Method for determining spatial coherences among features of an object

• US 5,970,421 A
• Filed: 11/19/1997
• Issued: 10/19/1999
• Est. Priority Date: 11/19/1997
• Status: Expired due to Term

• Current Assignee: Target Strike Incorporated
• Original Assignee: Target Strike Incorporated
• Inventors: Ostrovsky, Emil Y.
• Primary Examiner(s): McElheny, Jr., Donald E.
• Application Number:
  US08/972,989
• Time in Patent Office: 699 Days
• Field of Search: 702/1, 702/5, 702/16, 702/2
• US Class Current: 702/1
• CPC Class Codes: G01V 1/366 by correlation of seismic signals
• CPC Class Codes: G01V 11/00 Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00

Abstract

A processing system implements a method for establishing spatial coherences among features of an object. The processing system begins by measuring at least one physical property of the object at a plurality of different locations to construct at least one data field. Second, the processing system digitizes the data within the data field. Third, the processing system arranges the data within the data field into a matrix. Fourth, the processing system creates a target dichotomy matrix from the matrix. Fifth, the processing system transforms the data within the matrix to develop transformation matrices. Sixth, the processing system develops base dichotomy matrices from the transformation matrices. Seventh, the processing system creates a full description set of matrices utilizing the base dichotomy matrices. Eighth, the processing system creates a matrix that establishes spatial coherences among features of the object utilizing the full description set of matrices. Finally, the processing system displays the matrix that establishes spatial coherences among features of the object.

Claims

1. A method for establishing spatial coherences among features of an object, comprising the steps of:

measuring at least one physical property of the object at a plurality of different locations to construct at least one data field;

digitizing the data within the data field;

arranging the data within the data field into a matrix;

creating a target dichotomy matrix from the matrix;

transforming the data within the matrix to develop transformation matrices;

developing base dichotomy matrices from the transformation matrices;

creating a full description set of matrices utilizing the base dichotomy matrices;

creating a matrix that establishes spatial coherences among features of the object utilizing the full description set of matrices; and

displaying the matrix that establishes spatial coherences among features of the object.

2. The method according to claim 1 further comprising the step of determining a minimum number of base dichotomy matrices necessary to develop the full description set of matrices.

3. The method according to claim 2 wherein the step of creating a matrix that establishes spatial coherences among features of the object utilizing the full description set of matrices, comprises the steps of:

creating density matrices utilizing the full description set of matrices and the target dichotomy matrix;

creating a multi-level density matrix utilizing the density matrices; and

displaying the multi-level density matrix to establish spatial coherences among features of the object.

4. The method according to claim 2 wherein the step of creating a matrix that establishes spatial coherences among features of the object utilizing the full description set of matrices comprises the steps of:

creating a plurality of target dichotomy matrices;

creating aggregate density matrices utilizing the full description set of matrices and the plurality of target dichotomy matrices;

creating a multi-level density matrix utilizing the aggregate density matrices; and

displaying the multi-level density matrix to establish spatial coherences among features of the object.

5. The method according to claim 1 wherein the step of developing a target dichotomy matrix, comprises the steps of:

placing a boundary around a target region within the matrix;

assigning a logical "1" to any data within the target region; and

assigning a logical "0" to any target data outside the target region.

6. The method according to claim 1 wherein the step of transforming the data within the matrix to develop transformation matrices comprises applying at least one transform function to the data within the matrix.

7. The method according to claim 1 wherein the step of developing base dichotomy matrices from the transformation matrices, comprises the steps of:

calculating a median value for a transformation matrix;

assigning a logical "0" to any transformed data within the transformation matrix less than the median value;

assigning a logical "1" to any transformed data within the transformation matrix greater than the median value;

randomly assigning logical "1'"'"'s" and logical "0'"'"'s" to any transformed data within the transformation matrix equaling the median value; and

repeating the above steps for each transformation matrix.

