JPH11132901A - Optical member-inspecting apparatus, image-processing apparatus and computer readable medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an optical member-inspecting apparatus which can automatically judge whether a linear failure detected in an optical member to be inspected is caused by a flaw or a flash. SOLUTION: When image data corresponding to an optical member to be inspected are compositd in an image memory area 62a, a CUP 60 takes out images of failure factors one by from the image data and recognizes for every image of the taken-out individual failure factors whether the failure factor is a spot defect or a linear defect. When recognizing that the failure factor is the linear defect, the CPU 60 recognizes further whether the failure factor is a flaw or a flash. In the recognition process, the CPU 60 measures a maximum fillet and a minimum fillet of the taken image, thereby recognizing that the failure factor is the flaw only when a ratio of the minimum fillet to the maximum fillet is smaller than 50% and the minimum fillet is smaller than 50 dots, and that the failure factor is the flash in other than the above case.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

[0001] 1. Field of the Invention [0002] The present invention relates to an optical member inspection apparatus for detecting a cause of a defect of an optical member such as a lens, an image processing apparatus, and a computer readable medium storing a program.

[0002]

2. Description of the Related Art An optical member such as a lens or a prism is used to refract an incident light beam regularly or to travel in parallel.
It is designed to converge or diverge at one point or linearly. However, when lint or the like is mixed in the optical member during the formation of the optical member (so-called “burr”), scratches or the like are generated on the surface of the optical member due to human handling after molding, Since the incident light flux is disturbed, desired performance cannot be obtained.

[0003] For this reason, various optical member inspection apparatuses for automatically detecting the cause of a defect in an optical member and automatically determining the quality of the optical member have been proposed. For example, the present applicant discloses in Japanese Patent Application No. 9-50760 an optical member inspection apparatus that images the entire inspection target optical member using a rotation mechanism and a line sensor that rotates the inspection target optical member around its optical axis. ,Proposed.

[0004] In any of the various optical member inspection apparatuses, the detected defect factors are classified by type, and the defect factors are classified by type. Of the optical members to be inspected were comprehensively evaluated based on the statistical results.

[0005]

However, the algorithm of the image processing program which has been conventionally used to classify the causes of failure can only classify whether each cause of failure is a point defect or a linear defect. Did not. In other words, whether a certain defect factor is a point defect or a linear defect depends on the fillet ratio (filler ratio) in two directions orthogonal to the defect factor and a rectangle having the same fillet width as the defect factor. It is easily recognizable based on the ratio of the actual area of the defect factor to the area of the region. However, when a certain defect factor is classified as a linear defect, an algorithm for classifying whether the linear defect is a flaw or a fluff has not been established yet.

[0006] Therefore, according to the conventional optical member inspection apparatus, even if a scratch that can be repaired by merely polishing the surface of the optical member is generated, the same evaluation as in the case of a limp that cannot be repaired at all is obtained. By the standard, it was uniformly determined to be defective.

[0007] The object of the present invention is to solve the above problems.
Optical member inspection device, image processing device, and computer-readable medium storing a program that can automatically determine whether a linear defect factor detected in an inspection target optical member is a flaw or a fluff Is to provide.

[0008]

The invention described in each claim is
It has been made to solve the above problems. First,
An optical member inspection apparatus according to claim 1, wherein the image pickup apparatus captures an inspection target optical member and outputs image data including an image of the inspection target optical member, and an image that stores image data output from the imaging apparatus. A memory, an area specifying means for specifying an area indicating a cause of a defect of the inspection target optical member from the image data stored in the image memory, and a maximum fillet and a minimum fillet of the area specified by the area specifying means. A fillet measuring means for measuring each; a ratio comparing means for comparing the ratio of the maximum fillet to the minimum fillet measured by the fillet measuring means with a predetermined threshold value; and the region specifying means based on a comparison result by the ratio comparing means. Defect type recognizing means for recognizing whether the area specified by the above indicates any of a flaw and a bulge of the optical member to be inspected. Characterized in that was.

With such a configuration, the imaging device captures an image of the optical member to be inspected and outputs image data including an image of the optical member to be inspected. The output image data is stored in the image memory. The area specifying means specifies, from the image data stored in the image memory, an area indicating a cause of a defect of the inspection target optical member. Each time the area specifying means specifies one area in this way, the fillet measuring means measures the maximum fillet and the minimum fillet of the specified area, respectively, and the ratio comparing means sets the measured maximum fillet and the minimum fillet. Is compared with a predetermined threshold value, and the defect factor type recognizing means determines, based on the comparison result by the ratio comparing means, that the area specified by the area specifying means is any one of a flaw and a fluff of the optical member to be inspected. Is recognized. If the ratio between the maximum fillet and the minimum fillet is obtained in this manner, the approximate shape of the defect factor can be recognized, so that it is automatically recognized whether the linear defect factor is a flaw or a fluff. It becomes possible.

According to a second aspect of the present invention, in the optical member inspection apparatus, the failure factor type recognizing means determines that a ratio of the minimum fillet to the maximum fillet is larger than the predetermined threshold. When the comparison result indicates that the area specified by the area specifying means indicates the fluff of the optical member to be inspected.

According to a third aspect of the present invention, the optical member inspection apparatus according to the second aspect further includes a minimum fillet comparing unit that compares a minimum fillet measured by the fillet measuring unit with a predetermined threshold value, and When the comparison result by the ratio comparing means indicates that the ratio of the minimum fillet to the maximum fillet is smaller than the predetermined threshold, the recognizing means determines that the minimum fillet is smaller than the predetermined threshold. Only when the comparison result by the fillet comparing means indicates, the area specified by the area specifying means is recognized as indicating a flaw in the optical member to be inspected, and otherwise, the area specified by the area specifying means is the It is specified by recognizing that the inspection target optical member shows a fluff.

According to a fourth aspect of the present invention, in the optical member inspection apparatus according to the first aspect, a work memory for holding image data in accordance with rectangular coordinates and an area specified by the area specifying means are stored in the image memory. Image data cutting means for cutting out the image data and pasting the image data to the working memory, wherein the fillet measuring means rotates the image data pasted to the working memory; Filet measuring means for measuring the fillet of the area in each axis direction of the Cartesian coordinates while the means is rotating the image data pasted on the working memory; and the maximum fillet measured by the fillet measuring means. Maximum fillet specifying means for specifying a fillet as the maximum fillet; and a maximum fillet measured by the fillet measuring means. By becoming a fillet from a minimum fillets specifying means for specifying as the minimum fillet, those identified.

According to a fifth aspect of the present invention, in the optical member inspection apparatus according to the first aspect, a work memory for holding image data in accordance with rectangular coordinates and an area specified by the area specifying means are stored in the image memory. Image data extracting means for cutting out from the image data and pasting it to the working memory, and a fillet measuring means for measuring an inertial principal axis vector of the image data pasted to the working memory; and Image data rotating means for rotating image data pasted on the working memory such that the inertia principal axis vector is oriented in any axis direction of the rectangular coordinates, and the rectangular coordinates after rotation by the image data rotating means. A fillet measuring means for measuring the fillet of the area in each axial direction, and a fillet measuring means. Maximum fillet specifying means for specifying the larger one of the fillets measured as the maximum fillet, and minimum fillet specifying means for specifying the smaller one of the fillets measured by the fillet measuring means as the minimum fillet. That is what we have identified.

