Object identification and inventory management

• US 9,152,862 B2
• Filed: 09/14/2012
• Issued: 10/06/2015
• Est. Priority Date: 09/15/2011
• Status: Active Grant

• Current Assignee: Alitheon, Inc.
• Original Assignee: RAF Technology, Incorporated (Constellation Software Incorporated)
• Inventors: Ross, David Justin, Elmenhurst, Brian J.
• Primary Examiner(s): Mehta, Bhavesh
• Assistant Examiner(s): Mistry, Oneal R
• Application Number:
  US13/618,362
• Publication Number:
  US 20140140570A1
• Time in Patent Office: 1,117 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06T 2207/30108 Industrial image inspection
• CPC Class Codes: G06T 7/0008 checking presence/absence
• CPC Class Codes: G06V 20/80 Recognising image objects characterised by unique random patterns

Abstract

A method/apparatus for identifying an object based on a pattern of structural features located in a particular region wherein the pattern comprises at least one fingerprint feature. The region may be recognized and used to identify the object. A first feature vector (FV) may be extracted from a first image of the pattern and may be mapped to an object identifier. To authenticate the object, a second FV may be extracted from a second image of the same region. The FVs may be compared and difference(s) determined. A match correlation value (MCV) may be calculated based on the difference(s). The difference(s) may be dampened if associated with expected wear and tear reducing the impact of the difference(s) on the MCV. The differences may be enhanced if associated with changes that are not explainable as wear and tear increasing the impact of the difference(s) on the MCV.

Claims

1. A machine-implemented method comprising:

recognizing a structural feature of an object;

selecting a region of the object based on the structural feature;

provisioning a database coupled to a network for communications;

capturing a first image of the selected region so as to form first image data, wherein the first image has sufficient resolution to show at least one fingerprint feature;

processing the first image data to extract a first feature vector expressing the fingerprint feature, wherein the first feature vector comprises a first set of numeric values, each of the numeric values responsive to light intensity in a corresponding sub-region of the first image; and

storing the first feature vector in the database in association with a unique identifier associated with the object;

after said storing, performing a process with respect to an article, the process comprising;

selecting a region of an article, the selected region of the article corresponding to the selected region of the object;

capturing a second image of the selected region of the article, so as to form second image data, wherein the second image has substantially the same resolution as the first image;

processing the second image data to extract a second feature vector, wherein the second feature vector comprises a second set of numeric values, the second set of numeric values responsive to the same respective sub-regions of the second image as the first set of numeric values are to the first image;

querying the database using the identifier of the object as an index to access the first feature vector;

comparing the second feature vector to the stored first feature vector, by comparing the first set of numeric values to the second set of numeric values to determine difference values;

responsive to the comparison, augmenting or modifying a difference between the first and second feature vectors, the augmenting or modifying comprising at least one of reducing at least one of said difference values or increasing at least one of said difference values;

calculating a match correlation value responsive to the augmenting or modifying; and

identifying a match between the object and the article in the case that the match correlation value exceeds a predetermined threshold.

2. The method of claim 1 further comprising calculating the match correlation value asymmetrically, including:

determining whether the first feature vector indicates a feature that is absent from the second feature vector; and

enhancing the match correlation based on a result of the determination.

3. The method of claim 1 further comprising calculating the match correlation value asymmetrically, including:

determining whether the second feature vector indicates a feature that is absent from the first feature vector; and

dampening match correlation based on a result of the determination.

4. The method of claim 1 further comprising reporting a match in the case that the match correlation value exceeds the predetermined threshold.

5. The method of claim 1 further comprising dampening the match correlation value by eliminating ones of the difference values that are below a threshold value.

6. The method of claim 1 further comprising dampening the match correlation value by reducing ones of the difference values that are below a threshold value.

7. The method of claim 1 wherein increasing at least one of the difference values further comprises increasing ones of the difference values that are above a threshold value.

8. The method of claim 1 wherein the fingerprint feature comprises at least one of:

cracks or other non-uniformities in surface material, a crystalline pattern, a pattern of fibers, a bleed pattern, a pattern of fabrication irregularities.

9. The method of claim 1 wherein extracting the first feature vector includes selecting an average grayscale level in each sub-region of the first image as the corresponding numeric value for that sub-region, and selecting an average grayscale value in each sub-region of the second image as the corresponding numeric value for that sub-region.

10. The method of claim 9 wherein the comparing step includes calculating a sum of the squares of the difference values, and then comparing the sums of squares.

