Patent Application Titled “Quantitative Image Analysis” Published Online (USPTO 20240087285): Detectsystem Lab A/S
2024 APR 03 (NewsRx) -- By a
The assignee for this patent application is Detectsystem Lab A/S (Vejle 0st,
Reporters obtained the following quote from the background information supplied by the inventors: “Digital documents such as files and images are widespread throughout society and industries. These digital documents may be classified in many ways regarding confidentiality and possible content of personal information making exchange and storage of such documents a cumbersome task. Furthermore, digital documents are relatively easy to manipulate to make different modified versions of the same digital document, e.g., an image may be resized, skewed or elements may be added or removed.
“For some purposes where such digital documents and images are to be used, it may be sufficient and more expedient to convert the digital documents and images into a reduced data format. While this reduced data format will still serve many purposes going forward, even in the absence of the digital document or image itself, it can more easily comply with the requirements for data compliance in relation to confidentiality, General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA) and the like.
“Fraud is a global problem that affects not least the insurance industry. It is estimated that 10% of all insurance pay-outs are made to fraudsters. To receive an insurance pay-out, various documents must be presented to validate the insurance claim. Even then, there are loopholes. Fraudsters seek to cheat insurers in a plethora of ways and today, fraud has moved into the digital arena too.
“Digital documents introduce a variety of new ways to cheat and commit fraud, not least insurance fraud. Verifying the uniqueness, ownership and authenticity of documents and items is very difficult when documents are digital files. In the past, digital rights management has been used on some file types to ensure that they were not copied, although the inconvenience thereof made it infeasible, and so insecure documents are here to stay. Verifying the uniqueness, ownership and authenticity of documents and items is the job of insurance investigators, who make value-judgments about documents throughout their workday. The more scrutinous they are, the slower and more expensive insurance pay-outs and premiums get. Some insurance companies have decided to solve this by being slack with verification and accepting as high as 20% fraud, since this allows them to have fewer investigators and so retain operative costs low.
“Digital documents can often be copied indiscriminately. Keeping track of documents can be difficult and re-use of the same claim document for multiple cases across different insurance companies do happen. When a claim document is re-used in multiple insurance claim across different insurance companies, it is often due to insurance fraud. Fraudster either use their own or others images in multiple claims e.g. images downloaded from the internet.
“To combat re-use of images in insurance fraud, some systems utilize a method where an image is converted to a HASH value and saved in a database. However, when a fraudster either mirrors, edits, rotates, resizes, crops or skews images, these traditional methods are no longer sufficient.
“As fraudsters utilizes the same images across different insurance companies, there is a need for a database comprised of images from different insurers. This will help them combat fraud by getting an alert signal when an image is an image that has been handled (used in an insurance claim) in earlier claims. However; saving images directly to a database that can be accessed by different employees at insurance companies is not compliant to e.g. GDPR. To combat future fraud, a method is needed to save images into a database in a compliant manner, while still having the ability to correctly verifying if an image is used in other insurance claims, even when said image has been modified, e.g. cropped, skewed, rotated and/or mirrored.
“There is a thus a need for increased security in the pay-out process as well as in verifying document authenticity at large. There is also a need for reducing the amount of data stored over time such as to limit data storage capacity expansions and minimize energy expenditure. Additionally, there is a need for being able to store reduced data format of digital images such that data is applicable for certain purposes while at the same time not requiring the cumbersome handling in compliance with confidentiality and data protection regulations.”