According to a sixth aspect of the present invention, in the optical member inspection apparatus according to the first aspect, a work memory for storing image data in accordance with rectangular coordinates and an area specified by the area specifying means are stored in the image memory. Image data extracting means for cutting out from the image data and pasting it to the working memory, and a fillet measuring means for measuring an inertial principal axis vector of the image data pasted to the working memory; and A fillet measuring means for measuring the fillet of the region in a direction parallel to and perpendicular to the inertial principal axis vector, and a fillet in a direction parallel to the inertial principal axis vector measured by the fillet measuring means as the maximum fillet. The maximum fillet specifying means for specifying, and the The fillet in a direction perpendicular to sexual principal axis vector that consists of a minimum fillets specifying means for specifying as the minimum fillet, those identified.

According to a seventh aspect of the present invention, there is provided an image processing apparatus comprising: an image memory for storing image data obtained by imaging an optical member to be inspected; and an image memory for storing the image data to be inspected from the image data stored in the image memory. Area specifying means for specifying an area indicating a cause of failure of the optical member; fillet measuring means for measuring the maximum fillet and minimum fillet of the area specified by the area specifying means; and maximum fillet measured by the fillet measuring means. A ratio comparing unit that compares a ratio of the minimum fillet to a predetermined threshold value, and a region specified by the region specifying unit based on a comparison result obtained by the ratio comparing unit. And a failure factor type recognizing means for recognizing whether or not
It is characterized by having.

According to another aspect of the present invention, a computer having an image memory stores image data obtained by imaging an optical member to be inspected in the image memory. From the stored image data, an area indicating a cause of failure of the inspection target optical member is specified, the maximum fillet and the minimum fillet of the specified area are respectively measured, and the ratio between the measured maximum fillet and the minimum fillet is determined by a predetermined value. A program is stored which is compared with a threshold value and which recognizes, based on a result of the comparison, whether the specified area indicates a flaw or a fluff of the optical member to be inspected.

[0017]

Embodiments of the present invention will be described below with reference to the drawings.

[0018]

First Embodiment <Configuration of Optical Member Inspection Apparatus> The schematic configuration of an optical member inspection apparatus according to the first embodiment is shown in a side sectional view of FIG.
As shown in FIG. 1, the illumination lamp 1, the diffusion plate 2, and the imaging device 3 that constitute the optical member inspection device are arranged on the same optical axis l.

The image pickup apparatus 3 includes an image pickup lens 4 as a positive lens system, and an image pickup element (CCD line sensor having a plurality of pixels arranged in one direction) 5 for picking up an image formed by light converged by the image pickup lens 4. And from. In FIG. 1, the image sensor 5 is installed so that its pixel row is directed to the left and right. The pixel array of the image sensor 5 intersects with the optical axis 1 of the image pickup lens 4 perpendicularly at the center. Note that the imaging lens 4 is
The image pickup device 3 is attached to a frame (not shown) of the optical member inspection device so that the image pickup device 3 itself can move forward and backward in the direction of the optical axis l.

The image pickup element 5 repeatedly picks up an image in a line at predetermined time intervals (a period in which electric charges are appropriately accumulated in each pixel), and scans each pixel in the order in which the pixels are arranged. Output the accumulated charge. The charges output from the image sensor 5 in this manner are subjected to predetermined amplification processing and A / D conversion processing, and then input to the control device 6 as image data including luminance signals for one line.

The optical member 14 to be inspected is a circular lens as shown in FIG. 2 which is a plan view as viewed from the side of the image pickup device 3, and is held by a holder 15 attached to a frame (not shown) of the optical member inspection device. The imaging lens 4 is held such that its surface (the surface facing the imaging lens 4) is conjugate with the imaging surface of the imaging element 5. This holder 15
Has an annular shape centered on the optical axis l of the imaging lens 4, and holds the periphery of the inspection target optical member 14 over the entire periphery thereof. Therefore, as long as the center of the peripheral edge of the inspection target optical member 14 and the optical axis coincide with each other, the inspection target optical member 1
The optical axis 4 is coaxial with the optical axis 1 of the imaging lens 4.

The holder 15 is rotatable about an optical axis l of the imaging lens 4 in a plane orthogonal to the optical axis l. An annular gear 16 is formed on the periphery of the holder 15. This annular gear 16 meshes with the pinion gear 7 attached to the drive shaft of the drive motor 8. Accordingly, when the drive motor 8 rotates its drive shaft, the holder 15 is rotationally driven via the two gears 7 and 16, and the inspection target optical member 14 held by the holder 15 is orthogonal to the optical axis l. It is rotationally driven in the plane.

The magnification of the image pickup lens 4 (ie, the position of the image pickup device 3 itself and the position of the image pickup lens 4 with respect to the image pickup element 5) is determined by the total width in the diameter direction of the optical member 14 to be inspected. Is adjusted so that an image can be formed. In FIG. 2, an imaging target area for one line that can be imaged by the imaging element 5 is indicated by a two-dot chain line.

The illumination lamp 1 is an incandescent lamp that emits illumination light (white light), and is fixed to a frame (not shown) of the optical member inspection device. The diffusing plate 2 disposed between the illumination lamp 1 and the optical member 14 to be inspected has a structure shown in FIG.
As shown in (1), it has a disk shape larger in diameter than the optical member 14 to be inspected, and its surface is processed as a rough surface. Accordingly, the diffusion plate 2 can receive the illumination light emitted from the illumination lamp 1 on the entire back surface and diffuse the illumination light toward the optical member 14 to be inspected. The diffusion plate 2 is fixed to a frame (not shown) of the optical member inspection device so that the center of the diffusion plate 2 is orthogonal to the optical axis 1 of the imaging lens 4.

A light-shielding plate 9 having a band shape is attached on the surface of the diffusion plate 2 with its longitudinal direction oriented parallel to the direction of the pixel rows of the image sensor 5. The center of the light shielding plate 9 coincides with the optical axis l of the imaging lens 4. The total length of the light shielding plate 9 in the longitudinal direction is longer than the diameter of the optical member 14 to be inspected. Then, as shown in FIG. 2, when viewed from the position of the imaging device 3, both ends of the light shielding plate 9 protrude outside the outer edge of the inspection target optical member 14. Further, as shown in FIG. 4 which is a cross-sectional view of the optical member inspection apparatus in a direction orthogonal to the direction of the pixel rows of the image sensor 5, the width of the light shielding plate 9 is the margin of light incident on each pixel of the image sensor 5. It is wider than the interval between the light beams m and m.

The control device 6 determines whether the optical member 14 to be inspected is a non-defective product or a defective product based on the image data input from the image pickup device 3. This is a processing device that supplies a drive current.

FIG. 3 is a block diagram showing the internal circuit configuration of the control device 6. As shown in FIG.
Is composed of a CPU 60, a frame memory 61, a host memory 62, and a motor drive circuit 63 which are interconnected via a bus B.