8. The method according to claim 2 wherein the step of creating a full description set of matrices utilizing the base dichotomy matrices, comprises the steps of:

selecting a desired number of most distinct base dichotomy matrices used in creating the full description set of matrices wherein the desired number is greater than the minimum number;

creating a companion matrix for each of the selected most distinct base dichotomy matrices;

forming a base level of matrices utilizing each of the selected most distinct base dichotomy matrices and its companion matrix; and

intersecting the base level matrices in increasing numbers up to the desired number to build intersection level matrices that form the full description set of matrices.

9. The method according to claim 8 wherein the step of selecting a desired number of most distinct base dichotomy matrices used in creating the full description set of matrices wherein the desired number is greater than the minimum number, comprises the step of:

determining a degree of compactness for each base dichotomy matrix;

ordering the base dichotomy matrices from the most compact to the least compact;

assessing the degree of similarity between each pair of base dichotomy matrices;

selecting a threshold degree of similarity number;

utilizing the threshold degree of similarity number to form a number of clusters of base dichotomy matrices equal to the desired number; and

selecting the most representative base dichotomy matrix from each cluster.

10. The method according to claim 3 wherein the step of creating density matrices utilizing the full description set of matrices and the target dichotomy matrix, comprises the steps of:

selecting a threshold degree of overlap number;

comparing a matrix of the full description set with the target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number;

repeating the above steps for each matrix in an intersection level of the full description set;

developing a density number for each cell of a density matrix utilizing matrices selected from an intersection level of the full description set; and

performing the above steps beginning at the minimum number intersection level and proceeding to a preselected intersection level or a base level of the full description set of matrices.

11. The method according to claim 3 wherein the step of creating density matrices utilizing the full description set of matrices and the target dichotomy matrix, comprises the steps of:

selecting a threshold degree of overlap number and a threshold measure of similarity number;

comparing a matrix of the full description set with the target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number and the measure of similarity number;

repeating the above steps for each matrix in an intersection level of the full description set;

developing a density number for each cell of a density matrix utilizing matrices selected from an intersection level of the full description set; and

performing the above steps beginning at an intersection level corresponding to the minimum number and proceeding to an intersection level that does not satisfy the threshold measure of similarity number, a preselected intersection level, or a base level of the full description set of matrices.

12. The method according to claim 3 wherein the step of creating a multi-level density matrix comprises the step of vertically combining the density matrices.

13. The method according to claim 4 wherein the step of creating aggregate density matrices utilizing the full description set of matrices and the plurality of target dichotomy matrices, comprises the steps of:

selecting a threshold degree of overlap number;

comparing a matrix of the full description set with a target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number;

repeating the above steps for each matrix in an intersection level of the full description set;

developing a density number for each cell of a density matrix utilizing matrices selected from an intersection level of the full description set;

repeating the above steps for each target dichotomy matrix of the plurality of target dichotomy matrices;

summing the density numbers for each cell of each density matrix to form an aggregate density matrix; and

performing the above steps beginning at the minimum number intersection level and proceeding to a preselected intersection level or a base level of the full description set of matrices.

14. The method according to claim 4 wherein the step of creating aggregate density matrices utilizing the full description set of matrices and the plurality of target dichotomy matrices, comprises the steps of:

selecting a threshold degree of overlap number and a threshold measure of similarity number;

comparing a matrix of the full description set with a target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number and the measure of similarity number;

repeating the above steps for each matrix in an intersection level of the full description set;

developing a density number for each cell of a density matrix utilizing matrices selected from an intersection level of the full description set;

repeating the above steps for each target dichotomy matrix of the plurality of target dichotomy matrices;

summing the density numbers for each cell of each density matrix to form an aggregate density matrix; and

performing the above steps beginning at an intersection level corresponding to the minimum number and proceeding to an intersection level that does not satisfy the threshold measure of similarity number, a preselected intersection level, or a base level of the full description set of matrices.

15. The method according to claim 4 wherein the step of creating a multi-level density matrix comprises the step of vertically combining the aggregate density matrices.

16. The method according to claim 2 wherein the step of creating a matrix that establishes spatial coherences among features of the object utilizing the full description set of matrices comprises the steps of:

creating measure of similarity matrices utilizing the full description set of matrices and the target dichotomy matrix;

creating a multi-level measure of similarity matrix utilizing the measure of similarity matrices; and

displaying the multi-level measure of similarity matrix to establish spatial coherences among features of the object.

