JP4563183B2 - Inspection method and apparatus for streaky unevenness defect - Google Patents

Inspection method and apparatus for streaky unevenness defect Download PDF

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JP4563183B2
JP4563183B2 JP2005000683A JP2005000683A JP4563183B2 JP 4563183 B2 JP4563183 B2 JP 4563183B2 JP 2005000683 A JP2005000683 A JP 2005000683A JP 2005000683 A JP2005000683 A JP 2005000683A JP 4563183 B2 JP4563183 B2 JP 4563183B2
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敦司 岡澤
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Dai Nippon Printing Co Ltd
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Description

本発明は対象物における光学特性が均一であるか否かを検査する技術分野に属する。特に、スジ状ムラ欠陥の短辺方向の幅に影響されることなく高い検査性能を得ることができるスジ状ムラ欠陥の検査方法および装置に関する。   The present invention belongs to the technical field of inspecting whether or not optical characteristics of an object are uniform. In particular, the present invention relates to a method and apparatus for inspecting streaky uneven defects that can obtain high inspection performance without being affected by the width in the short side direction of streaky uneven defects.

スジ状ムラ欠陥を検査する従来の多くの検査装置においては、対象物を撮像して得た入力画像からスジ状ムラ欠陥を抽出する処理において、2次微分フィルタを適用している(特許文献1、特許文献2)。2次微分フィルタを用いて精度良くスジ状ムラを抽出するためには、図7に示すように、スジ状ムラ欠陥の短辺方向の幅に適合する2次微分フィルタを適用する必要性がある。しかし、対象物に発生する短辺方向の幅を検査前に知ることは不可能である。また、あらかじめ準備し適用できる2次微分フィルタの種類には限度があり、それらが必ずしも適合するわけではない。したがって、十分な検査性能を得ることができなかった。
特開2000−199745 特開2002−28059
In many conventional inspection apparatuses for inspecting streaky unevenness defects, a secondary differential filter is applied in the process of extracting streaky unevenness defects from an input image obtained by imaging an object (Patent Document 1). Patent Document 2). In order to accurately extract streak-like unevenness using a second-order differential filter, it is necessary to apply a second-order differential filter that matches the width of the streaky unevenness defect in the short side direction, as shown in FIG. . However, it is impossible to know the width in the short side direction generated in the object before the inspection. In addition, there are limits to the types of secondary differential filters that can be prepared and applied in advance, and they are not necessarily compatible. Therefore, sufficient inspection performance could not be obtained.
JP 2000-199745 A JP 2002-28059 A

本発明は上記の問題を解決するために成されたものである。その目的は、スジ状ムラ欠陥の短辺方向の幅に影響されることなく高い検査性能を得ることができるスジ状ムラ欠陥の検査方法および装置を提供することにある。   The present invention has been made to solve the above problems. An object of the present invention is to provide a method and apparatus for inspecting streaky unevenness defects that can obtain high inspection performance without being affected by the width in the short side direction of streaky unevenness defects.

本発明の請求項1に係るスジ状ムラ欠陥の検査方法は、入力画像に対してノイズ除去フィルタを適用しノイズ除去画像を得るノイズ除去過程と、前記ノイズ除去画像に対して検出すべきスジ状ムラ欠陥の方向の平滑化フィルタを適用し平滑化画像を得る平滑化過程と、前記平滑化画像に対して縦方向と横方向と斜め2方向の4方向の中から前記検出すべきスジ状ムラ欠陥の方向を除く3方向について1次微分フィルタを適用して前記3方向の1次微分画像を得る1次微分過程と、前記3方向の1次微分画像の画素の画素値をその絶対値に置き換えて前記3方向の絶対値画像を得る絶対値化過程と、前記3方向の絶対値画像における同一位置の画素の画素値を比較して最も大きい画素値を画素の画素値とする最大値画像を得る最大値化過程と、前記最大値画像に対して所定の閾値を適用し2値画像を得る2値化過程と、前記2値画像に対してラベリングを行いラベリング画像を得るラベリング過程と、前記ラベリング画像からラベルリング番号がAであるラベルA領域を抽出する領域抽出過程と、前記ラベルA領域の境界を構成する画素Bを抽出し境界画像を得る境界抽出過程と、前記境界を構成する画素Bの座標(X[B],Y[B])を抽出する座標抽出過程と、前記座標に着目し前記4方向の絶対値画像から前記座標の画素の画素値が最も大きい絶対値画像を抽出する画像抽出過程と、前記抽出した絶対値画像に適用した1次微分フィルタの方向を画素値の変化方向とする変化方向抽出過程と、前記座標から前記変化方向における前記ラベルA領域の境界と交差するまでの画素の座標を抽出し交差領域座標とする交差領域抽出過程と、前記ノイズ除去画像における前記交差領域座標の画素の最大画素値と最少画素値の差である画素値差を演算する画素値差演算過程と、前記境界を構成するすべての画素Bについて得た前記画素値差の平均値を演算し、その平均値を前記ラベルA領域におけるムラ領域と非ムラ領域の画素値の差であるムラ値とする平均値演算過程と、前記ムラ値に基づいて前記ラベルA領域の良否を判定する良否判定過程とを有するようにしたものである。
また本発明の請求項2に係るスジ状ムラ欠陥の検査装置は、ラインセンサカメラと搬送手段と処理手段とを具備するスジ状ムラ欠陥の検査装置であって、前記ラインセンサカメラは線状の撮像領域の主走査を行って検査対象物品を撮像し撮像信号を出力し、前記搬送手段は前記主走査に対する副走査の方向に前記検査対象物品を搬送し、前記処理手段は前記主走査と前記副走査に同期して前記撮像信号を入力し入力画像を生成するとともに、前記入力画像に対して前記請求項1記載のスジ状ムラ欠陥の検査方法を適用したデータ処理を行い前記検査対象物品の良否を判定するようにしたものである。
According to a first aspect of the present invention, there is provided a method for inspecting a stripe-like unevenness defect in which a noise removal process is performed by applying a noise removal filter to an input image to obtain a noise removal image, and a stripe shape to be detected for the noise removal image. A smoothing process for obtaining a smoothed image by applying a smoothing filter in the direction of the unevenness defect, and the streak-like unevenness to be detected from four directions of the vertical direction, the horizontal direction, and the two diagonal directions with respect to the smoothed image. Applying a first-order differential filter to the three directions excluding the defect direction to obtain a first-order differential image in the three directions, and the pixel values of the pixels of the first-order differential image in the three directions are set to absolute values thereof. The maximum value image in which the absolute value process in which the absolute value image in the three directions is replaced and the pixel value of the pixel at the same position in the absolute value image in the three directions is compared and the largest pixel value is the pixel value of the pixel Maximization process to obtain A binarization process for obtaining a binary image by applying a predetermined threshold to the maximum value image, a labeling process for labeling the binary image to obtain a labeling image, and a labeling number from the labeling image. A region extraction process for extracting the label A region which is A, a boundary extraction step for extracting a pixel B constituting the boundary of the label A region to obtain a boundary image, and the coordinates (X [B ], Y [B]), an image extraction process for extracting the absolute value image having the largest pixel value of the pixel of the coordinates from the absolute value images in the four directions by paying attention to the coordinates, A change direction extraction process in which the direction of the primary differential filter applied to the extracted absolute value image is the change direction of the pixel value, and pixels from the coordinates to the boundary of the label A region in the change direction An intersection area extraction process for extracting coordinates and setting the intersection area coordinates; and a pixel value difference calculation process for calculating a pixel value difference that is a difference between a maximum pixel value and a minimum pixel value of the pixels of the intersection area coordinates in the noise-removed image; Then, an average value of the pixel value differences obtained for all the pixels B constituting the boundary is calculated, and the average value is set as a mura value which is a difference between pixel values of the mura area and the non-mura area in the label A area. An average value calculation process and a pass / fail determination process for determining pass / fail of the label A region based on the unevenness value are provided.
According to a second aspect of the present invention, there is provided a streaky unevenness defect inspection apparatus comprising a line sensor camera, a conveying means, and a processing means, wherein the line sensor camera has a linear shape. The main scanning of the imaging region is performed to image the inspection target article and an imaging signal is output. The transport unit transports the inspection target article in the sub-scanning direction with respect to the main scanning. The imaging signal is input in synchronization with sub-scanning to generate an input image, and the input image is subjected to data processing to which the method for inspecting streaky unevenness defects according to claim 1 is applied. The quality is determined.

