JPH0544725Y2 - - Google Patents
Info
- Publication number
- JPH0544725Y2 JPH0544725Y2 JP1988013634U JP1363488U JPH0544725Y2 JP H0544725 Y2 JPH0544725 Y2 JP H0544725Y2 JP 1988013634 U JP1988013634 U JP 1988013634U JP 1363488 U JP1363488 U JP 1363488U JP H0544725 Y2 JPH0544725 Y2 JP H0544725Y2
- Authority
- JP
- Japan
- Prior art keywords
- image
- edge line
- line data
- calculation means
- average value
- 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.)
- Expired - Lifetime
Links
- 238000011156 evaluation Methods 0.000 claims description 24
- 238000013075 data extraction Methods 0.000 claims description 4
- 238000000034 method Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 238000003384 imaging method Methods 0.000 description 7
- 238000006243 chemical reaction Methods 0.000 description 5
- 238000000605 extraction Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 102100027340 Slit homolog 2 protein Human genes 0.000 description 3
- 101710133576 Slit homolog 2 protein Proteins 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
Description
【考案の詳細な説明】
〔産業上の利用分野〕
本考案は、プレス成型品等の物体表面の粗さ、
うねりあるいは塗装むら等の良否を判別する物体
表面の平滑性評価装置に係り、特に平滑性評価の
精度を向上させる技術に関する。[Detailed description of the invention] [Industrial application field] The present invention is designed to improve the roughness of the surface of objects such as press-formed products,
The present invention relates to a smoothness evaluation device for the surface of an object that determines the quality of waviness, uneven coating, etc., and particularly relates to a technique for improving the accuracy of smoothness evaluation.
かかる装置としては、従来例えば第１０図に示
すようなものがある（特開昭55−40924号公報参
照）。この装置は、車体パネル等の被検体１に対
して、スリツト２を通して光源３から光を投射
し、該被検体１の表面に表われる光像４を撮像装
置５で入力し、該撮像装置５が出力するビデオ信
号に対し画情報変換部６にて２値化処理を施して
から画像メモリ７に該２値画像を格納し、演算部
８にて前記光像４の歪み度合いを算出することに
よつて被検体１の表面平滑性を評価するようにな
つている。演算部８における処理は、具体的に
は、一本一本の縁線の方向変化度の検出や予め記
憶させておいた基準画像との偏差を求めるテンプ
レートマツチングの手法が用いられる。
As such a device, there is a conventional device as shown in FIG. 10 (see Japanese Patent Laid-Open No. 55-40924). This device projects light from a light source 3 through a slit 2 onto an object 1 to be examined, such as a car body panel, and inputs a light image 4 appearing on the surface of the object 1 to an imaging device 5. performs binarization processing on the video signal outputted by the image information converter 6, stores the binary image in the image memory 7, and calculates the degree of distortion of the optical image 4 in the arithmetic unit 8; The surface smoothness of the subject 1 is evaluated by the following method. Specifically, the processing in the calculation unit 8 uses a template matching method to detect the degree of change in direction of each edge line and to find a deviation from a reference image stored in advance.
ところで、かかる従来の平滑性評価装置にあつ
ては、基準画像との間のマツチング度合いを検出
する等の手段を用いて被検体の平滑性を評価する
ようになつているため、基準となる被検体と異な
る形状の被検体については平滑性の絶対評価がで
きないという問題がある。そこで、本考案の目的
は、被検体に投影されたスリツト光像の定量化に
より、いかなる形状の被検体に対してもその平滑
性を絶対評価できるようにすることにある。
By the way, in such conventional smoothness evaluation devices, the smoothness of the object is evaluated using means such as detecting the degree of matching with a reference image, so There is a problem in that absolute evaluation of smoothness cannot be performed for a specimen having a shape different from that of the specimen. Therefore, the purpose of the present invention is to make it possible to absolutely evaluate the smoothness of any shape of the object by quantifying the slit light image projected onto the object.
