adhering to the glass plate 2 or a defect generated in the glass plate 2.
【００２４】図４は、欠陥候補の一例を示す平面図であ
る。同図（ａ）は楕円形の欠陥候補５、同図（ｂ）は長
方形の欠陥候補６を示し、それぞれ欠陥候補の中心領域
５ａ、６ａ、輪郭の内側領域５ｂ、６ｂ、輪郭の近傍領
域５ｃ、６ｃの３領域を有する。欠陥候補の輪郭は、領
域５ｂと５ｃ（６ｂと６ｃ）との境界に位置する。各領
域の幅は、画像における輪郭の画素数で決めることがで
きる。FIG. 4 is a plan view showing an example of defect candidates. The figure (a) shows the elliptical defect candidate 5, the figure (b) shows the rectangular defect candidate 6, and the center regions 5a and 6a of the defect candidate, the inner regions 5b and 6b of the contour, and the neighboring region 5c of the contour, respectively. , 6c. The contour of the defect candidate is located at the boundary between the regions 5b and 5c (6b and 6c). The width of each region can be determined by the number of contour pixels in the image.
【００２５】また、分割する領域の数は、識別した欠陥
候補の種類やその画像上での大きさ等に応じて最適なも
のを用い、各領域における特徴量として、領域内の信号
のヒストグラムより求めた最大値、最小値、平均値、ま
たは標準偏差等のうち、必要なものを用いればよい。例
えば、図４のように領域を、欠陥候補の中心領域、輪郭
の内側領域および輪郭の近傍領域の３領域に分割し、各
領域におけるＲＧＢそれぞれの画像の信号を用いれば、
９つのヒストグラム情報に基づいた特徴量を用いること
ができ、それにより欠陥候補の種類を識別することがで
きる。The number of areas to be divided is optimum according to the type of the identified defect candidate and its size on the image, and the feature amount in each area is obtained from the histogram of the signals in the area. Of the maximum value, the minimum value, the average value, the standard deviation, and the like that have been obtained, the necessary one may be used. For example, as shown in FIG. 4, if the area is divided into three areas, that is, the central area of the defect candidate, the area inside the contour, and the area near the contour, and the signals of the RGB images in each area are used,
The feature amount based on the nine pieces of histogram information can be used, and thus the type of defect candidate can be identified.
【００２６】図５は、本発明による被検査物の欠陥検査
方法を実施するための検査装置の別の実施形態を示す構
成図である。画像を三原色であるＲ（赤）、Ｇ（緑）お
よびＢ（青）の画像に分離しうる撮像手段であるカラー
のエリアカメラ１ａ、１ａにより、光源３から発せられ
た光が被検査物２を透過した像を取得する。撮像により
得られた信号は図示しない演算装置に送られ、画像処理
に必要な各種演算処理が実施される構成となっている。
なお、エリアカメラ１ａ、１ａの代わりに、カラーのラ
インセンサカメラまたは走査可能な点状のセンサを用い
てもよい。FIG. 5 is a block diagram showing another embodiment of the inspection apparatus for carrying out the defect inspection method for the inspection object according to the present invention. Light emitted from the light source 3 is emitted from the light source 3 by the color area cameras 1a and 1a which are image pickup means capable of separating an image into three primary colors R (red), G (green) and B (blue). Acquire an image transmitted through. The signal obtained by imaging is sent to an arithmetic unit (not shown), and various arithmetic processes necessary for image processing are performed.
A color line sensor camera or a scannable dot sensor may be used instead of the area cameras 1a and 1a.
【００２７】また、同図に示すように、エリアカメラ１
ａ、１ａは、異なる方向からほぼ同一の視野を撮像して
いるので、各カメラで得られる画像間に視差が生じる。
視差のある画像間における同一点の座標のずれより、対
称点が存在する図４中のＸ、Ｚ座標を推定することが可
能となる。一般に視差画像間の同一点の特定は容易でな
いが、本実施形態においては、先に延べたように欠陥候
補に対して多くの特徴量を得ることができ、複数画像間
での同一の欠陥候補の特定が容易になる。Further, as shown in FIG.
Since a and 1a capture almost the same visual field from different directions, parallax occurs between the images obtained by the cameras.
