JP2002286411A - Method and device for analyzing fringe - Google Patents

Abstract

PROBLEM TO BE SOLVED: To make masking work uniquely and stably performable and make stable analysis result obtainable by applying an edge detection processing for detecting any edge in a fringe image area to an entire image area including the fringe image area and an emphasizing processing for emphasizing the edge, and further a diffusion processing for averaging the concentration value in the fringe image area, so as to determine the masking range. SOLUTION: The edge detection processing using a differential computation is applied to an entire image area, and at the same time, an emphasizing processing using a differential computation is applied thereto. Next, a median filter is used for diffusion processing, thus reducing the variation of concentration value of image within a fringe image area and specifying the fringe image area. Furthermore, the contour of the specified fringe image area is traced and the traced contour is smoothened, and the obtained entire image area is binarized to determine the shape of the fringe image area.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a fringe analyzing method and apparatus for analyzing fringe images, and more particularly to masking a fringe image area having a fringe pattern such as an interference fringe carrying wavefront information of an object to be observed. And a fringe analysis method and apparatus for extracting from a whole image area for analysis.

[0002]

2. Description of the Related Art Conventionally, it has been widely used to analyze fringe images such as interference fringes and moiré fringes to obtain information such as the shape of an object to be observed. With the improvement, in the field of light wave interferometry in precision measurement of the surface of an object, interferometry that reads information of 1 interference fringe (1 fringe) or less due to the necessity of measuring surface accuracy of 1/10 wavelength or more and wavefront aberration The method (sub-fringe interferometry) has been developed.

As such a subfringe interference measuring method, for example, “Optics”, Vol. 13, No. 1 (1984, 2)
Month) Various techniques are known as described in “Basic theory of sub-fringe interference measurement” on pages 55 to 65.

[0004]

In the above-described fringe analysis method such as the sub-fringe interference measurement method, a fringe carrying information of an object to be observed is located in an entire image area captured from an image input device such as a CCD camera. It is necessary to extract and analyze only the image area, and therefore, it is necessary to mask the entire image area to remove unnecessary areas.However, the task of determining the masking area has not been automated until now. Instead, the operator manually performs the operation while viewing the image.

For this reason, not only is an operator with sufficient knowledge of the fringe analysis method necessary, but also disadvantages such as a difference in analysis result depending on the operator occur. Further, there is a problem that it is difficult to increase the efficiency of the analysis work because the operation of the operator takes time.

As described above, the masking operation in the fringe analysis is left to the manual operation of the operator, and the background is that the density value of the image repeatedly and largely changes sinusoidally in the fringe image area. There is a situation in which it is not possible to discriminate between the edge of the stripe image region and the light and dark boundary of the stripe pattern only by applying the normal image preprocessing.

The present invention has been made in view of the above circumstances, and enables a unique and stable masking operation to be performed under an image condition peculiar to fringe analysis, whereby a stable analysis result can be obtained. It is an object of the present invention to provide a simple fringe analysis method and apparatus. In particular,
An object of the present invention is to provide a fringe analysis method capable of automating a masking operation and improving the efficiency of an analysis operation, and an apparatus suitable for unmanned production and inspection using the method.

[0008]

SUMMARY OF THE INVENTION In order to solve the above-mentioned problems, a fringe analyzing method and apparatus according to the present invention perform edge detection processing and edge enhancement processing of a fringe image area with respect to an entire image area, and originally analysis. The masking range for extracting the stripe image region is determined by performing a diffusion process for blurring the necessary stripe image region as needed.

That is, according to the fringe analyzing method of the present invention, the fringe image region is extracted by masking in the entire image region including the fringe image region carrying the wavefront information of the object to be observed, and the analysis is performed based on the extraction result. In the fringe analysis method for performing the wavefront information, the entire image region is subjected to an edge detection process of detecting an edge of the stripe image region in the entire image region, and an enhancement process of enhancing the edge, Further, the entire image area is subjected to diffusion processing for averaging density values in the stripe image area in a range where the edge can be recognized, thereby specifying the stripe image area and determining the masking range. This is a feature of the present invention.

The whole image area may refer to the entire area of the captured image in some cases. However, the entire image area may be an area including the stripe image area, and does not necessarily indicate the entire image area. .