前記目的を達成して従来技術の問題点を解決す
るため、第１の考案に係る平滑性評価装置は、被
検体の表面に少なくとも２つのスリツト光像を投
影する光像投影手段と、該スリツト光像を撮影し
て２値画像を生成する２値画像生成手段と、該２
値画像に基づき各スリツト光像の縁線データを抽
出する縁線データ抽出手段と、隣接する縁線デー
タのそれぞれについて、前記画像上の所定方向に
おける平均値を求め、この平均値と該平均値を求
めた当該縁線データとの前記所定方向における偏
差を演算する第１の偏差演算手段と、前記隣接す
る２縁線における、前記第１の偏差演算手段によ
り演算された値の前記所定方向における偏差を演
算する第２の偏差演算手段と、該第２の偏差演算
手段により演算された値の総和を演算する総和演
算手段とを備えたものである。
In order to achieve the above object and solve the problems of the prior art, the smoothness evaluation device according to the first invention includes a light image projection means for projecting at least two slit light images onto the surface of a subject; a binary image generating means for generating a binary image by photographing an optical image;
An edge line data extraction means extracts edge line data of each slit light image based on the value image, and calculates an average value in a predetermined direction on the image for each of the adjacent edge line data, and calculates this average value and the average value. a first deviation calculating means for calculating a deviation in the predetermined direction from the edge line data obtained by calculating the value of the value calculated by the first deviation calculating means for the two adjacent edge lines in the predetermined direction; The apparatus includes a second deviation calculation means for calculating a deviation, and a sum calculation means for calculating the sum of the values calculated by the second deviation calculation means.
また、第２の考案に係る平滑性評価装置は、被
検体表面に少なくとも２つのスリツト光像を投影
する光像投影手段と、該スリツト光像を撮影して
２値画像を生成する２値画像生成手段と、該２値
画像に基づき各スリツト光像の縁線データを抽出
する縁線データ抽出手段と、該縁線データに基づ
き隣接する２縁線間の距離の前記画像上の所定方
向における平均値を演算する平均距離演算手段
と、該平均距離と上記２縁線間の距離との差を演
算する距離差演算手段と、該距離差演算手段によ
り演算された値に基づき、平均値を演算する距離
差平均値演算手段とを備えたものである。 Further, the smoothness evaluation device according to the second invention includes a light image projecting means for projecting at least two slit light images onto the surface of a subject, and a binary image generating means for generating a binary image by photographing the slit light images. a generating means; an edge line data extracting means for extracting edge line data of each slit light image based on the binary image; and an edge line data extraction means for extracting edge line data of each slit light image based on the binary image; An average distance calculation means for calculating an average value, a distance difference calculation means for calculating the difference between the average distance and the distance between the two edge lines, and an average value based on the value calculated by the distance difference calculation means. and a distance difference average value calculation means for calculating the distance difference average value.
縁線データ抽出手段において、２値画像に基づ
各スリツト光線の縁線データを抽出し、第１の偏
差演算手段において隣接する縁線データのそれぞ
れについて画像上の所定方向における平均値を求
め、この平均値と該平均値を求めた当該縁線デー
タとの前記所定方向における偏差を演算する。
The edge line data extraction means extracts the edge line data of each slit ray based on the binary image, and the first deviation calculation means calculates the average value of each of the adjacent edge line data in a predetermined direction on the image, A deviation in the predetermined direction between this average value and the edge line data for which the average value was calculated is calculated.
さらに第２の偏差演算手段において、前記第１
の偏差演算手段により演算された値の前記所定方
向における偏差を演算する。 Furthermore, in the second deviation calculation means, the first
A deviation in the predetermined direction of the value calculated by the deviation calculation means is calculated.
そして、総和演算手段において第２の偏差演算
手段により演算された値の総和を演算し、該総和
に基づいて被検体表面の平滑性を評価する。 Then, the sum calculation means calculates the sum of the values calculated by the second deviation calculation means, and the smoothness of the surface of the subject is evaluated based on the sum.
この場合、被検体固有のうねり（縁線データの
低周波部分に相当）については、隣接する縁線間
では偏差が相殺されるが、一方被検体表面の凸凹
（縁線データの高周波部分に相当）については、
隣接する２縁線間では偏差を生じることになる。
このため、隣接する２縁線間の偏差の程度を求め
れば、これに基づいて被検体表面の平滑性が評価
される。 In this case, deviations from the undulations specific to the object (corresponding to the low frequency part of the edge line data) are canceled out between adjacent edge lines, but on the other hand, the unevenness of the object surface (corresponding to the high frequency part of the edge line data) is canceled out between adjacent edge lines. )about,
A deviation will occur between two adjacent edge lines.