It is possible to estimate the X and Z coordinates in FIG. 4 where the symmetric point exists from the shift of the coordinates of the same point between the images having parallax. In general, it is not easy to identify the same point between parallax images, but in the present embodiment, many feature amounts can be obtained for defect candidates as previously described, and the same defect candidate between multiple images can be obtained. Will be easy to identify.
【００２８】透明体の欠陥検査では、欠陥候補の位置が
透明体の表面もしくは裏面、または肉厚内の何れにある
のかを知ることが重要な場合がある。本実施形態は欠陥
候補のＺ座標を推定でき、欠陥候補が非検査物の表面ま
たは内部の何れにあるかを三次元位置から判断できる。In the defect inspection of the transparent body, it may be important to know whether the position of the defect candidate is on the front surface or the back surface of the transparent body or within the thickness. In this embodiment, the Z coordinate of the defect candidate can be estimated, and whether the defect candidate is on the surface or inside of the non-inspection object can be determined from the three-dimensional position.
【００２９】被検査物が曲面や不規則な形状である、あ
るいはＺ方向の位置決めが不十分であるために、撮像系
を基準としたＺ座標のみで欠陥候補が表面、裏面、また
は肉厚内の何れにあるかを特定できない場合もある。こ
れに対して本実施形態では、表面および裏面に基準とな
るマーキングを施す。そして、これらのマーキングに基
づいて、被検査物の表面および裏面のＺ座標を同時に測
定し、欠陥候補のＺ座標をそれらと比較することによ
り、欠陥候補が表面、裏面または肉厚内の何れにあるか
を特定する。Since the object to be inspected has a curved surface or an irregular shape or the positioning in the Z direction is insufficient, the defect candidate is detected on the front surface, the back surface, or within the wall thickness only by the Z coordinate based on the imaging system. In some cases, it may not be possible to specify which of the two. On the other hand, in the present embodiment, reference markings are provided on the front surface and the back surface. Then, based on these markings, the Z coordinates of the front surface and the back surface of the object to be inspected are simultaneously measured, and the Z coordinates of the defect candidate are compared with them, so that the defect candidate is located on the front surface, the back surface, or within the thickness. Identify if there is.
【００３０】マーキングを施すための具体的な手段とし
ては様々考えられる。塗料などによるマーキングは検査
後に洗浄が必要になるため、レーザ光を用いるとよい。
すなわち、レーザ７により発光したレーザ光をガラス板
２に照射し、ガラス板２の主表面および裏面に散乱によ
る輝点（散乱スポット８，９）を発生させ、輝点の位置
を測定することにより、ガラス板２の表面および裏面の
Ｚ座標を求める。Various concrete means for marking can be considered. Laser marking is preferably used because marking with paint or the like requires cleaning after inspection.
That is, by irradiating the glass plate 2 with laser light emitted from the laser 7 to generate bright spots (scattering spots 8 and 9) due to scattering on the main surface and the back surface of the glass plate 2, and measuring the position of the bright spot. , Z-coordinates of the front surface and the back surface of the glass plate 2 are obtained.
【００３１】なお、マーキングのためのレーザ光として
赤い光線を用いた場合、ガラス板２の主表面および裏面
での散乱光も赤い光となり、Ｒの画像でより強い輝点と
して測定される。カメラ画像の背景となる被検査領域３
ａ−２はＧとＢの混色であるため、Ｒの画像においては
暗視野となり、表面および裏面の赤い点は、暗視野中で
明るい輝点となり、Ｒ画像での検出が容易となる。When a red light beam is used as the laser light for marking, the scattered light on the main surface and the back surface of the glass plate 2 also becomes red light, which is measured as a stronger bright spot in the R image. Inspected area 3 which is the background of the camera image
Since a-2 is a mixed color of G and B, it becomes a dark field in the R image, and the red dots on the front surface and the back surface become bright bright points in the dark field, which facilitates detection in the R image.
【００３２】一方、遮光性の欠陥候補等は、Ｒ画像では
輝点とならずに検出できない、または一部が光るだけで
輪郭が抽出できない場合がある。これに対して、Ｇまた
はＢの画像では比較的容易に輪郭が検出できる。本実施
形態においては、ＧまたはＢの画像を用いて輪郭を抽出
し形状の特徴量を算出するとともに、中心部や周辺部な
どの領域を特定し、その座標位置に基づいてＲ、Ｇおよ
びＢの各画像における信号特徴量を算出している。On the other hand, the light-shielding defect candidate or the like may not be detected because it does not become a bright spot in the R image, or the contour may not be extracted because only a part of it shines. On the other hand, the contour can be detected relatively easily in the G or B image. In the present embodiment, the contour is extracted by using the G or B image to calculate the feature amount of the shape, the area such as the central portion and the peripheral portion is specified, and R, G and B are identified based on the coordinate positions. The signal feature amount in each image is calculated.