The frame memory 61 is a buffer in which image data input from the imaging device 3 is written. The host memory 62 includes an image memory area 62a, a work memory area 62b, and an image processing program storage area 62c as a computer-readable medium for storing an image processing program executed by the CPU 60. The image memory area 62a is an area in which the image data written in the frame memory 61 is written every predetermined time from the top row in row units. This image memory area 62
The image data written in a is data based on the polar coordinate system (polar coordinate data) because of the imaging method of the imaging device 3, but the CPU 60 executes the image processing program to perform the coordinate conversion (polar coordinate-rectangular coordinate conversion). Is converted into data in a rectangular coordinate system (rectangular coordinate data). The work memory area 62b is an area in which the CPU 60 executes the image processing program and temporarily stores image data for each defect factor cut out from the image memory area 62a in accordance with rectangular coordinates.

The motor drive circuit 63 supplies a drive current for driving the drive motor 8 such that the holder 15 and the optical member 14 to be inspected rotate counterclockwise at a constant speed when viewed from the imaging device 3 side. Supply.

The CPU 60 is a computer for controlling the entire control device 6, and includes an area specifying unit, an image data extracting unit, a fillet measuring unit, a ratio comparing unit, a minimum fillet comparing unit, and a defect factor type recognizing unit. Equivalent to. That is, the CPU 60 executes the image processing program stored in the image processing program storage area 62c of the host memory 62, and periodically stores the image data written in the frame memory 61 in the image memory area 62c of the host memory 62.
a, and when the polar coordinate data corresponding to the entire inspection target optical member 14 is synthesized in the image memory area 62a, the polar coordinate data is converted into rectangular coordinate data. Then, the CPU 60 detects whether or not there is a defect factor in the orthogonal coordinate data, cuts out an image of each detected defect factor, pastes the image into the work memory 62b, and
The type is recognized on 2b. Further, the CPU 6
A value of 0 classifies all failure factors for each type, statistic features of the failure factors for each classified type, and determines pass / fail based on the statistical result. In addition, the CPU 60 controls the frame memory 6
An instruction to supply a drive current to the drive motor 8 is issued to the motor drive circuit 63 in synchronization with the capture of image data from No. 1.

<Principle of Failure Factor Detection> In the optical member inspection apparatus configured as described above, in the plane of FIG. 4, light that can enter the image pickup lens 4 and enter each pixel of the image pickup element 5 is: It is a light beam having a light ray along the optical axis l of the imaging lens 4 as a principal ray, and only light passing between the marginal rays m, m shown in FIG. When the marginal rays m, m are traced in the opposite direction, they intersect at the surface of the inspection target optical member 14 and then spread toward the diffusion plate 2. Then, on the diffusion plate 2, the space between the marginal rays m, m is shielded by the light shielding plate 9. Therefore, as shown in FIG. 4, the image pickup target area of the optical element 14 to be inspected by the image pickup element 5 (a part conjugate with the light receiving surface of the pixel array of the image pickup element 5 with respect to the image pickup lens 5 and its vicinity in the optical axis direction) is defective. If there is no factor, no light is incident on each pixel of the image sensor 5. That is, the light n diffused from the side of the light-shielding plate 9 on the surface of the diffusion plate 2 is transmitted through the imaging target region in the inspection target optical member 14, but passes outside the marginal rays m, m. Does not enter. The light diffused from the side of the light-shielding plate 9 on the surface of the diffusion plate 2 and transmitted through a portion of the inspection target optical member 14 other than the imaging target region can enter the imaging lens 4. It does not converge on each pixel. Therefore, the image data output from the imaging device 3 is dark over the entire area except for a bright portion (due to diffused light on the side surface) corresponding to the outer edge of the inspection target optical member 14.

On the other hand, as shown in FIG. 2, when there are a flaw C and a dust D in the imaging target area on the surface of the optical member 14 to be inspected, as shown in FIG. When the light n diffused from the side portion of the plate 9 hits these flaws C and dust D, this light
And dust D. The diffused light n ′ diverges around the intersection of the marginal rays m, m, and a part of the light is incident on the pixels of the image sensor 5 via the image pickup lens 4. Therefore, images of the flaw C and the dust D (images brighter than the surroundings) are formed on the imaging surface of the imaging element 5.

The image pickup (charge accumulation and scanning) by the image pickup device 5 is performed every time the inspection target optical member 14 rotates by a predetermined angle in synchronization with the rotation of the inspection target optical member 14 by the drive motor 8. Then, every time imaging (charge accumulation and scanning) is performed by the imaging element 5, the linear image data is written into the frame memory 61 of the control device 6 and taken into the image memory area 62 a of the host memory 62. As a result, as the inspection target optical member 14 rotates, each line of image data captured by the imaging device 3 is sequentially written in each row of the image memory area 62a from the top row.

The horizontal axis of the image data (polar coordinate data) stored in the image memory area 62a of the host memory 62 when the optical member 14 to be inspected makes a half rotation is the center (optical axis) O of the optical member 14 to be inspected. , And the vertical axis indicates the rotation angle of the inspection target optical member 14 based on the diameter connecting the points A and B. CP of control device 6
U60 converts the polar coordinate data into rectangular coordinate data as described above.

When such coordinate conversion is completed for all the pixels of the polar coordinate data, the image data (orthogonal coordinate data) stored in the image memory area 62a is obtained by imaging the optical member 14 to be inspected by the area sensor. It is substantially equivalent to image data. Therefore, regardless of the position of the defect factor, the area of the defect factor in the rectangular coordinate data is directly proportional to the area of the actual defect factor, and the shape of the defect factor in the rectangular coordinate data is the same as the shape of the actual defect factor. Be similar. Therefore, based on the image data stored in the image memory area 62a, the CPU 60 can make a pass / fail judgment.

<Principle of Recognition of Type of Failure Factor> Next, the principle of recognition of the type of failure factor described above will be described. Recognition of this type includes a first step of determining whether the defect factor is a point defect (dust mixed in the optical member or dust adhering to the surface of the optical member) or a linear defect; If the defect is a linear defect, the second step is to determine whether the cause of the defect is a flaw or a fluff.

FIG. 6 shows the image data of the cause of the defect (burr) written from the image memory area 62a to the working memory area 62b. The working memory area 62b
Is an orthogonal coordinate system. As shown in FIG. 6, the maximum width in the x-axis direction of the failure factor is defined as “x fillet”, and the maximum width in the y-axis direction is defined as “y fillet”. The ratio of the larger one of the x fillet and the y fillet to the other is defined as “filler ratio”.

Incidentally, the x fillet and the y fillet are shown in FIG.
As shown in FIG. 3 and FIG. 14, the image data of the defective factor rotates and fluctuates in the work memory area 62b. Then, the value of the x fillet or y fillet that has become the maximum value during such rotation is defined as “maximum fillet”, and the value of the y fillet or x fillet that has become the minimum value is “minimum fillet”.
Is defined as The ratio of the minimum fillet to the maximum fillet is defined as “maximum fillet ratio”.

As a result of such a definition, in the case of a point defect,
The value of the x fillet and the value of the y fillet are very close irrespective of the rotational position of the defective image data, so that the fillet ratio is a very high value. However, the fillet ratio slightly fluctuates in accordance with the rotational position of the image data that causes the failure.
However, when the fillet ratio becomes minimum (50%) with the rotation of the image data of the defect factor, the area of the defect factor itself with respect to the product of the x fillet and the y fillet (the area of the rectangular area shown in FIG. 6). (Hereinafter referred to as “occupancy ratio”) is the maximum (100%), and the fillet ratio is the maximum (100%)
Is satisfied, the occupancy becomes the minimum (50%). Therefore, in the first stage recognition for recognizing whether the defect factor is a point defect or a linear defect, a threshold function whose occupancy is inversely proportional to the fillet ratio as shown in FIG. Is prepared, regardless of the rotation position of the image data of the defect factor,
If the combination of the fillet ratio and the occupancy measured for the cause of the defect exceeds this threshold function, it is recognized that the cause of the defect is a point defect.

However, based on the fillet ratio and the occupancy, it is not possible to recognize whether the linear defect is a flaw or a fluff. This is because, depending on the rotational position of the image data of the flaw and the rotational position of the image data of the fillet, the fillet ratio of the fluff may be higher than the fillet ratio of the flaw.

Meanwhile, the ratio of the minimum fillet to the maximum fillet width is very low because the scratch is formed by a straight line.
The fluff often has a characteristic that the ratio of the minimum fillet to the maximum fillet is often higher than the case of a flaw because the fluff is composed of a curve. Therefore, in the second stage recognition for recognizing whether the linear defect is a flaw or a fluff,
The maximum fillet ratio is used, and as shown in FIG. 8, when the maximum fillet ratio is 50% or more, it is always recognized as a fluff. However, in consideration of the possibility that the fluff is linear, if the maximum fillet ratio is less than 50%, it cannot be recognized whether the linear defect is flawed or fluffy. In this case, on the assumption that the scratch is absolutely thin, the value of the minimum fillet itself is used, and as shown in FIG. 8, the number of dots of the minimum fillet (the surface of the inspection target optical member 14 corresponding to one dot) is used. If the length is less than 15 dots, the linear defect is recognized as being flawed, and if it is 15 dots or more, the linear defect is recognized as being fuzzy. Is done.

<Control Processing> Next, in order to make a pass / fail judgment based on the principle of detecting the cause of the defect and the principle of recognizing the type of the cause of the defect, the control device according to the image processing program read from the image processing program storage area 62c. 6
The contents of the control processing executed by the (CPU 60) will be described with reference to the flowcharts of FIGS.

The control process shown in FIG. 9 starts when an unillustrated inspection start button connected to the control device 6 is pressed. In the first S001 after the start, the CPU 60
Instructs the motor drive circuit 63 to supply a drive current to the drive motor 8, and rotates the inspection target optical member 14 at a constant speed.

In the next step S002, the CPU 60 stores the image data written from the imaging device 3 into the frame memory 61 in the image memory area 62a of the host memory 62. In the next S003, the CPU 60 checks whether or not the polar coordinate data corresponding to the entire inspection target optical member 14 has been combined in the image memory area 62a by storing the image data in S002. If the polar coordinate data corresponding to the entire inspection target optical member 14 has not been synthesized yet, the process returns to S002, and waits for the imaging device 3 to write the image data obtained by the next imaging to the frame memory 61.

On the other hand, when the polar coordinate data corresponding to the entire inspection target optical member 14 is synthesized, the CPU
In step S004, the polarizer 60 performs a polar coordinate-rectangular coordinate conversion on the polar coordinate data.

In the next S005, the CPU 60 executes a labeling step. That is, the CPU 60 extracts, from the image data (orthogonal coordinate data) stored in the image memory area 62a, an area composed of a series of pixels having a luminance value equal to or greater than a predetermined threshold value, and uniquely identifies each extracted area. (Labels) n (n = 1, 2, 3,...) (Corresponding to area specifying means).

In the next step S006, the CPU 60 initializes a variable i for specifying the cause of the defect whose type is to be recognized, and sets it to "1" (corresponding to the area specifying means). Next S
In 007, the CPU 60 executes the type recognition process of the i-th defect factor. FIG. 10 shows a subroutine for recognizing the type of a defect factor executed in S007. In the first step S101 after entering this subroutine, the CPU 60
Stores the image of the i-th defect factor in the image memory area 62a.
Is cut out from the image data (orthogonal coordinate data) stored in the storage area 62b and pasted into the work memory area 62b (corresponding to an image data cutting means).

In the next step S102, the CPU 60 obtains the area (total number of pixels) of the image data of the defective factor attached to the working memory area 62b. In the next S103, the CPU 6
0 measures the x fillet and the y fillet of the defective factor at the rotational position at that time.

In the next step S104, the CPU 60 determines in step S10
The fillet ratio is calculated by executing the following equation (1) for the x fillet and the y fillet obtained in 3. Filler ratio = small fillet / larger fillet × 100 (1) Further, the CPU 60 calculates the following formula with respect to the x fillet and the y fillet obtained in S103 and the area of the defect factor obtained in S102. By performing (2), the occupancy is calculated.

Occupancy rate = Area of defective factor / (x fillet × y fillet) × 100 (2) The CPU 60 compares the fillet ratio and the occupancy rate with the threshold function shown in FIG. 7, and pastes it into the work memory area 62 b. It is determined whether the attached defect factor is a point defect or a linear defect. That is, if the intersection between the fillet ratio and the occupancy on the graph of FIG. 7 is located above the threshold function, the CPU 60 recognizes that the defect factor is a point-like defect, and determines the fillet ratio and the occupancy. Is located below the threshold function, it is recognized that the defect factor is a linear defect. When the CPU 60 recognizes that the defect factor is a point defect, the CPU 60
This defect factor recognition subroutine is terminated as it is, and the process returns to the main routine of FIG. On the other hand, the CPU 60
Indicates that if the defect factor is recognized as a linear defect,
The PU 60 advances the process to S105.

In the next step S105, the CPU 60 calculates the maximum fillet and the minimum fillet of the defective factor attached to the working memory area 62b (corresponding to fillet measuring means). FIG.
Reference numeral 1 denotes a maximum fillet and minimum fillet calculation subroutine executed in S105. In the first step S201 after entering this subroutine, the CPU 60 initializes a variable R indicating the rotation angle from the initial state of the image of the cause of the defect in the work memory area 62b and sets it to “0”.

In the next S202, the CPU 60 sets the variable Fm
ax is initialized and set to “0”, and a variable Fmin is initialized and set to “1000”. Next, the CPU 60
The x fillet and y fillet are measured at each rotation position and the variable Fm
S203 to S210 for updating ax and variable Fmin
Is executed. In the first step S203 after entering this loop processing, the x fillet and the y fillet of the defect factor pasted in the working memory area 62b at that time are measured, and the larger one of these is substituted for the variable Fcmax and the smaller one. Is assigned to the variable Fcmin (corresponding to fillet measuring means).

In the next S204, the CPU 60 determines whether the variable Fc
Check whether the value of max is greater than the value of variable Fmax. If the value of the variable Fcmax is larger than the value of the variable Fmax, the CPU 60 determines in step S205 that the variable Fcmax
By overwriting the value of max with the variable Fmax, this variable F
After max is updated, the process proceeds to S206 (corresponding to the maximum fillet specifying means). On the other hand, when the value of the variable Fcmax is equal to or less than the value of the variable Fmax, the CPU 60 advances the processing to S206 as it is.

In S206, the CPU 60 sets the variable Fcmin
Check whether the value of is smaller than the value of the variable Fmin. If the value of the variable Fcmin is smaller than the value of the variable Fmin, the CPU 60 determines in step S207 that the variable Fcmin
This variable Fmi by overwriting the value of n with the variable Fmin
After updating n, the process proceeds to S208 (corresponding to minimum fillet specifying means). On the other hand, when the value of the variable Fcmin is equal to or larger than the value of the variable Fmin, the CPU 60 advances the process to S208.

In S208, the CPU 60 checks whether or not the value of the variable R is 90 or more. If the value of the variable R is less than 90, the CPU 60 proceeds to S2
At 09, the value of the variable R is added by a. This value a
Indicates a rotation step of the image of the cause of the defect in the work memory area 62b. Specifically, this value a is “1”
(Degree) ".

In the next step S210, the CPU 60 rotates the image of the defective factor in the working memory area 62b in a fixed direction so that the rotation angle from the initial state becomes R degrees (corresponding to image data rotating means). After that, the CPU 60
Returns the process to S203, and measures each fillet as a cause of failure after rotation.

When it is determined in S208 that the value of the variable R has become 90 or more as a result of repeating the loop processing of S203 to S210 described above, that is, from the initial state of the image of the defective factor in the work memory area 62b. When the rotation angle becomes 90 degrees, the CPU 60 terminates this maximum fillet and minimum fillet calculation subroutine,
The process returns to the failure factor recognition routine of FIG. At this time,
The maximum fillet of the cause of the defect in the work memory area 62b is left in the variable Fmax, and the minimum fillet is left in the variable Fmin.

In the defective factor recognition routine of FIG. 10 to which the processing has been returned, the CPU 60 determines whether the variable
The following equation (3) is executed for Fmax and Fmin, and the maximum fillet ratio is calculated.

Maximum fillet ratio = Fmin / Fmax × 100 (3) In the next S107, the CPU 60 checks whether or not the maximum fillet ratio calculated in S106 is less than 50% (corresponding to a ratio comparing means). ). Then, if the maximum fillet ratio is 50% or more, the CPU 60 executes the processing in S110.
If the maximum fillet ratio is less than 50%, the process proceeds to S108.

In S108, the CPU 60 checks whether or not the value of the variable Fmin is less than 15 (corresponding to the minimum fillet comparing means). Then, if the value of variable Fmin is equal to or greater than 15, the process proceeds to S110, and if the value of variable Fmin is less than 15, the process proceeds to S109.

At S109, the CPU 60 recognizes that the defect factor in the work memory area 62b indicates a flaw (corresponding to a defect factor type recognizing means). On the other hand, in S110, the CP
U60 recognizes that the failure factor in the work memory area 62b indicates a fluff (corresponding to a failure factor type recognition unit). In any case, the CPU 60 ends the failure factor recognition routine and returns the processing to the main routine of FIG.

In the main routine of FIG. 9 to which the processing has been returned, the CPU 60 checks in the next S008 whether the variable i has reached the maximum value (the maximum numerical value labeled in S005). If the variable i has not yet reached the maximum value, the process returns to S007 after incrementing the variable i in S009, and executes the next failure factor type recognition process. On the other hand, when the variable i has reached the maximum value, the CPU 60 advances the processing to S010.

In S010, the CPU 60 classifies all the causes of failure in the image data in the image memory area 62a for each type recognized in S007. Then, statistics of the characteristic amounts of the failure factors are performed for each of the classified types. For example,
The total area of the classified defective factors, the maximum total fillet, the ratio of the maximum total fillet to the total area, and the like are calculated.

In the next S011, the CPU 60 sets
A pass / fail judgment is made based on the statistical result of 0. That is, S0
It is checked whether or not each value calculated in 10 exceeds a judgment threshold value prepared in advance for each value.

In the next S012, the CPU 60 sets
It is checked whether the result of the pass / fail judgment in 1 indicates a good product or a bad product. If the result of the pass / fail judgment indicates a non-defective product, in S013, the CPU 60 externally outputs (image display, audio output) indicating that the inspection target optical member 14 is a non-defective product. On the other hand, if the result of the determination indicates a defective product, the CPU 60 outputs, in S014, an external output (image display, audio output) indicating that the inspection target optical member 14 is a defective product. After the above, CPU
60 ends this control processing.

<Operation of the Embodiment> According to the present embodiment configured as described above, it is possible to transmit the light through the optical member 14 to be inspected, enter the imaging lens 4 and enter each pixel of the imaging element 5. The light is blocked by the light shielding plate 9 on the diffusion plate 2 in advance. Therefore, if no defect factor occurs in the inspection target optical member 14 in the imaging target region of the imaging element 5, the luminance values of the pixels in the image data imaged by the imaging element 5 are all black values (8 bits). "0" in the gray scale).

On the other hand, when a defect factor is generated in the optical member 14 to be inspected in a range where the image can be picked up by the image pickup device 5, light incident into the area from the side of the light-shielding plate 9 causes the defect factor. And a part of the diffused light enters the imaging lens 4. As a result, a bright image of a defect factor against the background of the dark shadow of the light-shielding plate 9 is formed on the image sensor 5.
Is formed on the image pickup surface. At this time, the luminance value of the pixel obtained by imaging the bright image of the cause of the defect in the image data is a value corresponding to the brightness of the image (and the degree of overlap between the image and the pixel of the image sensor 5) ( "1 to 255" in an 8-bit gray scale).

Then, while the optical member 14 to be inspected makes a half rotation, the image pickup by the image pickup device 5 is performed at a constant period, and the linear image data obtained by each image pickup is stored in the image memory area 62a (S002). ).

The CPU 60 of the control device 6 converts the polar coordinate data stored in the image memory area 62a into rectangular coordinate data (S004). As a result, in the orthogonal coordinate data, the cause of the defect of the inspection target optical member 14 appears as an image having a similar shape. The CPU 60 cuts out the image of each defect factor one by one from the rectangular coordinate data,
Each of them performs recognition processing of the type. At this time, in the first stage of the recognition process, the CPU 60 determines, as in the related art, the x-fillet and y-fillet of the defect factor at the time of pasting to the working memory area 62b, and the area of the defect factor in the image data. , The failure factor is recognized as a point defect or a linear defect. In the second stage of the recognition process executed when the defect factor is recognized as a linear defect, the CPU 60 sets the work memory area 62b
Then, the image of the defect factor is rotated once by one, and each time the defect factor is rotated once, the x fill and the y fillet of the defect factor are measured. And the failure factor is 90
The ratio of the minimum fillet to the maximum fillet (maximum fillet ratio) based on the maximum (maximum fillet) and minimum (minimum fillet) values of the x fillet value and the y fillet value measured during rotation by degrees. Is less than 50% and the minimum fillet is less than 15 dots, the defect factor is recognized as being flawed, otherwise, the defect factor is recognized as being fuzzy. This is because when the minimum fillet ratio is 15 dots or more, the flaw is too thick as a flaw, and when the maximum fillet ratio is 50% or more, the cause of the defect is considered to be a curved shape.

As described above, according to the present embodiment, it is possible to automatically recognize whether the defect factor is a flaw or a fluff by a rational method.

[0071]

[Embodiment 2] A second embodiment of the present invention is shown in FIG.
Only the maximum fillet and minimum fillet calculation processing subroutine executed at 105 is different from that of the first embodiment, and the other configuration is the same as that of the first embodiment.

According to the maximum fillet and minimum fillet calculation processing subroutine of FIG. 11 in the first embodiment described above, the rotation processing (S208 to S210) of the image of the cause of the defect in the work memory area 62b must be performed 90 times. , Variable Fmax and variable Fmin calculation processing (S2
03-S207) had to be performed 91 times. The maximum fillet and minimum fillet calculation processing subroutine of FIG. 12 in the second embodiment is based on the premise that the minimum fillet exists at an angular position near the angular position where the maximum fillet is found. Is rotated by 10 degrees to identify the angular position where the value of the x-fillet or y-fillet is the largest, and then the image of the defect factor is added once at a time from the angular position 9 degrees back from the specified angular position. Rotate x
At the time when the value of the fillet or y fillet becomes the largest, the value of the largest x fillet or y fillet is treated as the maximum fillet, and the smallest one of the values of each fillet that has been measured so far is regarded as the minimum fillet. deal with. Hereinafter, the specific contents of the maximum fillet and minimum fillet calculation processing subroutine executed in S105 of FIG. 10 in the second embodiment will be described with reference to FIG.

First S3 after entering the subroutine of FIG.
In 01, the CPU 60 initializes the variable Fmax to “0”
And initialize the variable Fmin to “1000”.

In the next step S302, the CPU 60 initializes a variable STEP indicating a rotation step angle and sets it to "10". In the next step S303, the CPU 60 initializes a variable KAKU indicating the rotation angle from the initial state of the image of the cause of the defect in the work memory area 62b and sets it to “0”.

Next, the CPU 60 sets the work memory area 62
b, the x-fillet and y-fillet are measured at each rotation position, and the variable Fmax
And a loop process from S304 to S322 for updating the variable Fmin. In the first step S304 after entering this loop processing, the CPU 60 resets a flag MAXflag indicating whether or not the variable Fmax has passed the maximum value to “0”. This flag MAXflag is reset to “0” when the variable Fmax has passed the maximum value, and is set to “1” when it cannot be determined that the variable Fmax has passed the maximum value.

In the next step S305, the CPU 60 rotates the image of the defect factor in the work memory area 62b so that the rotation angle from the initial state becomes KAKU (corresponding to image data rotating means).

In the next step S306, the x fillet of the defect factor pasted in the working memory area 62b at that time is measured and assigned to the variable FX, and the y fillet is measured and assigned to the variable FY (filler). Equivalent to measuring means).

In the next step S307, the CPU 60 sets the variable FX
Check whether the value of is smaller than the value of the variable Fmin. If the value of the variable FX is smaller than the value of the variable Fmin, the CPU 60 updates the variable Fmin by overwriting the value of the variable FX with the variable Fmin in S308, and then advances the process to S309. (Corresponds to fillet identification means). On the other hand, if the value of the variable FX is equal to or greater than the value of the variable Fmin, the CPU 60 proceeds to S
Proceed to 309.

At S309, the CPU 60 checks whether or not the value of the variable FY is smaller than the value of the variable Fmin. If the value of the variable FY is smaller than the value of the variable Fmin, the CPU 60 sets the value of the
After updating the variable Fmin by overwriting Fmin, the process proceeds to S311 (corresponding to the minimum fillet specifying means). On the other hand, if the value of the variable FY is equal to or larger than the value of the variable Fmin, the CPU 60 proceeds to S31
Proceed to 1.

In S311, the CPU 60 checks whether or not the value of the variable FX is larger than the value of the variable Fmax. If the value of the variable FX is larger than the value of the variable Fmax, the CPU 60 sets the value of the
After updating the variable Fmax by overwriting Fmax and resetting the flag FMAXflag to “1” in S313, the process proceeds to S314 (corresponding to the maximum fillet specifying means). On the other hand, if the value of the variable FX is equal to or less than the value of the variable Fmax, the CPU 60 proceeds to S3
Proceed to 14.

At S314, the CPU 60 checks whether or not the value of the variable FY is larger than the value of the variable Fmax. If the value of the variable FY is larger than the value of the variable Fmax, the CPU 60 sets the value of the
After updating this variable Fmax by overwriting Fmax and resetting the flag FMAXflag to "1" in S316, the process proceeds to S317 (corresponding to the maximum fillet specifying means). On the other hand, when the value of the variable FY is equal to or less than the value of the variable Fmax, the CPU 60 proceeds to S3.
Proceed to 17.

At S317, the CPU 60 sets the flag FMAX
Check whether flag is set to "1". If the flag FMAXflag is set to “1”, it is determined that the variable Fmax has not reached the maximum value yet, and the value of the variable KAKU is changed to the variable ST in S318.
After adding the value of EP, the process returns to S304. On the other hand, when FMAXflag is reset to “0”, the CPU 60 determines that the variable Fmax has reached the maximum value, and advances the processing to S319.

In S319, the CPU 60 subtracts “19” from the value of the variable KAKU. In the next S320, the CPU 6
A value of 0 checks whether the variable STEP has already been set to "1". Here, when the process of S320 is executed for the first time, since the variable STEP is still “10”, the CPU 60 substitutes “1” for the variable STEP in S321 and sets the variable Fmax in S322. After the initialization and setting to “0” and the initialization of the variable Fmin to “1000”, the process returns to S304. On the other hand, when S320 is executed again after the processing is returned to S304, the variable ST
Since EP is set to "1", the CPU 60 ends the maximum fillet and minimum fillet calculation processing subroutine, and returns the processing to the defect factor recognition routine of FIG.

The other configuration of the second embodiment is the same as that of the above-described first embodiment, and the description thereof will be omitted. <Operation of Embodiment> The operation of the second embodiment will be described with reference to FIG.
3 and FIG. Now, at 0 ° in FIG.
In the state shown in FIG.
Suppose that it was pasted on. Then, the CPU 60 updates the variable Fmax one after another by rotating the image of the defective factor by 10 °, and at the same time, updates the variable Fmin. In this way, while the variable Fmax is being updated, the flag FMAXflag
Continues to be set to "1" (S313, S31
6) The image of the cause of failure continues to be rotated by 10 ° (see FIG. 13).

As described above, if the image of the defective factor continues to be rotated, the update of the variable Fmin is stopped when the rotation angle from the initial state of the defective factor reaches 70 °, and the flag FMAXflag
Is reset to "0". As a result, it can be seen that the maximum fillet exists around a rotation angle immediately before that, that is, 60 °, that is, a rotation angle range of more than 50 ° and less than 70 °.