The range in which the edge can be recognized is
It indicates the range in which the stripe image area can be specified in the processing after the diffusion processing. Further, the emphasis processing and the diffusion processing may be performed a plurality of times as needed.

In the fringe analyzing method according to the present invention, the contour of the specified fringe image area is extracted, and a binarization process is performed on the inside and outside of the extracted contour line to thereby obtain the shape of the fringe image area. May be determined.

[0013] The edge detection processing and the emphasis processing may be processing performed using differential or difference calculation processing. Further, the diffusion process can be performed using a median filter. In the fringe analysis method of the present invention, a smoothing process may be performed on the outline to obtain a smoothed mask outline.

The fringe analyzer of the present invention extracts the fringe image area by masking within the entire image area including the fringe image area carrying the wavefront information of the object to be observed, and performs analysis based on the extraction result. In the fringe analyzer for obtaining wavefront information, edge detection enhancement for performing, on the entire image area, edge detection processing for detecting an edge of the stripe image area in the entire image area and enhancement processing for enhancing the edge Means, diffusion processing means for performing diffusion processing for blurring the stripe image area within the range in which the edge is preserved, for the entire image area processed by the edge detection enhancement means, and the edge detection enhancement means; A masking range for specifying the stripe image region in the entire image processed by the diffusion processing unit and determining the masking range. And it is characterized in by comprising a determination unit.

[0015]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a Fourier transform fringe analysis method according to an embodiment of the present invention will be described with reference to the drawings.

The method according to the present embodiment uses the Fourier transform method for the interference fringe image data obtained on the basis of the relative shape between the surface of the object and the reference surface, using the Fourier transform method. When analyzing the shape, the surface of the object to be observed and the reference surface are relatively inclined, and carrier fringes (included in the concept of carrier frequency; the same applies hereinafter) generated with this inclination are included in the shape information of the object to be observed. This is a fringe analysis method for obtaining the fringe image data by superimposing the fringe image data accompanying the fringe. Then, when masking is performed on the entire image region including the stripe image region to extract a stripe image region necessary for analysis,
The entire image area is subjected to an edge detection process for detecting an edge of the stripe image area in the entire image area, and an enhancement processing for enhancing the edge. The stripe image area is specified by performing a diffusion process for averaging the grayscale values in the image area. Further, the contour of the specified stripe image area is extracted, and the binarization process is performed inside and outside the extracted contour line, thereby determining the shape of the stripe image area and determining the masking range. is there.

Further, the edge detection processing and the emphasis processing are performed using a difference calculation processing, and the diffusion processing is performed using a median filter.

In the method of the present embodiment, first, an interference fringe image (an example is shown in FIG. 1) carrying shape information as one of the wavefront information of the observed object on which the spatial carrier fringe is superimposed is imaged by CCD. Obtain by camera. An image captured as shown in FIG. 1 includes a circular stripe image region and a background image region excluding the stripe image region, and the two image regions form an entire image region (including the stripe image region). (The entire area of the captured image; wider than the concept of the entire image area).

The stripe image area forms a stripe pattern in which the gray value changes sinusoidally, and the density value of the dark part is close to the density value of the dark part of the background image area. It seems that both ends of the linearly extending dark portion and the boundary portion with the background image area are connected. Even if the density values are digitized, the two cannot be distinguished because the density values are close.
Heretofore, this has been a background in which the stripe image area has to be specified manually by the operator. If the stripe image area is a simple figure as shown in FIG. 1, an experienced operator can uniquely identify the area to some extent, but if the shape of the stripe image area is complicated, the same image Even if the same operator performs an operation, the range of the specified area may be different.

Therefore, in the method of this embodiment, for example, the edge detection processing of the stripe image area using a non-linear filter is performed on all the image areas using a difference calculation (see FIG. 2), and the difference calculation is used. For example, an enhancement process using a Laplacian filter is performed (see FIG. 3). Further, the emphasis processing is performed again on the entire image area (see FIG. 4).

Since the emphasizing process is also performed on the fringe image area, the density difference of the image in the fringe image area is also emphasized.
The density change in the stripe image area is more gradual than the density change at the edge of the stripe image area. For this reason, when the emphasis process is performed, the density change in the edge portion of the stripe image region is emphasized and the edge stands out, while the density change in the stripe image region is relatively small, and the stripe pattern in the stripe image region is conspicuous. Disappears.