In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “In a first aspect, the present invention provides a method for quantitatively rating the degree of similarity between two digital images (100), comprising the following steps for each of the digital images (100):
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“Check whether image width is less than image height, if image width is less than image height rotate image 90 degrees to obtain a horizontal orientated digital image (190),
“resize the horizontal orientated digital image (190) to a resized digital image (110) having a certain fixed size and number of pixels (130),
“pixelate the digital image to pixels of a fixed size to obtain a pixilated image (200) with an average color of each pixelated area (195),
“calculate total pixel values of the pixelated image (200) top row and calculate total pixel values of the pixelated image (200) bottom row
“compare the total pixel value of the top row with the total pixel value of the pixilated image bottom row, if the total pixel value of pixilated image the top row is less than the total pixel value of the pixilated image bottom row, then rotate the pixilated image (200) 180 degrees; If the total pixel value of the pixilated image top row is equal to total pixel value of the pixilated image bottom row, then proceed calculating the next row from top row and compare to next row from bottom row, if the total pixel value of the next row from top are equal to the total pixel value of the next row from bottom proceed with next rows until the centre row is reached (201),
“calculate total pixel values of the pixelated image (200) first column and calculate total pixel values of the pixelated image (200) last column and compare the total pixel values; if the total pixel value of the pixilated image first-column is less than the total pixel value of the pixilated image last column value, then flip the pixilated image (200) over its vertical axis; if the total pixel value of the pixilated image first-column value is equal to the total pixel value of the pixilated image last column value, then proceed calculating the total pixel value of the pixilated image next column from first column and compare to the total pixel value of the pixilated image next column from last column; if the total pixel value of the pixilated image next column from first column are equal to total pixel value of the pixilated image of the next column from last column proceed with next columns until centre column is reached (202),
“read a pixel from each of the pixelated area in the pixilated image (198) and calculate closest predefined color of each of the pixels red (203) to obtain a pixel RGB,
“convert each pixel RGB color to HEX value (204),
“add each pixel HEX value to an image string (170) representing the image pixel value,
“then calculate a hit-score (180) as a percentage identity or homology between the image strings (170) of the two digital images (100), said hit-score (180) being the rating of the similarity between the two digital images (100).
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“In a preferred embodiment the method further comprises the step of rotating the pixilated image (200) 180 degrees if the total pixel value of the pixilated image next row from top is less than the total pixel value of the pixilated image next row from bottom.
“In a preferred embodiment the method further comprises the step of rotating the pixilated image (200) 180 degrees if the total pixel value of the pixilated image row V from top is less than the total pixel value of the pixilated image row V from bottom, where V is an integer larger than 1.
“In a preferred embodiment the method further comprises the step of flipping the pixilated image over its vertical axis if the total pixel value of the pixilated image next column from first column is less than the total pixel value of the pixilated image next column from last column.
“In a preferred embodiment the method further comprises the step of flipping the pixilated image over its vertical axis if the total pixel value of the pixilated image number Z column from first column is less than the total pixel value of the pixilated image of number Z column from last column, where Z is an integer larger than 1.
“In a preferred embodiment the pixilated image is rotated in the above-mentioned procedure before it is flipped in the above-mentioned procedure.
“In a second aspect, the present invention provides a method for quantitatively rating the degree of similarity between two digital images (100), comprising the following steps for each of the digital images (100):
“
“resize the digital image (100) to a resized digital image (110) having a certain fixed size and number of pixels,
“divide the resized digital image (110) into a number of sections (120) having X rows and Y columns so that each section has the same number of pixels (130),
“divide each section (120) into a number of pixels (130) by rows and columns, so that each section has the same number of pixels (130),
“for each pixel (130), determine the color code value set (140), e.g. RGB, and assign a score being an integer or a letter for each of the primary colors (e.g. R,
“for each section (120) assemble all the pixel strings (150) into a section string (160) by appending the pixel strings (150) in a fixed order through all the pixels (130) in the section (120), and
“assemble all the section strings (160) into an image string (170) by appending the section strings (160) in a fixed order through all the sections (120) of said resized digital image (110),
“then calculate a hit-score (180) as a percentage identity or homology between the image strings (170) of the two digital images (100), said hit-score (180) being the rating of the similarity between the two digital images (100).
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“Using this method, identical images and minor modifications thereof can be matched. If a high hit-score (180) is being calculated, then there is a high likelihood that two different images are indeed a set of an original image and a modified version of that original image.