本発明の請求項1に係るスジ状ムラ欠陥の検査方法によれば、ノイズ除去過程において入力画像に対してノイズ除去フィルタが適用されノイズ除去画像が得られ、平滑化過程においてノイズ除去画像に対して検出すべきスジ状ムラ欠陥の方向の平滑化フィルタが適用され平滑化画像が得られ、1次微分過程において平滑化画像に対して縦方向と横方向と斜め2方向の4方向の中から検出すべきスジ状ムラ欠陥の方向を除く3方向について1次微分フィルタを適用して3方向の1次微分画像が得られ、絶対値化過程において3方向の1次微分画像の画素の画素値がその絶対値に置き換えられて3方向の絶対値画像が得られ、最大値化過程において3方向の絶対値画像における同一位置の画素の画素値が比較されて最も大きい画素値を画素の画素値とする最大値画像が得られ、2値化過程において最大値画像に対して所定の閾値が適用され2値画像が得られ、ラベリング過程において2値画像に対してラベリングが行われラベリング画像が得られ、領域抽出過程においてラベリング画像からラベルリング番号がAであるラベルA領域が抽出され、境界抽出過程においてラベルA領域の境界を構成する画素Bが抽出され境界画像が得られ、座標抽出過程において境界を構成する画素Bの座標(X[B],Y[B])が抽出され、画像抽出過程において座標に着目し4方向の絶対値画像から座標の画素の画素値が最も大きい絶対値画像が抽出され、変化方向抽出過程において抽出した絶対値画像に適用した1次微分フィルタの方向が画素値の変化方向とされ、交差領域抽出過程において座標から変化方向におけるラベルA領域の境界と交差するまでの画素の座標が抽出され交差領域座標とされ、画素値差演算過程においてノイズ除去画像における交差領域座標の画素の最大画素値と最少画素値の差である画素値差が演算され、平均値演算過程において境界を構成するすべての画素Bについて得られた画素値差の平均値が演算されその平均値がラベルA領域におけるムラ領域と非ムラ領域の画素値の差であるムラ値とされ、良否判定過程においてムラ値に基づいてラベルA領域の良否が判定される。
すなわち、スジ状ムラ欠陥の短辺方向の幅に影響される2次微分フィルタを適用せず1次微分フィルタを適用する。従来の2次微分処理においてはムラ領域そのものを検出するのに対して、1次微分処理ではムラ領域と非ムラ領域の境界(画素値(輝度)変化領域)を抽出するため、短辺方向の幅に関係なくムラを抽出することができる(図8参照)。また、抽出した境界画像から画素値の変化方向を探索し、その方向の最大画素値と最少画素値の差としてムラ領域と非ムラ領域の差であるムラ値を求めることができる。したがって、スジ状ムラ欠陥の短辺方向の幅に影響されることなく高い検査性能を得ることができ、しかも高精度にムラ値を演算することができるスジ状ムラ欠陥の検査方法が提供される。
また本発明の請求項2に係るスジ状ムラ欠陥の検査装置によれば、ラインセンサカメラにより線状の撮像領域の主走査が行われ検査対象物品が撮像され撮像信号が出力され、搬送手段により主走査に対する副走査の方向に検査対象物品が搬送され、処理手段により主走査と副走査に同期して撮像信号が入力され入力画像が生成されるとともに、入力画像に対して請求項1記載のスジ状ムラ欠陥の検査方法が適用されたデータ処理が行われ検査対象物品の良否が判定される。すなわち、スジ状ムラ欠陥の短辺方向の幅に影響される2次微分フィルタを適用せず1次微分フィルタを適用する。従来の2次微分処理においてはムラ領域そのものを検出するのに対して、1次微分処理ではムラ領域と非ムラ領域の境界(画素値(輝度)変化領域)を抽出するため、短辺方向の幅に関係なくムラを抽出することができる(図8参照)。したがって、スジ状ムラ欠陥の短辺方向の幅に影響されることなく高い検査性能を得ることができ、しかも高精度にムラ値を演算することができるスジ状ムラ欠陥の検査装置が提供される。
According to the method for inspecting streaky unevenness according to claim 1 of the present invention, a noise removal filter is applied to an input image in a noise removal process to obtain a noise removal image, and a noise removal image is obtained in a smoothing process. The smoothing filter in the direction of the stripe-shaped unevenness to be detected is applied to obtain a smoothed image, and in the first-order differentiation process, the smoothed image is selected from four directions of the vertical direction, the horizontal direction, and the two diagonal directions. A first-order differential image is obtained by applying a first-order differential filter to the three directions excluding the direction of the stripe-shaped unevenness to be detected, and the pixel values of the pixels of the first-order differential image in the three directions are obtained in the absolute value conversion process. Is replaced with the absolute value to obtain an absolute value image in three directions, and in the maximization process, the pixel values of the pixels at the same position in the absolute value image in three directions are compared, and the largest pixel value is determined as the pixel value of the pixel. And a predetermined threshold is applied to the maximum value image in the binarization process to obtain a binary image, and the binary image is labeled in the labeling process to obtain a labeling image. In the region extraction process, a label A region having a labeling number A is extracted from the labeling image, and in the boundary extraction step, pixels B constituting the boundary of the label A region are extracted to obtain a boundary image. The coordinates (X [B], Y [B]) of the pixel B constituting the boundary are extracted, and the absolute value image in which the pixel value of the pixel of the coordinate is the largest from the four-direction absolute value image is focused on in the image extraction process. Is extracted, and the direction of the first-order differential filter applied to the absolute value image extracted in the change direction extraction process is set as the change direction of the pixel value. The coordinates of the pixel until it intersects the boundary of the label A area in the change direction are extracted and set as the intersection area coordinate. In the pixel value difference calculation process, the maximum pixel value and the minimum pixel value of the pixel of the intersection area coordinate in the noise removal image are extracted. A pixel value difference which is a difference is calculated, an average value of pixel value differences obtained for all the pixels B constituting the boundary in the average value calculation process is calculated, and the average value is a non-uniform region and a non-uniform region in the label A region. The unevenness value which is a difference between the pixel values of the label A region is determined, and the quality of the label A region is determined based on the unevenness value in the quality determination process.
That is, the primary differential filter is applied without applying the secondary differential filter that is affected by the width in the short side direction of the stripe-like unevenness defect. In the conventional secondary differentiation process, the uneven area itself is detected, whereas in the primary differential process, the boundary (pixel value (luminance) change area) between the uneven area and the non-uniform area is extracted. Unevenness can be extracted regardless of the width (see FIG. 8). Further, the direction of change of the pixel value is searched from the extracted boundary image, and the unevenness value which is the difference between the unevenness region and the non-nonuniformity region can be obtained as the difference between the maximum pixel value and the minimum pixel value in the direction. Therefore, it is possible to obtain high inspection performance without being influenced by the short-side direction of the width of the streak-like unevenness defect, moreover inspection method streaky unevenness defects that can be calculated unevenness value is provided with high precision The
Further, according to the inspection device for streaky unevenness according to claim 2 of the present invention, the main scanning of the linear imaging region is performed by the line sensor camera, the inspection object is imaged, and the imaging signal is output, and the conveying means is used. The inspection object is conveyed in a sub-scanning direction with respect to the main scanning, and an imaging signal is input and an input image is generated in synchronization with the main scanning and the sub-scanning by the processing unit, and the input image according to claim 1. Data processing to which the inspection method for streaky unevenness defects is applied is performed, and the quality of the inspection target article is determined. That is, the primary differential filter is applied without applying the secondary differential filter that is affected by the width in the short side direction of the stripe-like unevenness defect. In the conventional secondary differentiation process, the uneven area itself is detected, whereas in the primary differential process, the boundary (pixel value (luminance) change area) between the uneven area and the non-uniform area is extracted. Unevenness can be extracted regardless of the width (see FIG. 8). Therefore, it is possible to obtain high inspection performance without being influenced by the short-side direction of the width of the streak-like unevenness defect, moreover inspection apparatus streaky unevenness defects that can be calculated unevenness value is provided with high precision The