Next, a diffusion process using a median filter is performed on the entire image area. At this time, a median filter is set so that the edge of the stripe image area can be recognized even after the diffusion processing. In this way, the width of the density value of the image in the stripe image area is reduced, and the image in the stripe image area is blurred to specify the stripe image area (see FIG. 5).

Further, the contour of the specified fringe image area is traced by using, for example, a contour tracing algorithm (see FIG. 6), and a process of smoothing the traced contour is performed (see FIG. 7). Then, the obtained entire image area is subjected to, for example, a binarization process by the mode method to determine the shape of the striped image area. Next, a mask is created according to the determined shape of the stripe image area. And
Masking is performed on all of the first captured image areas to extract a striped image area.

Since a series of processes for performing the above-described masking process data in a fringe image area required for analysis, it is inherently incompatible with the following analysis itself. However, due to the improvement in the performance of information processing equipment such as computers, the time required for the above-described processing is about several seconds when using an apparatus described later, and as a result, the total time required for analysis is significantly reduced. Can also be achieved. Further, by performing a series of processes in a software manner, a unique and stable area can be specified even when the stripe image area is complicated. In other words, it is effective for a stripe image area of an arbitrary shape, and these points are greatly superior to those in the case of a conventional manual operation by an operator.

An embodiment of an analysis method for the data of the extracted stripe image area will be described below. Fourier transform is performed on the extracted data of the striped image area to obtain a Fourier spectrum in a frequency coordinate system (also referred to as a Fourier spectrum coordinate system or a spatial frequency domain; an example is shown in FIG. 8), and then the origin is obtained. Find the spectral peak at. Locate the spectrum amplitude than spectrum in the origin becomes the maximum (spectrum C (η-f x corresponding to the spatial carrier _frequency, the peak of the ζ-f _y)), to obtain the coordinates (fx, fy). Note that, at this time, as shown in FIG.
Although two peaks are detected, since both have a conjugate relationship with each other, one of the peaks may be selected.

Furthermore, coordinates (fx, fy) is the Fourier spectrum _{C (η-f x, ζ} -f y) to come to the origin of coordinate movement, to remove the spatial carrier frequency. next,
The inverse Fourier transform is performed to obtain the complex amplitude c (x, y) of the interference fringes shown in Expressions (2) and (3) to be described later to obtain the wrapped phase φ (x, y) of the observed object. Further, by performing an unwrapping process as described later, a continuous phase (Φ (x, y): phase distribution) corresponding to the surface shape of the observation target is obtained to obtain the surface shape of the observation target (an example is shown below). FIG. 9).

[0027] Incidentally, the interference fringe image data obtained in the fringe image shown in FIG. 1, x-direction component of the spatial carrier frequency, respectively f _x and y-direction _components, the following equation when the f _y (1)
Is represented by

[0028]

(Equation 1)

When the above equation (1) is modified, the following equation (2) is obtained.
Is obtained.

[0030]

(Equation 2)

Note that c (x, y) is represented by the following equation (3).

[0032]

(Equation 3)

When the above equation (3) is Fourier-transformed, the following equation (4) is obtained.

[0034]

(Equation 4)

Then, the above equation (4) is obtained by filtering.
Only the second term component of
_(f x, _{f y)} spatial carrier frequency based on a peak of the spectrum is located in _(f x, _{f y)} to extract.

On the other hand, C (η-f
_{x, ζ-f y)} expand on the frequency coordinates, the coordinates _(f x,
By moving the peak of the spectrum located at f _y ) to the origin on the frequency coordinate system, the spatial carrier frequency is removed, and then the inverse Fourier transform is performed to obtain c.
(x, y) is obtained, and the wrapped phase is obtained by obtaining the imaginary part of the following equation (5).

[0037]

(Equation 5)

Since the phase distribution obtained in this way is folded discontinuously between the principal values of -π to π, they are unwrapped by using a phase unwrapping algorithm, so that A continuous phase (Φ (x, y): phase distribution) corresponding to the observation object surface shape can be obtained.

Next, an embodiment of the present invention will be described with reference to FIGS.

