Method and apparatus for the detection of anomalies in two-dimensional digital images of products
AssigneeWipotec GmbH
InventorsMichael Siegrist, Manuel Bastuck
Life of this patent
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Abstract
A method for detecting anomalies in digital images of products, wherein a region of a digital image is detected as a maximum anomaly if the value of a property of the region is greater than a predetermined maximum threshold, and/or wherein a region is detected as a minimum anomaly if the value of the property of the region is less than a predetermined minimum threshold. The maximum threshold value and/or the minimum threshold value are determined in a learning process using relatively few digital images based on a statistical distribution of the largest or smallest values of a specific quantity used for the detection of anomalies in digital images to be examined.
Claims32
A method of detecting anomalies in digital images of products, each digital image being formed by a plurality of pixels, each pixel representing an associated location within the respective digital image and having a value characterising the associated location, each digital image defining one or more regions with each region including one pixel or multiple adjacent pixels, and wherein for each region a region value is determined, the region value comprising a value for at least one property of the respective region or a combined value for multiple properties of the respective region, the method comprising:
- (a) one or both of (i) detecting a respective region defined by a respective one of the digital images as a maximum anomaly when the region value of the respective region is greater than a predetermined maximum threshold value or a secondary maximum threshold value derived from the predetermined maximum threshold value and (ii) detecting the respective region defined by a respective one of the digital images as a minimum anomaly when the region value is less than a predetermined minimum threshold value or a secondary minimum threshold value derived from the predetermined minimum threshold value;
- and
- (b) wherein the predetermined maximum threshold value and the predetermined minimum threshold value are determined by,
- (i) for each of a number of learning digital images of good products or of good process products, specifying the one or more regions and determining the respective region value of each region and determining the maximum value of these respective region values as the maximum sample value of a maximum value sample or determining the minimum value of these respective region values as the minimum sample value of a minimum value sample;
- and
- (ii) using a statistical estimation method, determining estimates for all free, non-specified parameters of a probability density function specified to describe the maximum value sample using the maximum sample values or determining estimates for all free, non-specified parameters of a probability density function specified to describe the minimum value sample using the minimum sample values;
- and
- (iii) (I) setting a first rate at which a maximum anomaly is incorrectly detected in images to be examined or a second rate at which a maximum anomaly is correctly not detected in images to be examined, or (II) setting a third rate at which a minimum anomaly is incorrectly detected in images to be examined or a fourth rate at which a minimum anomaly is correctly not detected in an image to be examined;
- and
- one or both of,
- (A) determining the maximum threshold value using the probability density function parameterised according to (b) (ii) or a distribution function corresponding thereto such that the probability of occurrence of a maximum value greater than or equal to the maximum threshold value corresponds to the first rate or such that the probability of occurrence of a maximum value less than or equal to the maximum threshold value corresponds to the second rate, and
- (B) determining the minimum threshold value using the probability density function parameterised in accordance with (b)(ii) or a distribution function corresponding thereto such that the probability of occurrence of a minimum value less than or equal to the minimum threshold value is equal to the third rate or such that the probability of occurrence of a minimum value greater than or equal to the minimum threshold value is equal to the fourth rate.
A method of detecting anomalies in digital images of products, each digital image being formed by a plurality of pixels, each pixel representing an associated location within the respective digital image and having a value characterising the associated location, each digital image defining one or more regions with each region including one pixel or multiple adjacent pixels, and wherein for each region a region value is determined, the region value comprising a value for at least one property of the respective region or a combined value for multiple properties of the respective region, the method comprising:
- (a) one or both of (i) detecting a respective region defined by a respective one of the digital images as a maximum anomaly when the region value of the respective region is greater than a predetermined maximum threshold value or a secondary maximum threshold value derived from the predetermined maximum threshold value and (ii) detecting the respective region defined by a respective one of the digital images as a minimum anomaly when the region value is less than a predetermined minimum threshold value or a secondary minimum threshold value derived from the predetermined minimum threshold value;
- and
- (b) wherein the predetermined maximum threshold value and the predetermined minimum threshold value are determined by,
- (i) for each of a number of learning digital images of bad products or of bad process products, specifying the one or more regions and determining the respective region value of each region and determining the maximum value of these respective region values as the maximum sample value of a maximum value sample or determining the minimum value of these respective region values as the minimum sample value of a minimum value sample;
- and
- (ii) using a statistical estimation method, determining estimates for all free, non-specified parameters of a probability density function specified to describe the maximum value sample using the maximum sample values and/or determining estimates for all free, non-specified parameters of a probability density function specified to describe the minimum value sample using the minimum sample values;
- and
- (iii) (I) setting a first rate at which a maximum anomaly is correctly detected in images to be examined or a second rate at which no maximum anomaly is incorrectly detected in images to be examined, or (II) setting a third rate at which a minimum anomaly is correctly detected in images to be examined or a fourth rate at which no minimum anomaly is incorrectly detected in images to be examined;
- and
- one or both of,
- (A) determining the maximum threshold value using the probability density function parameterised according to (b)(ii) or a distribution function corresponding thereto such that the probability of occurrence of a maximum value less than or equal to the maximum threshold value corresponds to the second rate or such that the probability of occurrence of a maximum value greater than or equal to the maximum threshold value corresponds to the first rate, and (B) determining the minimum threshold value using the probability density function parameterised in accordance with (b)(ii) or a distribution function corresponding thereto such that the probability of occurrence of a minimum value greater than or equal to the minimum threshold value is equal to the fourth rate or such that the probability of occurrence of a minimum value less than or equal to the minimum threshold value is equal to the third rate.
Dependencies are inferred from the claim text.