Therefore, by determining the degree of deviation between two adjacent edge lines, the smoothness of the surface of the subject can be evaluated based on this.
また第２の考案では、上記縁線データに基づき
平均距離演算手段において隣接する２縁線間の距
離の画像上の所定方向における平均値を演算す
る。そして、この平均距離と上記２縁線間の距離
との差を距離差演算手段で求め、この差に基づき
平均値を距離差平均値演算手段で演算し、この演
算された平均値に基づいて被検体の表面の平滑性
を評価する。 In the second invention, based on the edge line data, the average distance calculating means calculates the average value of the distance between two adjacent edge lines in a predetermined direction on the image. Then, the difference between this average distance and the distance between the two edge lines is calculated by the distance difference calculating means, and based on this difference, the average value is calculated by the distance difference average value calculating means. Evaluate the smoothness of the surface of the object.
以下、添付図面に基づいて本考案の実施例を説
明する。
Embodiments of the present invention will be described below based on the accompanying drawings.
第１図は本考案に係る平滑性評価装置の一例を
示すものである。同図において、１は車体パネル
等の被検体、２はスリツト、３は光源、４は被検
体に投影された光像、５は撮像装置、１０は信号
処理部である。この信号処理部１０は、撮像装置
５の出力信号をデジタル信号に変換するＡ／Ｄ変
換器１１と、このＡ／Ｄ変換器１１を介して入力
した画像信号から２値画像を生成する２値化部１
２と、この２値画像に基づきスリツト光像の縁線
データを抽出する縁線抽出部１３と、隣接する縁
線間の形状差を求めて、該形状差から被検体１の
平滑性を評価する評価部１４と、各部において処
理された画像信号を格納する画像メモリ１５とを
備えている。また、２０は評価結果等を画像表示
するCRTデイスプレイ装置である。尚、この実
施例装置では、スリツト２および光源３が本考案
の光像投影手段を構成し、撮像装置５、Ａ／Ｄ変
換部１１および２値化部１２が本考案の２値画像
生成手段を構成していることになる。 FIG. 1 shows an example of a smoothness evaluation device according to the present invention. In the figure, 1 is an object to be inspected such as a car body panel, 2 is a slit, 3 is a light source, 4 is a light image projected onto the object, 5 is an imaging device, and 10 is a signal processing section. This signal processing unit 10 includes an A/D converter 11 that converts the output signal of the imaging device 5 into a digital signal, and a binary image that generates a binary image from an image signal inputted via this A/D converter 11. conversion part 1
2, an edge line extraction unit 13 that extracts edge line data of the slit light image based on this binary image, and a shape difference between adjacent edge lines, which evaluates the smoothness of the object 1 from the shape difference. and an image memory 15 that stores image signals processed in each section. Further, 20 is a CRT display device that displays images of evaluation results and the like. In this embodiment, the slit 2 and the light source 3 constitute the optical image projection means of the present invention, and the imaging device 5, the A/D conversion section 11, and the binarization section 12 constitute the binary image generation means of the present invention. This means that it consists of
次に本装置の作動を説明する。 Next, the operation of this device will be explained.
まず、スリツト２を通して光源３の光を被検体
１に投影し、該被検体１上にスリツトに応じた
明、暗のスリツト光像を形成する。このスリツト
光像を撮像装置５によつて撮影し、Ａ／Ｄ変換器
１１にて信号変換した後、該画像信号を画像メモ
リ１８に格納する。この場合の画像Ｉ（ｘ，ｙ）
を第２図に例示する。尚、同図において斜線部は
暗部、他の部分は明部（光部）である。 First, light from a light source 3 is projected onto the subject 1 through the slit 2 to form a bright and dark slit light image on the subject 1 according to the slit. This slit light image is photographed by the imaging device 5, and after signal conversion is performed by the A/D converter 11, the image signal is stored in the image memory 18. Image I(x,y) in this case
is illustrated in FIG. In the figure, the shaded area is a dark area, and the other areas are bright areas (light areas).
次に２値化部１２において、画像Ｉ（ｘ，ｙ）
を適当な閾値VThで２値化し、Ｉ（ｘ，ｙ）≧VTh
の時Ｊ（ｘ，ｙ）＝１，Ｉ（ｘ，ｙ）＜VThの時Ｊ
（ｘ，ｙ）＝０なる２値画像Ｊ（ｘ，ｙ）を生成し、
再び画像メモリ１５にストアする（第３図参照）。 Next, in the binarization unit 12, the image I(x,y)
is binarized using an appropriate threshold V Th , and I(x, y)≧V Th
When J (x, y) = 1, when I (x, y) < V Th, J
Generate a binary image J(x,y) where (x,y)=0,
The image is stored in the image memory 15 again (see FIG. 3).
次に、縁線抽出部１３において、２値画像Ｊ
（ｘ，ｙ）から縁線を抽出し、第４図に示すよう
な画像Ｋ（ｘ，ｙ）を得る。この場合の処理内容
を第６図に例示するが、基本的には、縁線上端
は、Ｊ（ｘ，ｙ−１）＝０かつＪ（ｘ，ｙ）＝１なる
点をすべて抽出することによつて得、縁線下端
は、Ｊ（ｘ，ｙ−１）＝１かつＪ（ｘ，ｙ）＝０なる
点をすべて抽出することによつて得るものであ
る。そして抽出した縁線上端にはＫ（ｘ，ｙ）＝１
なる値を与え、縁線下端にはＫ（ｘ，ｙ−１）＝２
なる値を与えることによつて縁線画像Ｋ（ｘ，ｙ）
を得、これを画像メモリ１５に格納する。次に、
評価部１４にて縁線画像Ｋ（ｘ，ｙ）に基づき縁
線形状の定量化処理を行なう。この処理では、ま
ず縁線画像Ｋ（ｘ，ｙ）についてラベル処理を行
ない、当該縁線データが第何番目のスリツト光
縁、下縁かを特定する。このラベル処理は、具体
的には画像Ｋ（ｘ，ｙ）をｙ方向にスキヤンした
際、何番目に出逢つた“１”又は“２”であるか
を識別してラベル付けするものであり、植縁を
lpu、下縁をlpLとすると、第５図に示すように上
から順次l1U，l1L，l2U，l2L，……となる。次に、
各データ系列から、隣接する縁線間の形状差を求
める。これは、隣接２縁線を各々の平均値回りに
正規化し、重ね合わせた時のずれの総和Ap（第８
図に例示した斜線部面積）で表され、表面が平滑
であるほど小さな値となる。具体的には次式与え
られる。 Next, in the edge line extraction unit 13, the binary image J
The edge line is extracted from (x, y) to obtain an image K(x, y) as shown in FIG. The processing contents in this case are illustrated in Fig. 6, but basically, all points where J(x, y-1) = 0 and J(x, y) = 1 are extracted from the upper end of the edge line. The lower end of the edge line is obtained by extracting all points where J(x,y-1)=1 and J(x,y)=0. Then, at the top of the extracted edge line, K (x, y) = 1
K(x,y-1)=2 at the lower edge of the edge line.
By giving a value of
is obtained and stored in the image memory 15. next,
The evaluation unit 14 performs a process of quantifying the shape of the edge line based on the edge line image K(x,y). In this process, first, a label process is performed on the edge line image K(x, y), and it is determined what number of slit light edge and lower edge the edge line data is. Specifically, this labeling process identifies and labels the number of "1" or "2" encountered when the image K(x, y) is scanned in the y direction. , the planting
Assuming that l pu is the lower edge and l pL is the lower edge, then as shown in FIG. 5, from the top, they are l 1U , l 1L , l 2U , l 2L , . . . . next,
From each data series, the shape difference between adjacent edge lines is determined. This is the sum of deviations A p (8th
(area of the shaded area shown in the figure), and the smoother the surface, the smaller the value. Specifically, the following equation is given.
AP＝M
〓x=0
｜lPU(x)−PU(x)
−（lPL(x)−PL(x)｜
PU(x)＝１／Ｍ＋１M
〓x=0
lPU(x)
PL(x)＝１／Ｍ＋１M
〓x=0
lPL(x)
そして各スリツト光像に対してA1，A2，……，
AQ（Ｑ：観測画面内のスリツト光像の数）を求め
Ａ＝１／Ｑ（A1＋A2＋……AQ）をもつて、被検体
１の表面平滑性評価値とする。このＡの算出過程
を第７図に示す。被検体の形状が平滑であるほど
前記APの値は小さくなり、従つて平滑性評価値
Ａも小さくなり、従つて平滑性評価値Ａも小さく
なる。例えば、第９図（）（）に示すように
スリツト光像の隣接する縁線がそろつて同様な変
化を示している場合は平滑性評価値Ａは小さく、
第９図（）のように線線が細くうねつているよ
うな場合は平滑性評価Ａは大きな値になる。尚、
こうして算出した平滑性評価値Ａ及び画像処理の
処理結果等は必要に応じてCRTデイスプレイ２
０に表示されることができる。A P = M 〓 x=0 ｜l PU (x)− PU (x) −(l PL (x)− PL (x)｜ PU (x)=1/M+1 M 〓 x=0 l PU (x) PL (x)=1/M+1 M 〓 x=0 l PL (x) And for each slit light image, A 1 , A 2 ,...,
A Q (Q: number of slit light images in the observation screen) is calculated and A=1/Q (A 1 +A 2 +...A Q ) is used as the surface smoothness evaluation value of the object 1. The calculation process for A is shown in FIG. The smoother the shape of the object, the smaller the value of A P becomes, and therefore the smoothness evaluation value A becomes smaller, and therefore the smoothness evaluation value A also becomes smaller. For example, as shown in FIG. 9()(), when the adjacent edge lines of the slit light image are aligned and show similar changes, the smoothness evaluation value A is small;
When the lines are thin and undulating as shown in FIG. 9(), the smoothness evaluation A takes a large value. still,
The smoothness evaluation value A calculated in this way and the processing results of image processing are displayed on the CRT display 2 as necessary.
0 can be displayed.
また、このように隣接する縁線間の形状差を求
める以外にも、同様の効果をもたらすパラメータ
として、隣接縁線間の距離のばらつきσを考えて
もよい。 In addition to determining the shape difference between adjacent edge lines in this way, the variation σ in the distance between adjacent edge lines may be considered as a parameter that brings about the same effect.
今、Ｐ番めのスリツト光像の幅（上縁線−下縁
線間の距離）WP（Ｘ）を考えると、
WP（Ｘ）＝lPL(x)−lPU(x)（０≦ｘ≦μ）
で与えられる。 Now, considering the width of the P-th slit light image (distance between the upper edge line and the lower edge line) W P (X), W P (X)=l PL (x)−l PU (x)( 0≦x≦μ).
すなわち、各スリツト光像の幅のばらつきσP
は、
σP＝１／Ｍ＋１M
〓x=0
｛WP(x)−P(x)｝2 P(x)＝１／Ｍ＋１M
〓x=0
WP(x)
で与えられる。そして、各スリツト光像に対して
σ1，σ2，……σQ（Ｑ：観測画面内の縞数）を求め、
σ＝１／Ｑ（σ1＋σ2＋……＋σQ）をもつて、被検体
の表面平滑性評価値とする。平滑な面であるほ
ど、σは小さい値をとることは前記実施例の場合
と同様である。 In other words, the variation in the width of each slit optical image σ P
is given by σ P =1/M+1 M 〓 x=0 {W P (x)− P (x)} 2 P (x)=1/M+1 M 〓 x=0 W P (x). Then, calculate σ 1 , σ 2 , ...σ Q (Q: number of fringes in the observation screen) for each slit light image,
Let σ=1/Q (σ 1 +σ 2 +...+σ Q ) be the surface smoothness evaluation value of the object. As in the previous embodiment, the smoother the surface, the smaller the value of σ.
以上説明したように、本考案に係る平滑性評価
装置は、隣接するスリツト光像縁線間の偏差に基
づいて被検体の平滑性を評価するようにしたか
ら、被検体の形状が異なつていても、その形状変
化が緩やかである限りは形状変化の影響を排除す
ることができ、材料固有のうねりに影響されず
に、各被検体を絶対評価することができる。
As explained above, since the smoothness evaluation device according to the present invention evaluates the smoothness of the object based on the deviation between adjacent slit light image edge lines, it is possible to However, as long as the shape change is gradual, the influence of the shape change can be eliminated, and each specimen can be absolutely evaluated without being affected by the inherent waviness of the material.
第１図は本考案に係る平滑性評価装置の実施例
を示す図、第２図は撮像装置から入力する画像例
を示す図、第３図は２値画像を例示する図、第４
図は抽出した縁線を例示する図、第５図は縁線に
ついてのラベル処理を例示する図、第６図は縁線
抽出のフローチヤート、第７図は本考案に係る平
滑性評価処理例を示すフローチヤート、第８図は
本考案に係る平滑性評価の原理説明図、第９図は
被検体形状によるスリツト光像の各種の例を示す
図、第１０図は従来の平滑性評価装置例を示す図
である。
１……被検体、２……スリツト、３……光源、
４……スリツト光像、５……撮像装置、１０……
信号処理部、１１……Ａ／Ｄ変換部、１２……２
値化部、１３……縁線抽出部、１４……評価部。
FIG. 1 is a diagram showing an embodiment of the smoothness evaluation device according to the present invention, FIG. 2 is a diagram showing an example of an image input from an imaging device, FIG. 3 is a diagram illustrating a binary image, and FIG.
The figure is a diagram illustrating extracted edge lines, Figure 5 is a diagram illustrating label processing for edge lines, Figure 6 is a flowchart of edge line extraction, and Figure 7 is an example of smoothness evaluation processing according to the present invention. 8 is a diagram explaining the principle of smoothness evaluation according to the present invention, FIG. 9 is a diagram showing various examples of slit light images depending on the shape of the object, and FIG. 10 is a conventional smoothness evaluation device. It is a figure which shows an example. 1...Object, 2...Slit, 3...Light source,
4... Slit light image, 5... Imaging device, 10...
Signal processing section, 11...A/D conversion section, 12...2
Value conversion section, 13... edge line extraction section, 14... evaluation section.
Claims (1)
像を投影する光像投影手段と、 該スリツト光像を撮影して２値画像を生成す
る２値画像生成手段と、 該２値画像に基づき各スリツト光像の縁線デ
ータを抽出する縁線データ抽出手段と、 隣接する縁線データのそれぞれについて、前
記画像上の所定方向における平均値を求め、こ
の平均値と該平均値を求めた当該縁線データと
の前記所定方向における偏差を演算する第１の
偏差演算手段と、 前記隣接する２縁線における、前記第１の偏
差演算手段により演算された値の前記所定方向
における偏差を演算する第２の偏差演算手段
と、 該第２の偏差演算手段により演算された値の
総和を演算する総和演算手段と、 を備えたことを特徴とする平滑性評価装置。 (2) 被検体の表面に少なくとも２つのスリツト光
像を投影する光像投影手段と、 該スリツト光像を撮影して２値画像を生成す
る２値画像生成手段と、 該２値画像に基づき各スリツト光像の縁線デ
ータを抽出する縁線データ抽出手段と、 該縁線データに基づき隣接する２縁線間の距
離の前記画像上の所定方向における平均値を演
算する平均距離演算手段と、 該平均距離と上記２縁線間の距離との差を演
算する距離差演算手段と、 該距離差演算手段により演算された値に基づ
き、平均値を演算する距離差平均値演算手段
と、 を備えたことを特徴とする平滑性評価装置。[Claims for Utility Model Registration] (1) A light image projection means for projecting at least two slit light images onto the surface of a subject; and a binary image generation means for photographing the slit light images to generate a binary image. and edge line data extracting means for extracting edge line data of each slit light image based on the binary image; for each of the adjacent edge line data, an average value in a predetermined direction on the image is determined, and this average value is calculated. and the edge line data for which the average value has been calculated, in the predetermined direction. A smoothness evaluation device comprising: second deviation calculation means for calculating the deviation in the predetermined direction; and summation calculation means for calculating the sum of the values calculated by the second deviation calculation means. . (2) a light image projection means for projecting at least two slit light images onto the surface of the subject; a binary image generation means for capturing the slit light images to generate a binary image; edge line data extraction means for extracting edge line data of each slit light image; and average distance calculation means for calculating an average value of the distance between two adjacent edge lines in a predetermined direction on the image based on the edge line data. , distance difference calculation means for calculating the difference between the average distance and the distance between the two edge lines; distance difference average value calculation means for calculating the average value based on the value calculated by the distance difference calculation means; A smoothness evaluation device comprising:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP1988013634U JPH0544725Y2 (en) | 1988-02-05 | 1988-02-05 |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP1988013634U JPH0544725Y2 (en) | 1988-02-05 | 1988-02-05 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH01120606U JPH01120606U (en) | 1989-08-16 |
| JPH0544725Y2 true JPH0544725Y2 (en) | 1993-11-15 |
Family
ID=31224108
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP1988013634U Expired - Lifetime JPH0544725Y2 (en) | 1988-02-05 | 1988-02-05 |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH0544725Y2 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7471383B2 (en) * | 2006-12-19 | 2008-12-30 | Pilkington North America, Inc. | Method of automated quantitative analysis of distortion in shaped vehicle glass by reflected optical imaging |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS60200141A (en) * | 1984-03-26 | 1985-10-09 | Hitachi Ltd | Detecting method of surface form of specular object |
| JPS61223605A (en) * | 1985-03-29 | 1986-10-04 | Fuji Photo Film Co Ltd | Method for inspecting surface shape |
| JPS6298204A (en) * | 1985-10-25 | 1987-05-07 | Omron Tateisi Electronics Co | Recognizing method for object |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS5584512U (en) * | 1979-12-19 | 1980-06-11 |
-
1988
- 1988-02-05 JP JP1988013634U patent/JPH0544725Y2/ja not_active Expired - Lifetime
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS60200141A (en) * | 1984-03-26 | 1985-10-09 | Hitachi Ltd | Detecting method of surface form of specular object |
| JPS61223605A (en) * | 1985-03-29 | 1986-10-04 | Fuji Photo Film Co Ltd | Method for inspecting surface shape |
| JPS6298204A (en) * | 1985-10-25 | 1987-05-07 | Omron Tateisi Electronics Co | Recognizing method for object |
Also Published As
| Publication number | Publication date |
|---|---|
| JPH01120606U (en) | 1989-08-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US5142592A (en) | Method and apparatus for detection of parallel edges in image processing | |
| EP1430446B1 (en) | Image processing method for appearance inspection | |
| CN105787870A (en) | Graphic image splicing fusion system | |
| US5621825A (en) | Image processor, image processing method and apparatus applying same | |
| JPH0658716A (en) | Detecting method of edge on image | |
| US5881164A (en) | Image data processing method and image data processing apparatus | |
| JPH1173509A (en) | Device and method for recognizing image information | |
| JPH0544725Y2 (en) | ||
| JPH11312243A (en) | Facial region detector | |
| JP3267107B2 (en) | Background image creation method and image processing apparatus | |
| Xu et al. | Effective target binarization method for linear timed address-event vision system | |
| JPH06201715A (en) | Moving object detecting device | |
| JP2743509B2 (en) | Method for determining binarization threshold of floc measuring device | |
| CN113382134B (en) | Focusing debugging method of linear array industrial camera | |
| JPH0645845Y2 (en) | Automatic surface smoothness inspection device | |
| KR100212845B1 (en) | Method for calculating direction of edge in digital image data | |
| Sahri et al. | A Comprehensive Comparative Experiment of Edge Detection Algorithms for OMR | |
| JPH03237571A (en) | Device for calculating binarizing threshold of image | |
| JPH064670A (en) | Pattern matching device for gradation picture | |
| JPH10170451A (en) | Defect-inspecting apparatus | |
| JPH08178795A (en) | Lcd panel detect inspection instrument | |
| JPH05225336A (en) | Method for extracting boundary and device therefor | |
| JP3048718B2 (en) | Vertex detector | |
| JPH04276887A (en) | Image processor | |
| JP2000268184A (en) | Image processing device and method and recording medium |