This apparatus is for implementing the method of the above embodiment. As shown in FIG. 10, in a Michelson interferometer 1, both reflections from a surface 2 of an object to be observed and a reference (reference) surface 3 are obtained. The interference fringe formed by the light beam is formed on the imaging surface of the CCD 5 of the imaging camera 4, is input to the computer 7 equipped with the CPU and the memory for image processing via the image input board 6, and is input. Various arithmetic processing is performed on the image data, and the processing results are displayed on the monitor screen 7A. Note that the imaging camera 4
Is temporarily stored in a memory by the processing of the CPU.

The computer 7 includes, as software, an inclination adjusting means (not shown) for adjusting the inclination of the reference surface. The inclination adjusting means is provided with a piezo driving section 9 via a D / A conversion board 8. To the PZT (piezo element) actuator to send a drive signal.
Thereby, the PZT (piezo element) actuator 10
Is displaced by a predetermined amount, and the reference surface 3 held by the PZT (piezo element) actuator is adjusted to be inclined by a predetermined amount. The inclination of the reference surface 3 forms a relative inclination between the surface 2 of the object to be observed and the reference surface 3. Due to the relative inclination, an interference fringe formed on the imaging surface of the CCD 5 of the imaging camera 4 is generated. The spatial carrier frequency is superimposed.

Further, as shown in FIG. 11, the computer 7 includes an edge detection / enhancement means 11, a diffusion processing means 12, and a masking range determination means 13 in software. As described above, the edge detection enhancement means 11 performs, for example, edge detection processing of the fringe image area using a non-linear filter on the entire image area of the obtained interference fringe image using difference calculation processing, and performs difference calculation. , For example, using a Laplacian filter.

Further, as described above, the diffusion processing means 12 performs the diffusion processing by the median filter on the entire image area to reduce the fluctuation of the density value of the image in the stripe image area, and Is specified. further,
As described above, the masking range determining unit 13 traces the contour of the specified fringe image region using, for example, a contour tracking algorithm, and performs a process of smoothing the traced contour. The binarization process is performed on all the obtained image regions, for example, by the mode method to determine the shape of the stripe image region.

As described above, in the apparatus according to the present embodiment, the masking range is automatically determined by software, so that it is possible to obtain a unique and stable analysis result that does not depend on the operator, and to automate the masking. The analysis can be automated.

The fringe analysis method and apparatus of the present invention
The present invention is not limited to the above embodiment, and other various modes can be changed. For example, in the above-described embodiment, digital processing is premised, and a method using difference calculation processing is used for edge detection and enhancement processing. However, in the case of analog processing, these methods are based on differential calculation processing. Be replaced.

As a method for detecting an edge, an edge detection by a zero-crossing method, a template-type edge detection operator, a method using a range filter, or the like can be used. Further, as an enhancement process, an image is Fourier-transformed. And emphasize high frequency components in the frequency domain,
A method of returning to the spatial domain by the inverse Fourier transform may be used.

For the diffusion processing, an edge preserving filter or a linear filter such as a moving average filter or a weighted average filter may be used as long as the edge can be recognized. Further, as the binarization processing, various methods such as a P-tile method, a differential histogram method, a discriminant analysis method, and a variable threshold method can be used.

In addition, various aspects can be changed in a portion relating to analysis performed on a stripe image region specified by masking. For example, as a mechanism for generating the carrier frequency, if the relative inclination between the wavefront from the object to be observed and the wavefront from the reference surface can be adjusted with high accuracy, the above-described mechanism between the object to be observed and the reference surface can be used. It is not limited to the one that adjusts the relative tilt.For example, a wavefront from the object to be observed or a wavefront from the reference surface is inserted by inserting a predetermined light modulation element or a wedge-shaped optical system into at least one optical path. May be adjustable. Also, after the generation of a predetermined carrier frequency, the carrier frequency may be increased or decreased by changing the wavelength difference between the two light beams.

In the apparatus of the above embodiment, the reference surface is tilted by the PZT actuator. Alternatively, the object to be observed may be tilted.
Further, the tilt amount adjusting means for tilting the reference surface and / or the observation target may be any device that can accurately tilt the reference surface and / or the observation target, and is not necessarily limited to the PZT actuator.

Further, in the above-described embodiment, the description has been made using the spatial carrier frequency as the carrier frequency. However, a time carrier frequency or a spatio-temporal carrier frequency can be used as the carrier frequency.

