Image analysis – Pattern recognition – Template matching
Reexamination Certificate
1998-04-06
2001-09-25
Boudreau, Leo (Department: 2721)
Image analysis
Pattern recognition
Template matching
C382S149000, C382S209000, C348S095000, C358S504000
Reexamination Certificate
active
06295374
ABSTRACT:
TECHNICAL FIELD
This invention relates to methods and systems for detecting a flaw in a sample image and, in particular, to interactive methods and systems for detecting a flaw in a sample image.
BACKGROUND ART
Relative geometric distortion is typically caused by differential stretching and twisting of screens of a screen printer being used to print an image. For purposes of this application, a flaw consists of an area of the image consisting of the wrong grey level, which cannot be accounted for by means of the relative geometrical distortion. A much exaggerated demonstration of such a distortion is shown in the image of FIG.
1
.
Referring now to
FIG. 2
, the type of flaws that need to be detected include a pigment splash
10
, a pigment bridge
12
, a pinhole
13
, a pigment overrun
14
, and a missing pigment.
Techniques which are able to detect flaws such as those described above have been known for a considerable period of time. However, these techniques, such as those described in U.S. Pat. No. 5,640,200 entitled “Golden Template Comparison Using Efficient Image Registration” are only capable of working where there is an effectively constant geometric relationship existing between the template image and the sample image.
Referring now to
FIG. 3
, one technique of the above-noted patent initially creates a composite template image by statistically combining multiple instances of a “perfect” image. The resulting template consists of a mean image, together with a standard deviation image (i.e. &sgr; image). Naturally, the areas of most “rapid” rate of change within the template components (i.e. edges and regions of color change) provide most of the contribution to the standard deviation image, which can be used to form a mask of regions which, even on a good sample, are subject to the greatest variation, and, on hunting for flaws, can be given much less weight than areas that are subject to little change.
Once the template image has been fully trained, sample images taken from the production line can be compared with it. However, there is very little chance of the template and sample image exactly overlaying. In order to remove this misalignment some form of registration must be undertaken. This process invariably uses some form of correlation between either known landmarks or fiducial marks on both template and sample images, else using the complete image itself. Done properly, this process can provide a sub-pixel accurate measurement of the relative positioning of the template and the sample. This information can be used to either digitally re-register the images, or else can be used to re-position the sample image relative to the camera to ensure that the two images are optimally aligned. Flaws between the template image and the sample image can then be detected by the simple process of undertaking an absolute subtraction of the two. Thus, any difference, irrespective of whether the greyscale is brighter or darker than the other, results in a positive difference.
The value of this difference can either be thresholded, or else compared to the standard deviation image. Comparing with the &sgr; image allows one to determine if the difference in that region of the image is greater than that expected from the distribution derived from the template samples. If so, the pixel is marked as a flaw, or else it is considered as part of the natural statistical variation between acceptable samples. The resulting flaw flag can be directly used to provide an error image, which, after further analysis using morphological tools such as blob analysis, high-lights the position of any artifacts.
The system of the above-noted patent works well in many applications, and is used extensively within the machine vision industry. However, there are some very important applications where the assumption of optimum alignment between the template image and the sample image being satisfied by a single correlation process is not valid.
For example, there are many cases within the screen printing industry, where the printing screen can become warped during use and thus change the relative alignment between the template and sample images across the image. In the case where this alignment drift can itself be categorized as a flaw, the previously described methodology can still be used. However, in the case where the results of the geometric warp are considered unimportant, the flaws maintain their criticality. In this situation, the current prior art systems produce far too many rejects, which often increase as the production batches progress.
The U.S. Pat. No. 5,412,577 to Sainio et al. entitled “Color Registration System for a Printing Press” discloses a control system for controlling misregistration between the colors of an image printed on a web. The system includes an imaging device such as a camera, a processor and image conversion circuits coupled to the processor. In the preferred embodiment, four printing units each print one color of the image upon the web. This kind of printing is commonly referred to as web offset printing. Colors on the units are cyan (C), magenta (M), yellow (Y) and black (K) The system further includes a print controller, a computer, a camera positioning unit, a camera assembly, and a printing plate scanner. The controller converts the signals from the computer into signals having suitable quality. The assembly is configured to scan a web for red (R), green (G), blue (B) and infrared (I). The U.S. Pat. No. 4,887,530 also to Sainio describes a web registration control system.
SUMMARY OF THE INVENTION
An object of the present invention is to provide a method and system for detecting a flaw in a sample image of an object which cope with geometric warpage or distortion while at the same time maintain their sensitivity to the flaw.
In carrying out the above object and other objects of the present invention, a method is provided for detecting a flaw in a sample image of an object in a machine vision system including a camera and a computer. The method includes the steps of: a) creating a template image of the object; b) registering the template image and the sample image; and c) subtracting the template image from the sample image to provide an error image after performing step b). The method also includes the steps of: d) analyzing the error image to identify a region of error; e) creating a final image of both the template image and the sample image in the region of error; and f) repeating steps b) through d) using the final sub-images to obtain a final region of error. The method finally includes the step of: g) identifying the final region of error as the flaw.
The flaw is typically an area of the sample image which has an incorrect greyscale level.
The above object and other objects, features, and advantages of the present invention are readily apparent from the following detailed description of the best mode for carrying out the invention when taken in connection with the accompanying drawings.
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Bland Anthony R.
Robinson David A.
Boudreau Leo
Brooks & Kushman P.C.
Integral Vision, Inc.
Sherali Ishrat
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