【００３３】[0033]
【実施例】以下、本発明の実施例に関して図面を参照し
て説明する。Embodiments of the present invention will be described below with reference to the drawings.
【００３４】〔実施例１〕本実施例では図１の装置を用
い、パタン３ａとして、被検査領域近傍に相当するＧと
Ｒの混色である黄色、周辺領域にＧとＢの混色である水
色を用いた。撮像された画像は、ＲＧＢのそれぞれの画
像に分離され、Ｒの画像は周辺に暗い部分をもつ状態で
の明視野画像、Ｇの画像は背景の広い範囲が明るい状態
での明視野画像、Ｂの画像は周辺に明るい部分をもつ状
態での暗視野画像となる。[Embodiment 1] In the present embodiment, the apparatus of FIG. 1 is used, and as the pattern 3a, yellow which is a color mixture of G and R corresponding to the vicinity of the inspection area, and light blue which is a color mixture of G and B in the peripheral area. Was used. The captured image is separated into RGB images, the R image is a bright field image in a state where there is a dark portion in the periphery, the G image is a bright field image in a state where a wide background is bright, and the B image is a bright field image. The image of is a dark field image with a bright portion in the periphery.
【００３５】本実施例においては、２０００画素の１次
元カラーラインセンサを用いて、画素分解能０．０８ｍ
ｍ、絞りをＦ１１、被検ガラスと照明パタンとの距離を
１００ｍｍという条件下で、図３のハッチング部に相当
する幅を２０ｍｍとして、板厚が１５ｍｍ程度のガラス
板の検査を行った。また、ガラス板の搬送速度は１００
ｍｍ／秒、ラインセンサで１ラインを取り込むのに要す
る時間を０．８ミリ秒とした。In the present embodiment, a 2000-pixel one-dimensional color line sensor is used, and the pixel resolution is 0.08 m.
The glass plate having a thickness of about 15 mm was inspected under the condition of m, the aperture was F11, and the distance between the test glass and the illumination pattern was 100 mm with the width corresponding to the hatched portion in FIG. 3 being 20 mm. Further, the transportation speed of the glass plate is 100.
mm / sec, the time required to capture one line with the line sensor was set to 0.8 msec.
【００３６】〔実施例２〕本実施例では、図５の装置を
用い、パタン３ａとして、被検査領域近傍に相当するＧ
とＢの混色である水色、周辺領域にＧとＲの混色である
黄色を用いた。撮像された画像は、Ｒ、ＧおよびＢの各
画像に分離され、Ｒの画像は周辺に明るい部分をもつ状
態での暗視野画像、Ｇの画像は背景の広範囲が明るい状
態である明視野画像、Ｂの画像は周辺に暗い部分をもつ
明視野画像となる。本実施例においては、市販の２次元
カラーカメラ２台を用いて同一平面上に設置し、エリア
カメラ１ａ，１ａにより得られる像に関して、図５に示
すＹ座標を一致させている。また、画素分解能０．０２
ｍｍ、絞りをＦ１１、被検ガラスと照明パタンとの距離
を１００ｍｍという条件下で、図３のハッチング部に相
当する幅を１５ｍｍとして、板厚が１５ｍｍ程度のガラ
ス板の検査を行った。また、マーキングを施すためのレ
ーザ７には赤色の半導体レーザを、図５に示すＺ軸に対
してＹ軸の方向に傾斜させて設置した。[Embodiment 2] In this embodiment, the apparatus shown in FIG. 5 is used, and the pattern 3a corresponds to the area G near the inspection area.