次に、本発明の実施の形態について図を参照しながら説明する。本発明のスジ状ムラ欠陥の検査装置における構成の一例を図1に示す。図1において、1はラインセンサカメラ、2は光源、3は画像処理部、4は入力部、5は出力部、6は搬送機、100は対象物である。
対象物100はどのような物品でも良く特に限定はない。たとえば、ウェブ、シート、パネル、等の検査対象となる表面を有する物品である。対象物100の走行方向は、図1において矢印で示す方向である。
Next, embodiments of the present invention will be described with reference to the drawings. An example of the configuration of the inspection device for streaky unevenness defects of the present invention is shown in FIG. In FIG. 1, 1 is a line sensor camera, 2 is a light source, 3 is an image processing unit, 4 is an input unit, 5 is an output unit, 6 is a conveyor, and 100 is an object.
The object 100 may be any article and is not particularly limited. For example, it is an article having a surface to be inspected, such as a web, a sheet, and a panel. The traveling direction of the object 100 is a direction indicated by an arrow in FIG.

ラインセンサカメラ1は、ラインセンサ素子、出力アンプ、時系列で信号出力するための駆動回路、結像レンズ、等により構成され、直線状の撮像領域を有する。ラインセンサ素子は複数の受光部を直線上に配列したCCD(charge couplled device)、MOS(metal oxide semiconductor)等のLSI(large scale integrated circuit)である。図1に示すように、ラインセンサカメラ1の撮像領域は、対象物100の幅方向(対象物100の走行方向に対して直角方法)に延びている。ラインセンサカメラ1による主走査と対象物100が走行することによる副走査により対象物100の表面(二次元の領域)の撮像を行うことができる。搬送機6はその副走査方向に対象物100を搬送する搬送機である。   The line sensor camera 1 includes a line sensor element, an output amplifier, a drive circuit for outputting signals in time series, an imaging lens, and the like, and has a linear imaging region. The line sensor element is a large scale integrated circuit (LSI) such as a charge coupled device (CCD) or a metal oxide semiconductor (MOS) in which a plurality of light receiving portions are arranged in a straight line. As shown in FIG. 1, the imaging area of the line sensor camera 1 extends in the width direction of the object 100 (a method perpendicular to the traveling direction of the object 100). Imaging of the surface (two-dimensional area) of the object 100 can be performed by main scanning by the line sensor camera 1 and sub-scanning by the object 100 traveling. The transporter 6 is a transporter that transports the object 100 in the sub-scanning direction.