Then, the CPU 60 returns the rotation angle from the initial state of the image of the defective factor to 51 ° (S31).
9) The image of the cause of the failure is rotated by 1 °, and the variable Fmax is updated again one after another, and at the same time, the variable Fmin is updated. In this way, while the variable Fmax is being updated, the flag FMAXflag is kept set to "1" (S313, S316), so that the image of the cause of failure continues to be rotated by 1 ° (see FIG. 14).

As described above, if the image of the defective factor continues to be rotated, when the rotation angle from the initial state of the defective factor reaches 61 °, the updating of the variable Fmin stops, and the flag FMAXflag is reset to “0”. Will remain. As a result, it can be seen that the maximum fillet exists at 60 ° which is the rotation angle position immediately before that.

Therefore, the CPU 60 suspends the rotation process of the image of the defect factor and the calculation process of the variables FMAX and Fmin at that time, treats the value of the variable Fmax at that time as the maximum fillet, Treat the value of Fmin as the minimum fillet.

As described above, according to the second embodiment, the number of rotations (S305) of the image of the cause of the defect required until the maximum fillet is found is 29 times at the maximum.
Calculation of Variables Fmax and Fmin (S306 to S31
The number of times of 6) is a maximum of 30 times. Moreover, once the variable Fmax
When the maximum value of is found, the rotation in the same direction is interrupted.
The number of times for both the calculation process of the variable Fmax and the variable Fmin can be further reduced. For example, in the examples of FIGS. 13 and 14,
The number of rotations of the image of the defect factor is 18 and the variable Fm
The number of times of calculating the ax and the variable Fmin is 19 times.

The other operation of the second embodiment is as follows.
Since it is completely the same as that of the first embodiment, the description is omitted.

[0091]

[Embodiment 3] A third embodiment of the present invention is the same as that shown in FIG.
Only the maximum fillet and minimum fillet calculation processing subroutine executed at 105 is different from that of the first embodiment, and the other configuration is the same as that of the first embodiment. That is, in the calculation processing subroutine of the maximum fillet and the minimum fillet of FIG.
The maximum fillet and the minimum fillet were found by measuring the x fillet and the y fillet each time the image of the defect factor in 2b was rotated, but in the third embodiment,
The main axis vector of inertia of the image of the cause of failure is obtained, and the image of the main cause of inertia is once determined so that the main axis vector of inertia is oriented in the 0 ° direction parallel to the x axis or the 90 ° direction parallel to the y axis in the rectangular coordinate system. X and measured x after rotation
The larger of the fillet and the y-fillet is treated as the largest fillet, and the other is treated as the smallest fillet. Hereinafter, the specific contents of the maximum fillet and minimum fillet calculation processing subroutine executed in S105 of FIG. 10 in the third embodiment will be described with reference to FIG.

First S4 after entering the subroutine of FIG.
In step 01, the CPU 60 calculates the direction θ of the principal axis vector of the image of the defect factor in the working memory area 62b based on the following equation (4) (corresponding to the principal axis vector measuring means).

Θ = (tan ⁻¹ (2 · M ₁₁ / (M ₀₂ −M ₂₀ ))) / 2 (4) Here, the equation for _calculating the moment M _pq (M (p, q)) is as follows:

[0094]

(Equation 1)

Is as follows. However, in equation (5), f
_ij is a binary image that takes 1 in a figure and takes 0 outside the figure. In the next step S402, the CPU 60 obtains the rotation angle R of the image of the cause of the defect for turning the inertia main axis vector in the 0 ° direction.

In the next step S403, the CPU 60 rotates the image of the defective factor by the rotation angle R obtained in step S402 (corresponding to image data rotating means). In the next S404, the CPU 60 measures the x-fillet of the defect factor affixed to the working memory area 62b at that time,
Substitute Fcmax and measure the y fillet to determine the variable Fcm
Substitute in for (corresponds to fillet measuring means).

In the next step S405, the CPU 60 sets the variable Fc
The value of max is substituted for a variable Fmax (corresponding to the maximum fillet specifying means). In the next S406, the CPU 60 sets the variable Fcmin
Is substituted for the variable Fmin (corresponding to the minimum fillet specifying means).

After the above, the CPU 60 terminates this maximum fillet and minimum fillet calculation processing subroutine,
The process returns to the failure factor recognition routine of FIG. The other configuration of the third embodiment is the same as that of the above-described first embodiment, and the description thereof will be omitted. <Operation of Embodiment> According to the third embodiment, the rotation process of the image of the cause of the defect in the work memory area 62b (S40)
3) only needs to be performed once, and the variables Fmax and Fm
The number of in calculation processes (in S404 to S406) is also one.

The other operation of the third embodiment is as follows.
Since it is completely the same as that of the first embodiment, the description is omitted.

[0100]

[Fourth Embodiment] A fourth embodiment of the present invention will be described with reference to FIG.
Only the maximum fillet and minimum fillet calculation processing subroutine executed at 105 is different from that of the first embodiment, and the other configuration is the same as that of the first embodiment. That is, in the fourth embodiment, the principal axis of inertia of the image of the cause of failure is obtained, and the image of the cause of failure is projected in the direction of the principal axis vector of inertia and in the direction orthogonal thereto. Then, the width of the image projected in the direction orthogonal to the principal axis vector of inertia is treated as the maximum fillet, and the width of the image projected in the direction of the principal axis vector of inertia is treated as the minimum fillet. Hereinafter, the specific contents of the maximum fillet and minimum fillet calculation processing subroutine executed in S105 of FIG. 10 in the fourth embodiment will be described with reference to FIG.

First S5 after entering the subroutine of FIG.
In step 01, the CPU 60 calculates the direction θ of the principal axis vector of the image of the defect factor in the work memory area 62b based on the above equation (4) (corresponding to the principal axis vector measuring means).

In the next step S502, the CPU 60 projects the image of the cause of the defect in the direction of the principal axis vector of inertia. Next S
In 503, the CPU 60 obtains the width of the image projected by the projection processing in S502 (corresponding to a fillet measuring unit).

In the next step S504, the CPU 60 executes
The width of the image obtained in step 3 is substituted for the variable Fmin (corresponding to the minimum fillet specifying means). In the next S505, the CPU 60
Project the image of the failure factor on an axis orthogonal to the principal axis vector of inertia.

In the next S506, the CPU 60 sets
The width of the image projected by the projection processing in step 5 is obtained (corresponding to fillet measuring means). In the next S507, the CPU 60
Substitutes the width of the image obtained in S506 for a variable Fmax (corresponding to the maximum fillet specifying means).

After the above, the CPU 60 terminates this maximum fillet and minimum fillet calculation processing subroutine,
The process returns to the failure factor recognition routine of FIG. Other configurations of the fourth embodiment are the same as those of the above-described first embodiment, and thus description thereof will be omitted.

<Operation of Embodiment> According to the fourth embodiment, the rotation processing of the image of the cause of the defect in the work memory area 62b is unnecessary, and the calculation processing of the variables Fmax and Fmin (S501 to S507) Need only be performed once.

[0107] Other functions of the fourth embodiment are as follows.
Since it is completely the same as that of the first embodiment, the description is omitted.

[0108]

According to the optical member inspection apparatus, the image processing apparatus, and the computer-readable medium storing the image processing program of the present invention configured as described above, the linear member detected in the optical member to be inspected can be used. It is possible to automatically and rationally determine whether the defect factor is a flaw or a fluff.