Light blue, which is a mixed color of B and B, and yellow, which is a mixed color of G and R, are used in the peripheral region. The captured image is separated into R, G, and B images. The R image is a dark-field image with a bright portion in the periphery, and the G image is a bright-field image with a wide background in a bright state. , B images are bright-field images having a dark portion in the periphery. In the present embodiment, two commercially available two-dimensional color cameras are used and they are installed on the same plane, and the Y-coordinates shown in FIG. 5 are matched for the images obtained by the area cameras 1a and 1a. Also, pixel resolution 0.02
mm, the aperture was F11, and the distance between the test glass and the illumination pattern was 100 mm, the width corresponding to the hatched portion in FIG. 3 was 15 mm, and the glass plate having a thickness of about 15 mm was inspected. Further, a red semiconductor laser was installed as the marking laser 7 inclining in the Y-axis direction with respect to the Z-axis shown in FIG.
【００３７】レーザ７によりレーザ光を照射すると、ガ
ラス表裏面の微細な凹凸や汚れによる散乱のため、レー
ザ光が照射された部分は発光する。これらの発光は赤い
光であるため、Ｒ画像においてこの散乱スポット８，９
の画像上の位置を検出する。Ｒ画像は背景が暗い暗視野
となっているため、Ｒの明るい部分としてＳ/Ｎで１０
０以上と安定したスポットの検出が実現される。また散
乱スポット８，９の位置は、レーザ７の取り付け位置と
角度、カメラ１ａと被検査物であるガラス板２とのおお
よその距離および厚さより想定される座標から探索し、
Ｒで周囲よりある値以上の差を持って明るくなってお
り、かつＧとＢのどちらでも周囲より暗くなっていない
という条件により特定している。When the laser light is irradiated by the laser 7, the portion irradiated with the laser light emits light due to scattering by fine irregularities on the front and back surfaces of the glass and dirt. Since these emitted lights are red lights, the scattered spots 8, 9 in the R image are
Position on the image. The R image has a dark background with a dark background, so the S / N is 10 as the bright part of R.
A stable spot detection of 0 or more is realized. Further, the positions of the scattering spots 8 and 9 are searched from the coordinates assumed from the mounting position and angle of the laser 7, the approximate distance between the camera 1a and the glass plate 2 as the inspection object, and the thickness,
It is specified by the condition that R is brighter than the surroundings with a certain value or more and that neither G nor B is darker than the surroundings.
【００３８】散乱スポット８，９の重心座標を用い、簡
易的にｂ・（ｘ１−ｘ２）／（ａ-ｘ１＋ｘ２）の式に
よりカメラからガラス表面および裏面までの距離を計算
する。ここで、ａは２台のカメラの撮像レンズの中心間
の距離、ｂは撮像レンズの中心と被検査物の表面を想定
した基準面までの距離であり、ｘ１、ｘ２はそれぞれの
カメラで撮像された画像上の測定対象点の座標を実空間
の位置に変換する倍率の相当する値をかけたものであ
る。この実施例においては、ａ＝６０ｍｍ、ｂ＝２６５
ｍｍとしている。表面の粗度の違いによるばらつきの影
響を含めても、表裏面のｚ方向の位置の測定精度は±１
ｍｍ以内の精度で達成されている。Using the barycentric coordinates of the scattered spots 8 and 9, the distance from the camera to the front and back surfaces of the glass can be calculated simply by the formula b · (x 1 −x 2 ) / (a−x 1 + x 2 ). . Here, a is the distance between the centers of the imaging lenses of the two cameras, b is the distance between the centers of the imaging lenses and the reference plane assuming the surface of the object to be inspected, and x 1 and x 2 are the respective cameras. It is obtained by multiplying the coordinates of the measurement target point on the image captured in (1) by the corresponding value of the magnification for converting into the position in the real space. In this example, a = 60 mm, b = 265
mm. Even including the influence of variations due to the difference in surface roughness, the measurement accuracy of the z-direction position on the front and back surfaces is ± 1.
It has been achieved with an accuracy within mm.
【００３９】また、各カメラから得られたＧ画像とＲ画
像の差分画像を算出し、その値の大小により欠陥候補を
抽出する。それらのうちから表裏面のスポットに相当す
る部分は除外している。抽出された欠陥候補に対して
は、Ｒ画像により輪郭を抽出し、輪郭より内側へ欠陥候
補の径の３０％の幅に相当する画素数分の範囲の輪郭内
側部分、それよりも内側の中心部、輪郭より外側へ径の
３０％分の幅に相当する範囲の輪郭近傍部分の３つの領
域を特定し、ＲＧＢのそれぞれの画像における該当部分
の信号の平均値を算出する。また、各Ｒ画像の輪郭内の
領域の重心座標を算出し、欠陥候補の座標としている。Further, a difference image between the G image and the R image obtained from each camera is calculated, and defect candidates are extracted according to the magnitude of the value. Of these, the parts corresponding to the spots on the front and back surfaces are excluded. For the extracted defect candidate, a contour is extracted from the R image, and the inside portion of the contour within the range of the number of pixels corresponding to a width of 30% of the diameter of the defect candidate inside the contour, and the center inside the contour. Part, three regions outside the contour in the region near the contour corresponding to a width corresponding to 30% of the diameter are specified, and the average value of the signal of the corresponding portion in each of the RGB images is calculated. Further, the barycentric coordinates of the area within the contour of each R image are calculated and used as the coordinates of the defect candidate.
【００４０】２台のカメラで得られた画像の欠陥候補に
対して、ｙの値がある範囲で一致し、かつ特徴量が最も
一致するものを同一の欠陥候補であると特定し、その画
像上の座標より散乱スポット８，９のＺ位置を算出する
場合と同じ式により、欠陥候補のＺ位置を算出し、散乱
スポット８，９のＺ位置と比較することにより、欠陥候
補がガラスの表面、裏面あるいは肉厚内の何れにあるか
を判定する。The defect candidates of the images obtained by the two cameras are identified as the same defect candidate when the value of y matches within a certain range and the feature amount is the best match. By calculating the Z position of the defect candidate by the same formula as the case of calculating the Z position of the scattering spots 8 and 9 from the above coordinates and comparing it with the Z position of the scattering spots 8 and 9, the defect candidate becomes the surface of the glass. , The back surface or within the thickness is determined.
【００４１】また、Ｒ、ＧおよびＢの各画像において、
中心領域、輪郭の近傍領域の信号を輪郭の内側領域の信
号値で割った値、Ｒ画像およびＢ画像の中心領域の信号
をＧ画像の中心領域の信号で割った値、中心部領域の信
号の９つの特徴量および形状の特徴を表す長径、円型
度、対象性を加えた１２個の特徴量に関し、実験結果よ
りもとめた欠陥候補の種類ごとの分布を参照し、欠陥候
補の種類を識別する。In each of the R, G and B images,
A value obtained by dividing the signal in the central area and the area near the contour by the signal value in the area inside the contour, a value obtained by dividing the signal in the central area of the R and B images by the signal in the central area of the G image, and a signal in the central area For the 12 feature values including the major axis, the circularity, and the symmetry, which represent the 9 feature values and the shape feature, the defect candidate type is determined by referring to the distribution of each defect candidate type obtained from the experimental results. Identify.
【００４２】これにより、ガラス板中の気泡、表面の気
泡、ガラス板中の透明な異物、ガラス板中の異物、表面
の異物、表面の凹凸、表面の傷を識別することができ
る。Thus, it is possible to identify bubbles in the glass plate, bubbles on the surface, transparent foreign substances in the glass plate, foreign substances in the glass plate, foreign substances on the surface, surface irregularities, and scratches on the surface.
【００４３】[0043]
【発明の効果】以上説明したとおり本発明は、光の３原
色の一つである第１の色と光の３原色の一つである第２
の色との混色からなる第１のパタンと、光の３原色の一
つである第３の色と前記第１の色との混色からなりかつ
前記第１のパタンの両側に隣接する第２および第３のパ
タンとを光源の発光面に設け、前記被検査物を透過した
前記第２のパタン、または前記被検査物に反射された前
記第２のパタンの何れかを撮像し、撮像した画像を解析
することにより欠陥を検査する。As described above, according to the present invention, the first color, which is one of the three primary colors of light, and the second color, which is one of the three primary colors of light, are used.
A first pattern formed by mixing the first color and a third pattern that is one of the three primary colors of light, and a second pattern that is adjacent to both sides of the first pattern. And a third pattern are provided on the light emitting surface of the light source, and either the second pattern transmitted through the inspected object or the second pattern reflected by the inspected object is imaged and imaged. Inspect the defect by analyzing the image.