ラインセンサカメラ1の光軸(撮像方向の中心線)は、図1に示す一例においては、対象物100の表面に対して垂直方向から外れており所定の角度となっている。すなわち、ラインセンサカメラ1は対象物100の表面を斜め方向から撮像する。ラインセンサカメラ1の光軸と対象物100の表面とが成す角度は、撮像しようとする欠陥の特性によって適正な角度が存在する。欠陥の特性に応じてその欠陥に特有の光変調を撮像できるような角度とする。   In the example shown in FIG. 1, the optical axis of the line sensor camera 1 (center line in the imaging direction) deviates from the vertical direction with respect to the surface of the object 100 and has a predetermined angle. That is, the line sensor camera 1 images the surface of the object 100 from an oblique direction. The angle formed by the optical axis of the line sensor camera 1 and the surface of the object 100 is an appropriate angle depending on the characteristics of the defect to be imaged. The angle is set such that light modulation peculiar to the defect can be imaged according to the characteristic of the defect.

光源2はラインセンサカメラ1の撮像領域を照明する照明手段である。光源2はラインセンサカメラ1の直線状の撮像領域に適合するように、直線状の照明領域が得られるような光源が好適である。光源2による照明領域は、一般的に、ラインセンサカメラ1の撮像領域を包含するように設定される場合が多い。直線状の照明領域を得るために、光源2としては、直線状の発光を行う光源が使用される。直線状の発光を行う光源としては、たとえば、直管型の蛍光灯、直管型のハロゲンランプ、LED(light emitting diode)を直線上に配列した光源、点光源の光線を光ファイバーで導き直線状に照射するようにした光ファイバー光源、点光源の光線を導光管で導きスリットから照射するようにした光源、等を使用することができる。   The light source 2 is an illumination unit that illuminates the imaging region of the line sensor camera 1. The light source 2 is preferably a light source capable of obtaining a linear illumination area so as to match the linear imaging area of the line sensor camera 1. In general, the illumination area by the light source 2 is often set so as to include the imaging area of the line sensor camera 1. In order to obtain a linear illumination region, a light source that emits linear light is used as the light source 2. As a light source that emits light in a straight line, for example, a straight tube fluorescent lamp, a straight tube halogen lamp, a light source in which LEDs (light emitting diodes) are arranged in a straight line, a light beam of a point light source is guided by an optical fiber, and is linear. An optical fiber light source adapted to irradiate a light source, a light source adapted to guide a light beam of a point light source through a light guide tube and irradiate from a slit, and the like can be used.

図1においては、直線状の発光を行う光源2が反射型撮像系の光源として使用され、対象物100の表面側にラインセンサカメラ1とともに光源2が配置されている。直線状の発光を行う光源2の延びる方向と、直線状の撮像領域の延びる方向とは平行となっている。ラインセンサカメラ1の光軸と光源2の配置との関係は、ラインセンサカメラ1において前述したように、欠陥を撮像し易くすることを考慮した上で総合的な判断から配置が決定される。   In FIG. 1, a light source 2 that emits light in a straight line is used as a light source of a reflective imaging system, and the light source 2 is disposed together with the line sensor camera 1 on the surface side of an object 100. The direction in which the light source 2 that performs linear light emission extends and the direction in which the linear imaging region extends are parallel to each other. The relationship between the optical axis of the line sensor camera 1 and the arrangement of the light sources 2 is determined from comprehensive judgment in consideration of facilitating imaging of defects as described above in the line sensor camera 1.

画像処理部3はラインセンサカメラ1が出力する撮像信号を入力して入力画像として記憶する画像記憶手段、すなわち画像メモリとを有する。画像処理部3はその画像メモリに記憶されている画像についてスジ状ムラ欠陥を抽出する等の画像データ処理、画像入力装置に関する設定、操作、等に係わるデータ処理、ユーザインタフェースに係わるデータ処理、等のデータ処理を行う。そのユーザインタフェースに係わり、キーボードやマウス等の入力部4、ディスプレイや警報器等の出力部5が画像処理部3に接続されている。   The image processing unit 3 includes an image storage unit that inputs an imaging signal output from the line sensor camera 1 and stores it as an input image, that is, an image memory. The image processing unit 3 performs image data processing such as extracting streak-like unevenness defects for the image stored in the image memory, data processing related to settings and operations related to the image input device, data processing related to the user interface, etc. Data processing is performed. In connection with the user interface, an input unit 4 such as a keyboard and a mouse and an output unit 5 such as a display and an alarm are connected to the image processing unit 3.

以上、本発明のスジ状ムラ欠陥の検査装置における構成について説明した。次に、本発明のスジ状ムラ欠陥の検査方法および装置における動作、すなわち主として画像処理部3における動作について図を参照して説明する。本発明の画像処理部3における動作の全体を示す概略図を図2に示す。入力画像からスジ状ムラ欠陥を抽出し良否判定する検査のデータ処理過程の一例をフロー図として図3に示す。また、検査のデータ処理において使用される平滑化フィルタの一例を図4に示し、1次微分フィルタの一例を図5に示す。   The configuration of the streaky unevenness defect inspection apparatus according to the present invention has been described above. Next, operations in the method and apparatus for inspecting streaky unevenness of the present invention, that is, operations in the image processing unit 3 will be described with reference to the drawings. FIG. 2 is a schematic diagram showing the overall operation of the image processing unit 3 of the present invention. FIG. 3 shows an example of a data processing process of inspection for extracting streaky unevenness defects from an input image and judging whether or not the product is acceptable. FIG. 4 shows an example of a smoothing filter used in inspection data processing, and FIG. 5 shows an example of a primary differential filter.

本発明においては、図2に示すように、ノイズ除去処理を行って得られたノイズ除去画像に基づいて、縦方向のスジ状ムラ欠陥を検出する処理、横方向のスジ状ムラ欠陥を検出する処理、斜め45度方向のスジ状ムラ欠陥を検出する処理、斜め135度方向のスジ状ムラ欠陥を検出する処理の合計4つの処理を適用してすべての方向のスジ状ムラ欠陥を検出する。図3に示すフロー図においては、その中の縦方向のスジ状ムラ欠陥を検出する処理についてだけ説明している。他の3つの処理については、そのフロー図を参照する説明から容易に類推可能であるから説明を省略する。   In the present invention, as shown in FIG. 2, based on the noise-removed image obtained by performing the noise removal process, a process for detecting vertical stripe-like unevenness defects and a horizontal stripe-like unevenness defect are detected. A total of four processes including a process, a process for detecting a stripe-shaped uneven defect in a 45-degree oblique direction, and a process for detecting a stripe-shaped uneven defect in a 135-degree oblique direction are applied to detect a stripe-shaped uneven defect in all directions. In the flowchart shown in FIG. 3, only the process for detecting the vertical stripe-like unevenness defect therein is described. The other three processes can be easily inferred from the description with reference to the flowchart, and the description thereof will be omitted.

まず図3のステップS1(ノイズ除去過程)において、画像メモリに入力された入力画像に対してノイズ除去フィルタを適用しノイズ除去処理を行いノイズ除去画像を得る。ノイズ除去フィルタとしては、ガウシアンフィルタ、メディアンフィルタ、平均値フィルタが存在するが、除去したいノイズに適合するフィルタを適用してノイズ除去を行う。たとえば、ソルト&ペッパーノイズはメディアンフィルタ、弱いノイズにはガウシアンフィルタ、強いノイズには平均値フィルタを適用する。   First, in step S1 (noise removal process) in FIG. 3, a noise removal filter is applied to the input image input to the image memory to perform noise removal processing to obtain a noise removal image. As a noise removal filter, there are a Gaussian filter, a median filter, and an average value filter, and noise removal is performed by applying a filter suitable for the noise to be removed. For example, a median filter is applied to salt and pepper noise, a Gaussian filter is applied to weak noise, and an average filter is applied to strong noise.

次に、ステップS2(平滑化過程)において、ノイズ除去画像に対して検出すべきスジ状ムラ欠陥の方向の平滑化フィルターを適用し平滑化画像を得る。ここでは(図3に示すフロー図においては)、一例として、検出すべきスジ状ムラ欠陥の方向が縦方向であるものとして、図4に示す、横方向、縦方向、斜め45度方向、斜め135度方向の中から、縦方向平滑化フィルターを適用する。   Next, in step S2 (smoothing process), a smoothing image is obtained by applying a smoothing filter in the direction of streaky unevenness to be detected to the noise-removed image. In this case (in the flowchart shown in FIG. 3), as an example, the direction of the stripe-shaped unevenness to be detected is assumed to be the vertical direction, and the horizontal direction, the vertical direction, the 45 ° oblique direction, and the oblique direction shown in FIG. A vertical direction smoothing filter is applied from the 135 degree direction.

次に、ステップS3(1次微分過程)において、Sobelフィルタ、Prewittフィルタ、等の1次微分フィルタをノイズ除去画像に適用し1次微分処理を行い1次微分画像を得る。スジ状ムラ欠陥の領域であるムラ領域とスジ状ムラ欠陥の領域ではない非ムラ領域の画素値の変化は、一般的に、穏やかであるので、1次微分フィルタにおける正係数の画素と負係数の画素との間隔を3画素以上離す。また、1次微分フィルタの方向については、平滑化画像に対して縦方向と横方向と斜め2方向の4方向の中から検出すべきスジ状ムラ欠陥の方向を除く3方向について1次微分フィルタを適用して3方向とする。すなわち、横方向、斜め2方向の3方向とし、その3方向の1次微分フィルタを適用する。   Next, in step S3 (first-order differentiation process), a first-order differential filter such as a Sobel filter or a Prewitt filter is applied to the noise-removed image, and a first-order differential process is performed to obtain a first-order differential image. Since the change in pixel values of the non-uniformity region which is not the region of the stripe-like unevenness and the unevenness region which is the stripe-like unevenness defect is generally gentle, the positive coefficient pixel and the negative coefficient in the first-order differential filter 3 pixels or more are separated from each other. As for the direction of the first-order differential filter, the first-order differential filter in three directions excluding the direction of the stripe-shaped unevenness to be detected from the four directions of the vertical direction, the horizontal direction, and the two diagonal directions with respect to the smoothed image. Is applied in three directions. That is, the horizontal direction and the diagonal two directions are set to three directions, and the first-order differential filter in the three directions is applied.

したがって、ステップS3においては、3方向の1次微分フィルタの各々を平滑化画像に適用し1次微分処理を行い3方向の(3つの)1次微分画像が得られる。図5に一例を示す1次微分フィルタにおいて、図5(A)は横方向の1次微分フィルタ、図5(B)は縦方向の1次微分フィルタ、図5(C)は斜め45度方向の1次微分フィルタ、図6(D)は斜め135度方向の1次微分フィルタである。   Therefore, in step S3, each of the three-direction primary differential filters is applied to the smoothed image to perform the primary differential processing, and three (three) primary differential images are obtained. FIG. 5A shows a first-order differential filter in the horizontal direction, FIG. 5B shows a first-order differential filter in the vertical direction, and FIG. 5C shows an oblique 45-degree direction. FIG. 6D shows a first-order differential filter in the oblique 135 degree direction.

次に、ステップS4(絶対値化過程)において、3方向の1次微分画像の各々について、画像を構成する画素の画素値をその画素値の絶対値に置き換える絶対値処理を行って3方向の絶対値画像を得る。
次に、ステップS5(最大値化過程)において、3方向の絶対値画像に基づいて最大値処理を行い最大値画像を得る。最大値処理は、複数の画像における対応する位置の画素の画素値を比較し、最大の画素値を最大値画像の対応する位置の画素の画素値とする処理である。
Next, in step S4 (absolute value conversion process), for each of the first-order differential images in the three directions, absolute value processing is performed to replace the pixel values of the pixels constituting the image with the absolute values of the pixel values. Obtain an absolute value image.
Next, in step S5 (maximization process), maximum value processing is performed based on the absolute value images in three directions to obtain a maximum value image. The maximum value processing is processing in which pixel values of pixels at corresponding positions in a plurality of images are compared and the maximum pixel value is set as a pixel value of a pixel at a corresponding position in the maximum value image.