“For example, a prior loss image (an image used in a previous insurance claim) may be provided in a different format or with different metadata in connection with fraudulent claims across different insurance companies. Also, such prior loss images may be provided with edits to part of the image, e.g., concealing certain blurred areas or cropping part out.
“Hence, in one embodiment the modification of the image is a rotated image, a resized image, a skewed image, a cropped image, a mirrored image, an image with addition or elimination of one or more elements such as text or signs, or a combination thereof.
“The present invention has several advantages, most notably that there is no need for storing previously handled images provided an image string (170) has been calculated by a known embodiment of the present method and stored. Since the image string (170) is a reduced data format that do not contain all the information from the image used for generating the image string (170), the confidentiality and data protection compliance challenges of storing images in a database (to e.g. see if an image has already been used in a previous insurance claim) are solved by the present invention.
“Storing only the reduced data format in the form of the image string (170) takes up only little space in a database and thus limits the speed of data storage capacity upgrade over time and it is energy-saving.
“Furthermore, by comparing a digital image through the use of image strings (170) with a list of historically used/generated image strings (170), a portion of insurance fraud attempts can be stopped in their tracks, freeing up insurance investigators to investigate other cases, such as more complicated cases. Yet further, the investigators need to perform fewer mouse-clicks on average to process an insurance claim.
“In a third aspect the present invention provides a method for determining whether an image (100) has already been handled as the same image (100) or a modification thereof, comprising the determination of an image string (170) by the method provided in the first aspect of the invention and calculating the hit-score as a percentage identity or homology between the image string (170) of said image and the image strings (170) in a database (205) comprising the image strings of previously handled images for which image strings (170) have been calculated and stored in the database (205).
“An important advantage of the methods of both the first and second aspect of the present invention is that they do not require access to the previously handled images for which image strings (170) are stored in a database (205).
“In a fourth aspect the present invention provides the use of the methods for the verification of the uniqueness of an image, such as from a digital image (100).
“In a fifth aspect the present invention provides the use of the methods in the process of handling insurance claims for increased security in the pay-out process.
“In a sixth aspect the present invention provides a computing device (210) having a processor (211) adapted to perform the steps of the methods.
“In a further aspect the present invention provides a computer program comprising instructions which cause the computer (210) to carry out one of the methods, when the program is executed by a computer (210).
“In a yet further aspect, the present invention provides computer-readable medium comprising instructions which cause the computer (210) to carry out one of the methods, when executed by a computer (210).”
The claims supplied by the inventors are:
“1. A method for quantitatively rating the degree of similarity between two digital images, comprising the following steps for each of the digital images: check whether image width is less than image height, if image width is less than image height rotate image 90 degrees to obtain a horizontal orientated digital image, resize the horizontal orientated digital image to a resized digital image having a certain fixed size and number of pixels, pixelate the digital image to pixels of a fixed size to obtain a pixilated image with an average color of each pixelated area, calculate total pixel values of the pixelated image top row and calculate total pixel values of the pixelated image bottom row, compare the total pixel value of the top row with the total pixel value of the pixilated image bottom row, if the total pixel value of pixilated image the top row is less than the total pixel value of the pixilated image bottom row, then rotate the pixilated image 180 degrees; If the total pixel value of the pixilated image top row is equal to total pixel value of the pixilated image bottom row, then proceed calculating the next row from top row and compare to next row from bottom row, if the total pixel value of the next row from top are equal to the total pixel value of the next row from bottom proceed with next rows until the centre row is reached, calculate total pixel values of the pixelated image first column and calculate total pixel values of the pixelated image last column and compare the total pixel values; if the total pixel value of the pixilated image first-column is less than the total pixel value of the pixilated image last column value, then flip the pixilated image over its vertical axis; if the total pixel value of the pixilated image first-column value is equal to the total pixel value of the pixilated image last column value, then proceed calculating the total pixel value of the pixilated image next column from first column and compare to the total pixel value of the pixilated image next column from last column; if the total pixel value of the pixilated image next column from first column are equal to total pixel value of the pixilated image of the next column from last column proceed with next columns until centre column is reached, read a pixel from each of the pixelated area in the pixilated image and calculate closest predefined color of each of the pixels read to obtain a pixel RGB, convert each pixel RGB color to HEX value, add each pixel HEX value to an image string representing the image pixel value, and then calculate a hit-score as a percentage identity or homology between the image strings of the two digital images, said hit-score being the rating of the similarity between the two digital images.