【００４４】すなわち、２色の単純なパタンでありなが
ら、微小凹凸欠点検査または形状測定を行うことがで
き、さらには検査または測定の高速化や高性能化を実現
できる。また、検査装置が簡単な構成で済むため、低価
格化、使用可能な対象の範囲の拡大ができる。さらに、
ガラス板中の気泡、表面の気泡、ガラス板中の透明な異
物、ガラス板中の異物、表面の異物、表面の凹凸、表面
の傷を識別することができる。That is, it is possible to perform the fine unevenness defect inspection or the shape measurement with a simple pattern of two colors, and also to realize the high speed and high performance of the inspection or measurement. Further, since the inspection device has a simple structure, it is possible to reduce the cost and expand the range of usable objects. further,
It is possible to identify bubbles in the glass plate, bubbles on the surface, transparent foreign substances in the glass plate, foreign substances in the glass plate, foreign substances on the surface, surface irregularities, and scratches on the surface.
【図１】（ａ）本発明の一実施形態を示す正面図、
（ｂ）Ａ−Ａ’線矢視図である。FIG. 1A is a front view showing an embodiment of the present invention,
(B) It is an AA 'line arrow line view.
【図２】カラーパタンを示す平面図である。FIG. 2 is a plan view showing a color pattern.
【図３】本発明のその他の実施形態を示す側面図であ
る。FIG. 3 is a side view showing another embodiment of the present invention.
【図４】本発明における欠陥候補の領域の分割を示す模
式図である。FIG. 4 is a schematic diagram showing division of defect candidate areas according to the present invention.
【図５】（ａ）本発明のその他の実施形態を示す正面
図、（ｂ）本発明のその他の実施形態を示す側面図、
（ｃ）Ｂ−Ｂ’線矢視図、（ｄ）Ｃ−Ｃ’線矢視図であ
る。5A is a front view showing another embodiment of the present invention, FIG. 5B is a side view showing another embodiment of the present invention, FIG.
(C) BB 'line arrow view, (d) CC' line arrow view.
１：ラインカメラ ２：ガラス板 ３：光源 ３ａ：カラーパタン ３ａ−１、３ａ−３：周辺領域 ３ａ−２：被検査領域 ４：演算装置 ５：楕円状欠陥 ６：矩形状欠陥 ７：レーザ ８：ガラス主表面における散乱スポット ９：ガラス裏面における散乱スポット 1: Line camera 2: Glass plate 3: Light source 3a: Color pattern 3a-1, 3a-3: peripheral area 3a-2: Inspected area 4: Computing device 5: Elliptical defect 6: Rectangular defect 7: Laser 8: Scattered spot on the glass main surface 9: Scattered spot on the back of the glass
───────────────────────────────────────────────────── フロントページの続き Ｆターム(参考） 2F065 AA04 AA49 AA61 BB01 BB15 BB22 BB27 CC14 CC25 FF02 FF09 FF42 GG03 GG04 GG18 HH02 HH12 HH13 HH15 JJ02 JJ03 JJ08 JJ09 JJ25 JJ26 QQ00 QQ21 QQ28 QQ41 QQ42 QQ43 2G051 AA42 AA90 AB01 AB06 AB07 BA08 BA10 BB05 BB07 CA03 CB01 CB02 DA06 DA15 EA08 EA17 EC01 EC02 ─────────────────────────────────────────────────── ─── Continued front page F term (reference) 2F065 AA04 AA49 AA61 BB01 BB15 BB22 BB27 CC14 CC25 FF02 FF09 FF42 GG03 GG04 GG18 HH02 HH12 HH13 HH15 JJ02 JJ03 JJ08 JJ09 JJ25 JJ26 QQ00 QQ21 QQ28 QQ41 QQ42 QQ43 2G051 AA42 AA90 AB01 AB06 AB07 BA08 BA10 BB05 BB07 CA03 CB01 CB02 DA06 DA15 EA08 EA17 EC01 EC02
Claims (6)
その反射光または透過光を撮像し、この撮像により得ら
れた画像をＲ（赤）、Ｇ（緑）およびＢ（青）の各画像
に分離し、分離した各画像の輝度分布に基づいて前記被
検査物の欠陥を検査する方法において、 光の３原色の一つである第１の色と光の３原色の一つで
ある第２の色との混色からなる第１のパタンと、光の３
原色の一つである第３の色と前記第１の色との混色から
なりかつ前記第１のパタンの両側にそれぞれ隣接する第
２および第３のパタンとを前記光源の発光面に設け、 前記被検査物を透過した前記第１のパタン、または前記
被検査物に反射された前記第１のパタンの何れかを撮像
し、この撮像した画像を解析することにより、前記被検
査物に存在する欠陥候補を抽出することを特徴とする被
検査物の検査方法。1. A light source irradiates an object to be inspected with light,
The reflected light or the transmitted light is imaged, the image obtained by this imaging is separated into R (red), G (green), and B (blue) images, and the image is separated based on the brightness distribution of the separated images. In a method for inspecting a defect of an object to be inspected, a first pattern composed of a mixture of a first color which is one of the three primary colors of light and a second color which is one of the three primary colors of light; Of 3
The light emitting surface of the light source is provided with second and third patterns each of which is a mixture of a third color which is one of the primary colors and the first color and which is adjacent to both sides of the first pattern. Existence in the inspection object by imaging either the first pattern transmitted through the inspection object or the first pattern reflected by the inspection object and analyzing the captured image. A method for inspecting an object to be inspected, which comprises extracting a defect candidate for the inspection.