次に、ステップS6(2値化過程)において、最大値画像に対して2値化処理を行い2値化画像を得る。2値化の閾値としては、検出したいムラ領域と非ムラ領域との境界が2値化画像における画素値1となるようにあらかじめ設定しておく。
次に、ステップS7(ラベリング過程)において、2値化画像に対してラベリング処理を行ってラベリング画像を得る。
次に、ステップS8(領域抽出過程)において、ラベリング画像からラベリング番号がAであるラベルA領域を抽出する領域抽出処理を行う。
Next, in step S6 (binarization process), a binarization process is performed on the maximum value image to obtain a binarized image. The binarization threshold value is set in advance so that the boundary between the non-uniformity region and the non-uniformity region to be detected becomes the pixel value 1 in the binary image.
Next, in step S7 (labeling process), a labeling image is obtained by performing a labeling process on the binarized image.
Next, in step S8 (region extraction process), region extraction processing for extracting a label A region with a labeling number A from the labeling image is performed.

次に、ステップS9(ムラ値演算過程)において、ラベルA領域におけるムラ領域と非ムラ領域との画素値の差であるムラ値を演算するムラ値演算処理を行う。
次に、ステップS10(良否判定過程)において、ムラ値に基づいてラベルA領域の良否を判定する良否判定処理を行う。
そして、ラベリング画像におけるすべてのラベリング番号について良否判定が済んでいないときには、ラベリング番号Aを良否判定が済んでいないラベリング番号に変更してステップS7以降の上述のステップを繰返す。ラベリング画像におけるすべてのラベリング番号について良否判定が済んだときには、上述の処理に係わるデータ(良否判定、中間画像、ログ、処理条件)、特に良否判定のデータをメモリに保存する。
Next, in step S9 (unevenness value calculation process), unevenness value calculation processing for calculating an unevenness value which is a difference between pixel values of the unevenness area and the non-nonuniformity area in the label A area is performed.
Next, in step S10 (quality determination process), quality determination processing for determining quality of the label A area based on the unevenness value is performed.
Then, when all the labeling numbers in the labeling image have not been judged as good or bad, the labeling number A is changed to a labeling number that has not been judged good or bad, and the above-described steps after step S7 are repeated. When the pass / fail determination is completed for all the labeling numbers in the labeling image, the data related to the above-described processing (pass / fail determination, intermediate image, log, processing condition), particularly the pass / fail determination data is stored in the memory.

以上、本発明のスジ状ムラ欠陥の検査方法および装置における画像処理について図を参照して説明した。次に、上記のステップS9(ムラ値演算過程)について図を参照して詳細を説明する。本発明のスジ状ムラ欠陥の検査方法および装置におけるムラ値演算過程の一例についてその詳細を図6に示す。
まず、図4、図6のステップS8(領域抽出過程)において、ラベルA領域を抽出する。
The image processing in the method and apparatus for inspecting streaky unevenness defects of the present invention has been described above with reference to the drawings. Next, step S9 (uneven value calculation process) will be described in detail with reference to the drawings. FIG. 6 shows the details of an example of the unevenness value calculation process in the method and apparatus for inspecting streaky unevenness defects of the present invention.
First, in step S8 (region extraction process) of FIGS. 4 and 6, the label A region is extracted.

次に、図6のステップS101(境界抽出過程)において、ラベルA領域の境界を構成する画素を抽出し境界画像を得る。なお、境界は特定の画素が特定の領域(集合)に含まれるか否かを決めるものであるから境界は画素ではない。しかしここでは、その境界に隣接する画素を境界を構成する画素と呼ぶ。境界を構成する画素は、境界によって決まる領域の内部に存在する画素と外部に存在する画素とがある。本発明においては、境界を構成する画素が内部外部のいずれに定義したものであっても、通常は、判定に大きく影響することはない。   Next, in step S101 (boundary extraction process) in FIG. 6, pixels constituting the boundary of the label A region are extracted to obtain a boundary image. Note that the boundary is not a pixel because it determines whether or not a specific pixel is included in a specific region (set). However, here, a pixel adjacent to the boundary is referred to as a pixel constituting the boundary. The pixels constituting the boundary include a pixel existing inside a region determined by the boundary and a pixel existing outside. In the present invention, even if the pixels constituting the boundary are defined either inside or outside, usually, the determination is not greatly affected.

次に、ステップS102(座標抽出過程)において、境界画像における画素の座標(X[i],Y[i])を抽出する。ただし、i=1〜n(n:総数)とする。
次に、ステップS103(初期化過程)において、i=1とする。
次に、ステップS104(着目過程)において、座標(X[i],Y[i])の画素Piを処理対象とする(着目する)。
次に、ステップS105(画像抽出過程)において、4方向の絶対値画像(ステップS3参照)から座標(X[i],Y[i])の画素Piの画素値(輝度)が最も大きい絶対値画像を抽出する。
Next, in step S102 (coordinate extraction process), pixel coordinates (X [i], Y [i]) in the boundary image are extracted. However, i = 1 to n (n: total number).
Next, in step S103 (initialization process), i = 1 is set.
Next, in step S104 (focus process), the pixel Pi at the coordinates (X [i], Y [i]) is set as a processing target (focussed).
Next, in step S105 (image extraction process), the absolute value having the largest pixel value (luminance) of the pixel Pi at coordinates (X [i], Y [i]) from the absolute value image in four directions (see step S3). Extract images.

次に、ステップS106(変化方向抽出過程)において、抽出した絶対値画像に適用した1次微分フィルターの方向を画素値(輝度)の変化方向とする変化方向抽出処理を行う。
次に、ステップS107(直線引過程)において、座標(X[i],Y[i])の画素Piを通り、画素値の変化方向に延びる直線をラベルA領域に当て嵌める(引く)。
次に、ステップS108(交差領域抽出過程)において、当て嵌めた直線とラベルA領域の画素とが交差する領域を抽出する。すなわち、座標(X[i],Y[i])の画素Piから変化方向におけるラベルA領域の境界と交差するまでの画素の座標を抽出し交差領域座標とする交差領域抽出処理を行う。
Next, in step S106 (change direction extraction process), change direction extraction processing is performed in which the direction of the primary differential filter applied to the extracted absolute value image is the change direction of the pixel value (luminance).
Next, in step S107 (straight line drawing process), a straight line that passes through the pixel Pi at the coordinates (X [i], Y [i]) and extends in the change direction of the pixel value is applied (drawn) to the label A region.
Next, in step S108 (intersection area extraction process), an area where the fitted straight line and the pixel in the label A area intersect is extracted. That is, the intersection area extraction process is performed by extracting the coordinates of the pixels from the pixel Pi at the coordinates (X [i], Y [i]) until the intersection with the boundary of the label A area in the change direction to obtain the intersection area coordinates.