In the above embodiment, the interference fringe image data is imaged using a Michelson interferometer, but the interference fringe image data obtained using another Fizeau-type interferometer is used. Of course, the same can be applied to Further, the present invention can be similarly applied to not only interference fringes but also moire fringes, speckle fringes, and other various fringe images.

[0052]

According to the fringe analyzing method and apparatus of the present invention, an edge detecting process for detecting an edge of a fringe image area in the entire image area for an entire image area including the fringe image area is performed. Perform emphasis processing to emphasize,
Furthermore, by performing a diffusion process for averaging the density values in the stripe image region within a range in which the edge can be recognized with respect to the entire image region, the stripe image region is specified and the masking range is determined. Can be uniquely and stably performed, whereby a stable analysis result can be obtained. In particular, it is also possible to automate the masking operation and increase the analysis efficiency.

[Brief description of the drawings]

FIG. 1 is a schematic diagram illustrating an example of interference fringe image data of an object to be observed.

FIG. 2 is a schematic diagram showing an example in which edge detection processing has been performed on the image data of FIG. 1;

FIG. 3 is a schematic diagram illustrating an example in which the image data of FIG. 2 is subjected to an enhancement process;

FIG. 4 is a schematic diagram showing an example in which the image data of FIG.

FIG. 5 is a schematic diagram showing an example of performing diffusion processing on the image data of FIG. 4;

FIG. 6 is a schematic diagram illustrating an example of tracing a contour in a stripe image area in the image data of FIG. 5;

FIG. 7 is a schematic diagram illustrating an example in which the outline of the image data in FIG. 6 is smoothed;

FIG. 8 is a schematic diagram illustrating an example of a spectrum peak in a frequency coordinate system.

FIG. 9 is a schematic diagram illustrating an example of a result of analyzing a shape of a surface of an object to be observed.

FIG. 10 is a block diagram illustrating an apparatus according to an embodiment of the present invention.

FIG. 11 is a block diagram for explaining a part of FIG. 9 in detail;

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Michelson-type interferometer 2 Observed object surface 3 Reference surface 4 Imaging camera 5 CCD 7 Computer 7A Monitor screen 9 Piezo drive unit 10 PZT actuator 11 Edge detection enhancement unit 12 Diffusion processing unit 13 Masking range determination unit

 ──────────────────────────────────────────────────続 き Continued on the front page F term (reference) 2F064 AA11 BB03 EE01 GG12 GG22 GG70 HH03 HH08 JJ01 JJ15 2G086 HH06

Claims (6)

[Claims] 1. A fringe analysis for extracting said fringe image area by masking in an entire image area including a fringe image area carrying wavefront information of an object to be observed, and performing analysis based on the extraction result to obtain said wavefront information. In the method, for the entire image area, edge detection processing for detecting an edge of the stripe image area in the entire image area;
The emphasis processing for emphasizing the edge is performed. Further, the entire image area is subjected to a diffusion processing for averaging density values in the fringe image area in a range where the edge can be recognized, so that the fringe image area is And determining a range of the masking. 2. The shape of the striped image area is determined by extracting a contour of the specified striped image area and performing a binarization process on the inside and outside of the extracted contour line. The fringe analysis method according to claim 1. 3. The edge detection process and the enhancement process
3. The fringe analyzing method according to claim 1, wherein the fringe analyzing method is performed by using a differential or difference calculation process. 4. The fringe analysis method according to claim 3, wherein said diffusion processing is performed using a median filter. 5. The fringe analysis method according to claim 3, wherein a smoothing process is performed on the outline to obtain a smoothed mask outline. 6. A fringe analysis for extracting said fringe image area by masking within an entire image area including a fringe image area carrying wavefront information of an object to be observed, and performing analysis based on the extraction result to obtain said wavefront information. In the apparatus, for the entire image area, edge detection processing for detecting an edge of the stripe image area in the entire image area,
Edge detection and enhancement means for performing enhancement processing for enhancing the edge; and, for the entire image area processed by the edge detection and enhancement means, average density values in the stripe image area within a range in which the edge can be recognized. Diffusion processing means for performing a diffusion processing for converting, and a masking range determination means for determining the masking range by specifying the stripe image area in the entire image processed by the edge detection enhancement means and the diffusion processing means; A fringe analyzing apparatus comprising: