Analyzing Objects Via Hyper-Spectral Imaging and Analysis

• US 20110052019A1
• Filed: 08/27/2010
• Published: 03/03/2011
• Est. Priority Date: 09/03/2009
• Status: Abandoned Application

• Current Assignee: Green Vision Systems Limited
• Original Assignee: Green Vision Systems Limited
• Inventors: MOSHE, Danny
• Application Number:
  US12/870,088
• Publication Number:
  US 20110052019A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06V 10/40 Extraction of image or video features

Abstract

Analyzing and classifying an object via hyper-spectral imaging and analysis. Generating, collecting respective reference objects and object hyper-spectral image data and information, of: (i) a set of reference objects related to or/and associated with the object, and (ii) the object, via a hyper-spectral imaging and analysis system. Forming, storing: (i) global reference database associated with reference objects hyper-spectral image data and information, and (ii) object database associated with object hyper-spectral image data and information, respectively, by processing and analyzing reference objects and object hyper-spectral image data and information, via a data-information processing and analyzing unit. Forming, storing a sub-global reference database associated with a sub-set of reference objects hyper-spectral image data and information and with a sub-set of the global reference database, by processing and analyzing reference objects and object hyper-spectral image data and information, and, the global reference database and the object database. Identifying, storing an object classification, by processing and analyzing the object database and the sub-global reference database, via the data-information processing and analyzing unit.

Claims

1. A method of analyzing and classifying an object via hyper-spectral imaging and analysis, the method comprising:

generating and collecting respective reference objects and object hyper-spectral image data and information, of;

(i) a set of reference objects related to or/and associated with the object, and (ii) the object, respectively, via a hyper-spectral imaging and analysis system;

forming and storing;

(i) a global reference database associated with said reference objects hyper-spectral image data and information, and (ii) an object database associated with said object hyper-spectral image data and information, respectively, by processing and analyzing said reference objects and said object hyper-spectral image data and information, via a data-information processing and analyzing unit of said hyper-spectral imaging and analysis system;

forming and storing a sub-global reference database associated with a sub-set of said reference objects hyper-spectral image data and information and with a sub-set of said global reference database, by processing and analyzing said reference objects and said object hyper-spectral image data and information, and, said global reference database and said object database, via said data-information processing and analyzing unit; and

identifying and storing an object classification, by processing and analyzing said object database and said sub-global reference database, via said data-information processing and analyzing unit.

2. The method of claim 1, wherein said forming and storing said sub-global reference database includes forming, and storing sets of reference object feature functions and object feature functions.

3. The method of claim 2, wherein each said reference object feature function is defined in terms of, related to, or/and associated with, elements of said global reference database.

4. The method of claim 2, wherein each said object feature function is defined in terms of, related to, or/and associated with, elements of said object database.

5. The method of claim 2, wherein each said reference object feature function is a function of one or more hyper-spectral imaging parameters selected from the group consisting of reference object emission wavelengths, reference object emission frequencies, and reference object emission energies, of said hyper-spectrally imaged reference objects.

6. The method of claim 2, wherein each said reference object feature function is a function of one or more hyper-spectral imaging parameters selected from the group consisting of reference object image shapes, and reference object image qualities.

7. The method of claim 2, wherein each said reference object feature function is related to or/and associated with one or more particular biophysicochemical properties, characteristics, or behaviors, of said reference object.

8. The method of claim 5, wherein each said reference object feature function is related to or/and associated with one or more particular biophysicochemical properties, characteristics, or behaviors, of said reference object.

9. The method of claim 5, wherein said reference object feature function is a linear or/and non-linear combination of said hyper-spectral imaging parameters.

10. The method of claim 5, wherein said reference object feature function is a ratio of two of said hyper-spectral imaging parameters.

11. The method of claim 5, wherein said reference object feature function is a ratio of two different said hyper-spectral imaging parameters of a hyper-spectrally imaged reference object, which are identified and selected from two corresponding different specific locations in a hyper-spectral image of a said reference object.

12. The method of claim 10, wherein said ratio form of said reference object feature function is related to or/and associated with one biophysicochemical property, characteristic, or behavior, of said reference object.

13. The method of claim 10, wherein said ratio form of said reference object feature function is related to or/and associated with two different biophysicochemical properties, characteristics, or behaviors, of said reference object.

14. The method of claim 6, wherein each said reference object feature function is related to or/and associated with one or more particular biophysicochemical properties, characteristics, or behaviors, of said reference object.

15. The method of claim 6, wherein said reference object feature function is a linear or/and non-linear combination of said hyper-spectral imaging parameters.

16. The method of claim 6, wherein said reference object feature function is a ratio of two of said hyper-spectral imaging parameters.

17. The method of claim 6, wherein said reference object feature function is a ratio of two different said hyper-spectral imaging parameters of a hyper-spectrally imaged reference object, which are identified and selected from two corresponding different specific locations in a hyper-spectral image of a said reference object.

18. The method of claim 2, wherein each said object feature function is a function of one or more hyper-spectral imaging parameters selected from the group consisting of object emission wavelengths, object emission frequencies, and object emission energies, of said hyper-spectrally imaged object.

19. The method of claim 2, wherein each said object feature function is a function of one or more hyper-spectral imaging parameters selected from the group consisting of object image shapes, and object image qualities.

20. The method of claim 2, wherein each said object feature function is related to or/and associated with one or more particular biophysicochemical properties, characteristics, or behaviors, of the object.

21. The method of claim 18, wherein each said object feature function is related to or/and associated with one or more particular biophysicochemical properties, characteristics, or behaviors, of the object.

22. The method of claim 18, wherein said object feature function is a linear or/and non-linear combination of said hyper-spectral imaging parameters.

23. The method of claim 18, wherein said object feature function is a ratio of two of said hyper-spectral imaging parameters.

24. The method of claim 18, wherein said object feature function is a ratio of two different said hyper-spectral imaging parameters of a hyper-spectrally imaged object, which are identified and selected from two corresponding different specific locations in a hyper-spectral image of the object.

25. The method of claim 23, wherein said ratio form of said object feature function is related to or/and associated with one biophysicochemical property, characteristic, or behavior, of said reference object.

26. The method of claim 23, wherein said ratio form of said object feature function is related to or/and associated with two different biophysicochemical properties, characteristics, or behaviors, of said reference object.

27. The method of claim 19, wherein each said object feature function is related to or/and associated with one or more particular biophysicochemical properties, characteristics, or behaviors, of the object.

28. The method of claim 19, wherein said object feature function is a linear or/and non-linear combination of said hyper-spectral imaging parameters.

29. The method of claim 19, wherein said object feature function is a ratio of two of said hyper-spectral imaging parameters.

30. The method of claim 19, wherein said object feature function is a ratio of two different said hyper-spectral imaging parameters of the hyper-spectrally imaged object, which are identified and selected from two corresponding different specific locations in a hyper-spectral image of the object.

31. The method of claim 2, wherein said identifying and storing said object classification includes comparing values of said reference object feature functions to values of said object feature functions, for identifying said values which are identically or approximately equal, and assigning such said values to said object classification.