次に、ステップS109(画素値差演算過程)において、ノイズ除去画像(ステップS1参照)における交差領域座標の画素の最大画素値と最少画素値の差である画素値差を演算する画素値差演算処理を行う。
次に、ステップS110(総数?)において、境界を構成する全画素について画素値差を演算したか否かが判定される。すなわち、i=Nであるか否かが判定される。i=NのときにはステップS111に進む。i=Nでないときには、ステップS112に進む。
Next, in step S109 (pixel value difference calculation process), a pixel value difference calculation that calculates a pixel value difference that is the difference between the maximum pixel value and the minimum pixel value of the pixels in the intersection region coordinates in the noise-removed image (see step S1). Process.
Next, in step S110 (total number?), It is determined whether or not the pixel value difference has been calculated for all the pixels constituting the boundary. That is, it is determined whether i = N. When i = N, the process proceeds to step S111. When i is not N, the process proceeds to step S112.

ステップS111(平均値演算過程)においては、境界を構成するすべての画素Pi(i=1〜n)について得た画素値差の平均値を演算し、その平均値をムラ値とする平均値演算処理を行う。
ステップS112(継続過程)においては、i=i+1としてステップS104に戻って上述した以降のステップを繰返す。
In step S111 (average value calculation process), an average value of pixel value differences obtained for all the pixels Pi (i = 1 to n) constituting the boundary is calculated, and an average value calculation using the average value as an uneven value. Process.
In step S112 (continuation process), i = i + 1 is set, the process returns to step S104, and the subsequent steps are repeated.

上述したように、本発明においては、スジ状ムラ欠陥の短辺方向の幅に影響される2次微分フィルタを適用せず1次微分フィルタを適用する。2次微分処理においてはムラ領域そのものを検出するのに対して、1次微分処理ではムラ領域と非ムラ領域の境界を抽出するため、短辺方向の幅に関係なくムラを抽出することができる。また、抽出した境界画像から画素値の変化方向を探索し、その方向の最大画素値と最少画素値の差としてムラ領域と非ムラ領域の差であるムラ値を演算するから、得られるムラ値は高精度である。したがって、スジ状ムラ欠陥の短辺方向の幅に影響されることなく高い検査性能を得ることができるスジ状ムラ欠陥の検査方法および装置が提供される。   As described above, in the present invention, the primary differential filter is applied without applying the secondary differential filter affected by the width in the short side direction of the stripe-like unevenness defect. In the secondary differentiation process, the uneven area itself is detected, whereas in the primary differential process, the boundary between the uneven area and the non-uniform area is extracted, so that the unevenness can be extracted regardless of the width in the short side direction. . In addition, the pixel value change direction is searched from the extracted boundary image, and the unevenness value, which is the difference between the unevenness region and the non-nonuniformity region, is calculated as the difference between the maximum pixel value and the minimum pixel value in that direction. Is highly accurate. Therefore, an inspection method and apparatus for streak-like unevenness defects that can obtain high inspection performance without being affected by the width in the short side direction of streak-like unevenness defects are provided.

本発明のスジ状ムラ欠陥の検査装置における構成の一例を示す図である。It is a figure which shows an example of the structure in the inspection apparatus of the stripe-shaped nonuniformity defect of this invention. 本発明の画像処理部における動作の全体を示す概略図である。It is the schematic which shows the whole operation | movement in the image processing part of this invention. 入力画像からスジ状ムラ欠陥を抽出し良否判定する検査のデータ処理過程の一例を示すフロー図である。It is a flowchart which shows an example of the data processing process of the test | inspection which extracts a stripe-shaped nonuniformity defect from an input image, and determines a quality. 検査のデータ処理において使用される平滑化フィルタの一例を示す図である。It is a figure which shows an example of the smoothing filter used in the data processing of a test | inspection. 検査のデータ処理において使用される1次微分フィルタの一例を示す図である。It is a figure which shows an example of the primary differential filter used in the data processing of a test | inspection. 検査のデータ処理における輝度差を演算する処理過程の一例についてその詳細を示す図である。It is a figure which shows the detail about an example of the process of calculating the brightness | luminance difference in the data processing of a test | inspection. スジ状ムラ欠陥の短辺方向の幅に適合する2次微分フィルタを適用することを示す説明図である。It is explanatory drawing which shows applying the secondary differential filter which adapts to the width | variety of the short side direction of a stripe-shaped nonuniformity defect. 1次微分処理と2次微分処理の相違を示す説明図である。It is explanatory drawing which shows the difference of a primary differentiation process and a secondary differentiation process.

符号の説明Explanation of symbols

1 ラインセンサカメラ
2 光源
3 画像処理部
4 入力部
5 出力部
6 搬送機
100 対象物














DESCRIPTION OF SYMBOLS 1 Line sensor camera 2 Light source 3 Image processing part 4 Input part 5 Output part 6 Conveyor 100 Target object














Claims (2)