11. The method of claim 1 wherein augmenting or modifying includes, only in the case that a square of an individual difference in corresponding numeric values is below a predetermined threshold value, reducing a corresponding one of the difference values before calculating the sum of the squares of the difference values, thereby dampening the difference.

12. The method of claim 1 including storing the location of the selected region in the database and maintaining the location as confidential.

13. The method of claim 1 wherein the object is one of a weapon, a document, a coin, a gemstone, an animal, and an art work.

14. A machine-implemented method comprising:

capturing a first digital image of a first selected region of an object, wherein the first digital image has sufficient resolution to show an area in the first selected region comprising a first pattern of features;

processing the first digital image to generate a first feature vector comprising data corresponding to the first pattern of features of the object; and

storing the first feature vector and the first digital image in a database in association with an object identifier;

after said storing, performing a process with respect to an article, the process comprising;

selecting a second selected region of the article, the second selected region of the article corresponding to the first selected region of the object;

capturing a second digital image of the second selected region of the article, wherein the second digital image has sufficient resolution to show an area comprising a second pattern of features;

processing the second digital image to generate a second feature vector comprising data corresponding to the second pattern of features of the article;

comparing the first feature vector with the second feature vector to determine difference values;

responsive to the comparison, augmenting or modifying a difference between the first and second feature vectors, the augmenting or modifying comprising at least one of reducing at least one of said difference values or increasing at least one of said difference values;

determining a match confidence that the data corresponding to the second pattern of features matches the data corresponding to the first pattern of features responsive to the augmenting or modifying;

comparing the match confidence to a predetermined threshold; and

in the case that the match confidence exceeds the predetermined threshold, indicating that the article is the object.

15. The method of claim 14 wherein the first pattern of features is a natural product of manufacture of the object or manufacture of a material or piece incorporated into the object.

16. The method of claim 15 wherein the first pattern of features comprises metal crystal structure defects or variations, the data of the first feature vector indicating a location of at least one defect or variation in a crystalline structure of a metal region recorded in the digital image.

17. The method of claim 15 wherein the first pattern of features comprises metal machining or milling marks presumably resulting from manufacture of a material or piece incorporated into the object.

18. A system, comprising:

one or more processors configured to;

capture a digital image of a region of an item, the region including an identifiable structure;

extract data representing at least one fingerprint feature from the digital image of the region of the item; and

store fingerprint feature data in a first feature vector in memory;

after said storing, select a region of an article, wherein the selected region of the article corresponds to the region of the item;

capture a digital image of the selected region of the article;

process digital image data of the digital image of the selected region of the article to extract a second feature vector;

compare the feature vectors to determine difference values and determine whether to augment or modify a difference between the first and second feature vectors;

in response to determining to augment or modify the difference between the first and second feature values, reduce at least one of said difference values or increase at least one of said difference values to generate a first match correlation value; and

in response to determining to not augment or modify the difference between the first and second feature values, generate a second match correlation value that is different than the first match correlation value.

19. The system of claim 18 wherein the digital image of the region of the item is a high-resolution color image, detailed enough to show elements of a grain surface of inter- and intra-character regions proximate to the identifiable structure.

20. The system of claim 19 wherein the item is a weapon and the identifiable structure is an alphanumeric identifier.

21. The system of claim 20 wherein the weapon is a handgun, rifle or shotgun.

22. The system of claim 21 wherein the one or more processors are further configured to transmit the encrypted digital image and the encrypted fingerprint data and the alphanumeric identifier of the item to an inventory control system in association with one another.

23. The system of claim 20 wherein the fingerprint feature is a natural product of manufacture of the weapon or manufacture of a material or piece incorporated into the weapon.

24. The system of claim 23 wherein the fingerprint feature comprises metal crystal structure defects or variations, the fingerprint feature data indicating a location of at least one defect or variation in a crystalline structure of a metal region.

25. The system of claim 18 wherein the digital image of the region of the item is detailed enough to reveal variations, imperfections and/or flaws in the individual characters within an alphanumeric identifier.

26. The system of claim 18 wherein the fingerprint feature comprises metal machining or milling marks presumably resulting from manufacture of a material or piece incorporated into the weapon.

27. The system of claim 18 wherein the region of the item is selected based on a predetermined offset vector from the identifiable structure on the item and wherein the one or more processors are further configured to identify the fingerprint feature based on the offset from the identifiable feature, and store data identifying the region in a fingerprint database in association with an alphanumeric identifier of the item.

28. The system of claim 18 wherein the one or more processors are further configured to encrypt the data identifying said region where the fingerprint feature is located.