（青）の混色からなるパタンであり、 前記第２および第３のパタンは、Ｇ（緑）およびＲ
（赤）の混色からなるパタンである請求項１に記載の被
検査物の検査方法。2. The first pattern is G (green) and B.
(Blue) is a mixed color pattern, and the second and third patterns are G (green) and R.
The method for inspecting an object to be inspected according to claim 1, wherein the inspection object is a pattern including a mixture of (red) colors.
（赤）の混色からなるパタンであり、 前記第２および第３のパタンは、Ｇ（緑）およびＢ
（青）の混色からなるパタンである請求項１に記載の被
検査物の検査方法。3. The first pattern is G (green) and R (green).
(Red) is a mixed color pattern, and the second and third patterns are G (green) and B.
The inspection method for an object to be inspected according to claim 1, wherein the inspection object is a pattern composed of a mixture of (blue) colors.
像における欠陥候補の中心領域、前記欠陥候補の輪郭近
傍の内側領域、および前記欠陥候補の輪郭近傍の外側領
域における各画像信号を取得し、これらの画像信号の特
徴量に基づいて前記欠陥候補の種類を識別する請求項１
〜３の何れか一項に記載の被検査物の検査方法。4. A center region of a defect candidate in each of R (red), G (green) and B (blue) images, an inner region near the contour of the defect candidate, and an outer region near the contour of the defect candidate. The type of the defect candidate is identified based on the characteristic amount of each image signal obtained from each image signal.
The inspection method of the inspection object according to any one of 1 to 3.
差のある複数の画像に基づいて前記欠陥候補の三次元位
置を求め、前記欠陥候補が被検査物の表面または内部の
何れにあるかを判定する請求項１〜４の何れか一項に記
載の被検査物の検査方法。5. A plurality of images having parallax are acquired, a three-dimensional position of the defect candidate is obtained based on the plurality of images having parallax, and the defect candidate is present on either the surface or the inside of the inspection object. The method for inspecting an object to be inspected according to any one of claims 1 to 4, which determines whether or not it is.
し、測定されたそれらの三次元位置を基準として欠陥候
補が前記透明体の内部、前記透明体の表面、または前記
透明体の裏面の何れにあるかを判定する請求項５に記載
の被検査物の検査方法。6. The object to be inspected is a transparent body, the front surface and the back surface of the transparent body are optically marked, and defect candidates are the inside of the transparent body with reference to their measured three-dimensional positions. The method for inspecting an object to be inspected according to claim 5, wherein it is determined whether it is on the front surface of the transparent body or the back surface of the transparent body.
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| JP2002141652A JP4151306B2 (en) | 2002-05-16 | 2002-05-16 | Inspection method of inspection object |
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|---|---|---|---|
| JP2002141652A JP4151306B2 (en) | 2002-05-16 | 2002-05-16 | Inspection method of inspection object |
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| JP2003329612A true JP2003329612A (en) | 2003-11-19 |
| JP4151306B2 JP4151306B2 (en) | 2008-09-17 |
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| JP2002141652A Expired - Fee Related JP4151306B2 (en) | 2002-05-16 | 2002-05-16 | Inspection method of inspection object |
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| Country | Link |
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