入力画像に対してノイズ除去フィルタを適用しノイズ除去画像を得るノイズ除去過程と、
前記ノイズ除去画像に対して検出すべきスジ状ムラ欠陥の方向の平滑化フィルタを適用し平滑化画像を得る平滑化過程と、
前記平滑化画像に対して縦方向と横方向と斜め2方向の4方向の中から前記検出すべきスジ状ムラ欠陥の方向を除く3方向について1次微分フィルタを適用して前記3方向の1次微分画像を得る1次微分過程と、
前記3方向の1次微分画像の画素の画素値をその絶対値に置き換えて前記3方向の絶対値画像を得る絶対値化過程と、
前記3方向の絶対値画像における同一位置の画素の画素値を比較して最も大きい画素値を画素の画素値とする最大値画像を得る最大値化過程と、
前記最大値画像に対して所定の閾値を適用し2値画像を得る2値化過程と、
前記2値画像に対してラベリングを行いラベリング画像を得るラベリング過程と、
前記ラベリング画像からラベルリング番号がAであるラベルA領域を抽出する領域抽出過程と、
前記ラベルA領域の境界を構成する画素Bを抽出し境界画像を得る境界抽出過程と、
前記境界を構成する画素Bの座標(X[B],Y[B])を抽出する座標抽出過程と、
前記座標に着目し前記4方向の絶対値画像から前記座標の画素の画素値が最も大きい絶対値画像を抽出する画像抽出過程と、
前記抽出した絶対値画像に適用した1次微分フィルタの方向を画素値の変化方向とする変化方向抽出過程と、
前記座標から前記変化方向における前記ラベルA領域の境界と交差するまでの画素の座標を抽出し交差領域座標とする交差領域抽出過程と、
前記ノイズ除去画像における前記交差領域座標の画素の最大画素値と最少画素値の差である画素値差を演算する画素値差演算過程と、
前記境界を構成するすべての画素Bについて得た前記画素値差の平均値を演算し、その平均値を前記ラベルA領域におけるムラ領域と非ムラ領域の画素値の差であるムラ値とする平均値演算過程と、
前記ムラ値に基づいて前記ラベルA領域の良否を判定する良否判定過程と、
を有することを特徴とするスジ状ムラ欠陥の検査方法。
Applying a noise removal filter to the input image to obtain a noise removal image,
Applying a smoothing filter in the direction of streak-like unevenness to be detected to the noise-removed image to obtain a smoothed image; and
A first-order differential filter is applied to three directions excluding the direction of the stripe-like unevenness to be detected from four directions of the vertical direction, the horizontal direction, and the two diagonal directions with respect to the smoothed image. A first derivative process for obtaining a second derivative image;
An absolute value conversion process of obtaining the absolute value image in the three directions by replacing the pixel value of the pixel of the primary differential image in the three directions with the absolute value;
A maximization process in which pixel values of pixels at the same position in the absolute value image in the three directions are compared to obtain a maximum value image having the largest pixel value as the pixel value of the pixel;
A binarization process for obtaining a binary image by applying a predetermined threshold to the maximum value image;
A labeling process of labeling the binary image to obtain a labeled image;
An area extraction process for extracting a label A area having a labeling number A from the labeling image;
A boundary extraction process for extracting a pixel B constituting a boundary of the label A region to obtain a boundary image;
A coordinate extraction process for extracting the coordinates (X [B], Y [B]) of the pixel B constituting the boundary;
An image extraction process of focusing on the coordinates and extracting an absolute value image having the largest pixel value of the pixels of the coordinates from the absolute value images in the four directions;
A change direction extraction process in which the direction of the primary differential filter applied to the extracted absolute value image is the change direction of the pixel value;
An intersection region extraction process in which the coordinates of the pixels from the coordinates to the intersection of the label A region in the change direction are extracted and used as intersection region coordinates;
A pixel value difference calculation process for calculating a pixel value difference that is a difference between the maximum pixel value and the minimum pixel value of the pixels of the intersection region coordinates in the noise-removed image;
An average value of the pixel value differences obtained for all the pixels B constituting the boundary is calculated, and the average value is set to a mura value that is a difference between pixel values of the mura area and the non-mura area in the label A area. Value calculation process,
A pass / fail determination process for determining pass / fail of the label A area based on the unevenness value;
A method for inspecting streaky unevenness defects, comprising:
ラインセンサカメラと搬送手段と処理手段とを具備するスジ状ムラ欠陥の検査装置であって、
前記ラインセンサカメラは線状の撮像領域の主走査を行って検査対象物品を撮像し撮像信号を出力し、
前記搬送手段は前記主走査に対する副走査の方向に前記検査対象物品を搬送し、
前記処理手段は前記主走査と前記副走査に同期して前記撮像信号を入力し入力画像を生成するとともに、前記入力画像に対して前記請求項1記載のスジ状ムラ欠陥の検査方法を適用したデータ処理を行い前記検査対象物品の良否を判定することを特徴とするスジ状ムラ欠陥の検査装置。
An inspection apparatus for streaky uneven defects comprising a line sensor camera, a conveying means, and a processing means,
The line sensor camera performs main scanning of a linear imaging region, images an inspection target article, and outputs an imaging signal;
The conveying means conveys the inspection target article in a sub-scanning direction with respect to the main scanning;
The processing means inputs the imaging signal in synchronization with the main scanning and the sub-scanning to generate an input image, and applies the method for inspecting streaky unevenness defects to the input image. An inspection apparatus for streaky unevenness defects, wherein data processing is performed to determine whether the inspection target article is good or bad.
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JP4863050B2 (en) * 2005-11-15 2012-01-25 大日本印刷株式会社 Color unevenness defect inspection method and apparatus
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH064674A (en) * 1992-06-16 1994-01-14 Tokimec Inc Method and device for removing undesired boundary picture element in area division
JPH064665A (en) * 1992-06-16 1994-01-14 Tokimec Inc Method and device for extracting boundary
JP2000134468A (en) * 1998-10-26 2000-05-12 Canon Inc Image forming device and its method
JP2000199745A (en) * 1998-10-26 2000-07-18 Dainippon Printing Co Ltd Method and apparatus for inspection of color nonuniformity
JP2001028059A (en) * 1999-07-15 2001-01-30 Dainippon Printing Co Ltd Method and device for color unevenness inspection
JP2003329601A (en) * 2002-05-10 2003-11-19 Mitsubishi Rayon Co Ltd Apparatus and method for inspecting defect
JP2004112536A (en) * 2002-09-19 2004-04-08 Ricoh Co Ltd Image processing apparatus, image processing method, and program to be executed by computer

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH064674A (en) * 1992-06-16 1994-01-14 Tokimec Inc Method and device for removing undesired boundary picture element in area division
JPH064665A (en) * 1992-06-16 1994-01-14 Tokimec Inc Method and device for extracting boundary
JP2000134468A (en) * 1998-10-26 2000-05-12 Canon Inc Image forming device and its method
JP2000199745A (en) * 1998-10-26 2000-07-18 Dainippon Printing Co Ltd Method and apparatus for inspection of color nonuniformity
JP2001028059A (en) * 1999-07-15 2001-01-30 Dainippon Printing Co Ltd Method and device for color unevenness inspection
JP2003329601A (en) * 2002-05-10 2003-11-19 Mitsubishi Rayon Co Ltd Apparatus and method for inspecting defect
JP2004112536A (en) * 2002-09-19 2004-04-08 Ricoh Co Ltd Image processing apparatus, image processing method, and program to be executed by computer

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