“2. A method according to claim 1 further comprising the step of rotating the pixilated image 180 degrees if the total pixel value of the pixilated image row V from top is less than the total pixel value of the pixilated image row V from bottom, where V is an integer larger than 1.
“3. A method according to claim 1 further comprising the step of flipping the pixilated image over its vertical axis if the total pixel value of the pixilated image number Z column from first column is less than the total pixel value of the pixilated image of number Z column from last column, where Z is an integer larger than 1.
“4. A method for quantitatively rating the degree of similarity between two digital images, comprising the following steps for each of the digital images: resize the digital image to a digital image having a certain fixed size and number of pixels, divide the resized digital image into a number of sections having X rows and Y columns so that each section has the same number of pixels, divide each section into a number of pixels by rows and columns so that each section has the same number of pixels, for each pixel, determine the color code value set, e.g. RGB, and assign a score being an integer or a letter for each of the primary colors (e.g. R,
“5. The method according to claim 4, wherein X and Y are the same, i.e. division of the resized digital images are being pixelated and colors transformed to closest colors from predefined color list.
“6. The method according to claim 4, wherein X and Y are both 3 or 4.
“7. The method according to claim 4, wherein said number of pixels in each section is in the range from 6 to 70, in the range from 8 to 49, in the range from 9 to 25, in the range from 9 to 16, or wherein said number of pixels in each section is 8, 9, 12, 15, 16 or 20.
“8. The method according to claim 1, wherein the color code value set is RGB (red, green, blue) giving a three digit/letter pixel string or CMYK (cyan, magenta, yellow and black) giving a four digit/letter pixel string.
“9. The method according to claim 1, wherein said score being an integer or a letter is selected from an integer, a single digit integer, an integer in the range from 1 to 7, an integer in the range from 1 to 5 and an integer in the range from 1 to 3.
“10. The method according to claim 1, wherein said score being an integer or a letter is selected from a letter, a letter from a group of three letters, a letter from a group of five letters or a letter from a group of seven letters, such as (a, b, c) or (f, g, h, i, j).
“11. The method according to claim 4, wherein said fixed order for assembling the strings is row by row starting from the top row moving down or starting from the bottom row moving up.
“12. The method according to claim 4, wherein said fixed order for assembling the strings is column by column starting from the left hand column moving right or starting from the right hand column moving left.
“13. A method for determining whether a digital image has already been handled as the same digital image or a modification thereof, comprising the determination of an image string by the method defined in claim 1 and calculating the hit-score as a percentage identity or homology between the image string of said image and the image strings in a database comprising the image strings of previously handled images, for which image strings have been calculated and stored in the database.
“14. The method of claim 13, which does not require access to the previously handled digital images for which image strings are stored in a database.
“15. The method according to claim 13, wherein said modification of the image is a rotated image, a resized image, a skewed image, a cropped image, a mirrored image, an image with addition or elimination of one or more elements such as text or signs, or a combination thereof.
“16. Use of the method as defined in claim 1 for the verification of the uniqueness of an image, such as a digital image.
“17. Use of the method as defined in claim 1 in the process of handling insurance claims for increased security in the pay-out process.
“18. A computing device having a processor adapted to perform the steps of a method as defined in claim 1.
“19. A computer program comprising instructions which cause the computer to carry out the method as defined in claim 1, when the program is executed by a computer.
“20. A computer-readable medium comprising instructions which cause the computer to carry out the method as defined in claim 1, when executed by a computer.”
For more information, see this patent application: HOLM, Rasmus Bakgard; LOVMAND, Jan; TORP, Lars. Quantitative Image Analysis.
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