[Brief description of the drawings]

FIG. 1 is a side sectional view showing a schematic configuration of an optical member inspection apparatus according to a first embodiment of the present invention.

FIG. 2 is a plan view of the inspection target optical member and the like in FIG. 1 viewed from a position of an imaging device.

FIG. 3 is a block diagram showing an internal circuit configuration of the control device of FIG. 1;

FIG. 4 is a diagram showing the progress of light when there is no defect factor in the optical member to be inspected.

FIG. 5 is a diagram showing the progress of light when there is a defect factor in the optical member to be inspected.

FIG. 6 is an explanatory diagram of a relationship between an image of a defect factor and x fillets and y fillets.

FIG. 7 is a graph showing a threshold function used for recognizing whether a defect factor is a point defect or a linear defect.

FIG. 8 is a graph showing a threshold value indicating whether a linear defect factor is a flaw or a fluff.

FIG. 9 is a flowchart showing control processing executed by the CPU of FIG. 3;

FIG. 10 is a flowchart showing a failure factor recognition processing subroutine executed in S007 of FIG. 9;

11 is a flowchart showing a maximum fillet and minimum fillet calculation processing subroutine executed in S105 of FIG. 10;

FIG. 12 shows a second embodiment of the present invention;
Flowchart showing a maximum fillet and minimum fillet calculation processing subroutine executed in 105

FIG. 13 is an explanatory diagram of an operation according to the second embodiment of the present invention.

FIG. 14 is an explanatory diagram of an operation according to the second embodiment of the present invention.

FIG. 15 is a block diagram of a third embodiment of the present invention;
Flowchart showing a maximum fillet and minimum fillet calculation processing subroutine executed in 105

FIG. 16 is a block diagram of a fourth embodiment of the present invention;
Flowchart showing a maximum fillet and minimum fillet calculation processing subroutine executed in 105

[Explanation of symbols]

 Reference Signs List 3 imaging device 6 control device 8 drive motor 14 optical member to be inspected 60 CPU 62 host memory 62a image memory area 62b working memory area 62c image processing program storage area

Claims (8)

[Claims] 1. An image pickup device for picking up an optical member to be inspected and outputting image data including an image of the optical member to be inspected, an image memory for storing image data output from the image pickup device, and an image memory Area specifying means for specifying an area indicating a cause of a defect of the optical member to be inspected from image data stored in the apparatus, and fillet measuring means for measuring a maximum fillet and a minimum fillet of the area specified by the area specifying means, respectively. A ratio comparing means for comparing the ratio of the maximum fillet to the minimum fillet measured by the fillet measuring means with a predetermined threshold value; and an area specified by the area specifying means based on a comparison result by the ratio comparing means. A defect factor type recognizing means for recognizing whether the inspection target optical member indicates any of a flaw and a fluff. An optical member inspection apparatus. 2. The defect factor type recognizing means, when the comparison result by the ratio comparing means indicates that the ratio of the minimum fillet to the maximum fillet is larger than the predetermined threshold value, the area specifying means. The optical member inspection apparatus according to claim 1, wherein the specified region is recognized as indicating a fluff of the optical member to be inspected. 3. The apparatus according to claim 1, further comprising: a minimum fillet comparing means for comparing a minimum fillet measured by said fillet measuring means with a predetermined threshold value. When the comparison result by the ratio comparing means indicates that the minimum fillet is smaller than the predetermined threshold, the area is limited only when the comparison result by the minimum fillet comparing means indicates that the minimum fillet is smaller than the predetermined threshold. Recognizing that the region specified by the specifying unit indicates a flaw of the optical member to be inspected, and otherwise recognizes that the region specified by the region specifying unit indicates a fluff of the optical member to be inspected. The optical member inspection apparatus according to claim 2, wherein 4. A work memory for holding image data in accordance with rectangular coordinates, and an image data cutout means for cutting out an area specified by the area specifying means from image data stored in the image memory and pasting the image data in the work memory. Wherein the fillet measuring means rotates image data pasted to the working memory, and the image data rotating means rotates the image data pasted to the working memory. While doing so, fillet measuring means for measuring the fillet of the area in each axis direction of the orthogonal coordinates, and maximum fillet specifying means for specifying the largest fillet measured by the fillet measuring means as the maximum fillet. , A minimum fillet that specifies a minimum fillet measured by the fillet measuring means as the minimum fillet. 2. The optical member inspection apparatus according to claim 1, further comprising a specifying unit. 5. A work memory for holding image data in accordance with rectangular coordinates, and an image data cutout means for cutting out an area specified by the area specifying means from image data stored in the image memory and pasting the image data in the work memory. The fillet measuring means further comprises: an inertial principal axis vector measuring means for measuring an inertial principal axis vector of the image data attached to the working memory; and the inertial principal axis vector is one of the orthogonal coordinates. Image data rotating means for rotating the image data attached to the working memory so as to face the direction, after rotation by the image data rotating means,
Fillet measuring means for measuring the fillet of the area in each axis direction of the rectangular coordinates, maximum fillet specifying means for specifying a larger one of the fillets measured by the fillet measuring means as the maximum fillet, 2. The optical member inspection device according to claim 1, further comprising a minimum fillet specifying unit that specifies a smaller one of the fillets measured by the measuring unit as the minimum fillet. 6. A work memory for holding image data in accordance with rectangular coordinates, and an image data cutout means for cutting out an area specified by the area specifying means from image data stored in the image memory and pasting the image data in the work memory. The fillet measuring means, the inertial principal axis vector measuring means for measuring the inertial principal axis vector of the image data pasted in the working memory, in a direction parallel to and orthogonal to the inertial principal axis vector Fillet measuring means for measuring the fillet in the area, maximum fillet specifying means for specifying a fillet in a direction parallel to the inertial principal axis vector measured by the fillet measuring means as the maximum fillet, and measurement by the fillet measuring means. The fillet in a direction orthogonal to the calculated principal axis vector of inertia. 2. The optical member inspection apparatus according to claim 1, further comprising a minimum fillet specifying unit that specifies a small fillet. 7. An image memory for storing image data obtained by imaging an optical member to be inspected, and an area indicating a cause of failure of the optical member to be inspected is specified from the image data stored in the image memory. An area specifying means, a fillet measuring means for measuring a maximum fillet and a minimum fillet of an area specified by the area specifying means, respectively, a ratio of the maximum fillet and the minimum fillet measured by the fillet measuring means is determined by a predetermined threshold value. A ratio comparing means for comparing with the ratio comparing means, and a failure factor for recognizing whether the area specified by the area specifying means indicates any of a flaw and a bulge of the inspection target optical member. An image processing apparatus comprising: a type recognition unit. 8. A computer having an image memory stores image data obtained by imaging the optical member to be inspected in the image memory, and performs the inspection from the image data stored in the image memory. The area indicating the cause of the failure of the target optical member is specified, the maximum fillet and the minimum fillet of the specified area are measured, and the ratio of the measured maximum fillet to the minimum fillet is compared with a predetermined threshold value. And a computer-readable medium storing a program for recognizing whether the specified area indicates a flaw or a fluff of the optical member to be inspected.