17. The method according to claim 16 wherein the step of creating measure of similarity matrices utilizing the full description set of matrices and the target dichotomy matrix, comprises the steps of:

selecting a threshold degree of overlap number;

comparing a matrix of the full description set with the target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number;

repeating the above steps for each matrix in an intersection level of the full description set;

developing a similarity number for each cell of a measure of similarity matrix utilizing matrices selected from an intersection level of the full description set; and

performing the above steps beginning at the minimum number intersection level and proceeding to a preselected intersection level or a base level of the full description set of matrices.

18. The method according to claim 16 wherein the step of creating measure of similarity matrices utilizing the full description set of matrices and the target dichotomy matrix, comprises the steps of:

selecting a threshold degree of overlap number and a threshold measure of similarity number;

comparing a matrix of the full description set with the target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number and the measure of similarity number;

repeating the above steps for each matrix in an intersection level of the full description set;

developing a similarity number for each cell of a measure of similarity matrix utilizing matrices selected from an intersection level of the full description set; and

performing the above steps beginning at an intersection level corresponding to the minimum number and proceeding to an intersection level that does not satisfy the threshold measure of similarity number, a preselected intersection level, or a base level of the full description set of matrices.

19. The method according to claim 16 wherein the step of creating a multi-level measure of similarity matrix comprises the step of vertically combining the measure of similarity matrices.

20. The method according to claim 2 wherein the step of creating a matrix that establishes spatial coherences among features of the object utilizing the full description set of matrices comprises the steps of:

creating a plurality of target dichotomy matrices;

creating aggregate measure of similarity matrices utilizing the full description set of matrices and the plurality of target dichotomy matrices;

creating a multi-level measure of similarity matrix utilizing the aggregate measure of similarity matrices; and

displaying the multi-level measure of similarity matrix to establish spatial coherences among features of the object.

21. The method according to claim 20 wherein the step of creating aggregate measure of similarity matrices utilizing the full description set of matrices and the plurality of target dichotomy matrices, comprises the steps of:

selecting a threshold degree of overlap number;

comparing a matrix of the full description set with a target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number;

repeating the above steps for each matrix in an intersection level of the full description set;

developing a similarity number for each cell of a measure of similarity matrix utilizing matrices selected from an intersection level of the full description set;

repeating the above steps for each target dichotomy matrix of the plurality of target dichotomy matrices;

summing the similarity numbers for each cell of each density matrix to form an aggregate measure of similarity matrix; and

performing the above steps beginning at the minimum number intersection level and proceeding to a preselected intersection level or a base level of the full description set of matrices.

22. The method according to claim 20 wherein the step of creating aggregate measure of similarity matrices utilizing the full description set of matrices and the plurality of target dichotomy matrices, comprises the steps of:

selecting a threshold degree of overlap number and a threshold measure of similarity number;

comparing a matrix of the full description set with a target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number and the measure of similarity number;

repeating the above steps for each matrix in an intersection level of the full description set;

developing a similarity number for each cell of a measure of similarity matrix utilizing matrices selected from an intersection level of the full description set;

repeating the above steps for each target dichotomy matrix of the plurality of target dichotomy matrices;

summing the similarity numbers for each cell of each measure of similarity matrix to form an aggregate measure of similarity matrix; and

performing the above steps beginning at an intersection level corresponding to the minimum number and proceeding to an intersection level that does not satisfy the threshold measure of similarity number, a preselected intersection level, or a base level of the full description set of matrices.

23. The method according to claim 20 wherein the step of creating a multi-level measure of similarity matrix comprises the step of vertically combining the aggregate measure of similarity matrices.

24. The method according to claim 2 wherein the step of creating a matrix that establishes spatial coherences among features of the object utilizing the full description set of matrices comprises the steps of:

creating maximum measure of similarity matrices utilizing the full description set of matrices and the target dichotomy matrix;

creating a multi-level maximum measure of similarity matrix utilizing the measure of similarity matrices; and

displaying the multi-level maximum measure of similarity matrix to establish the most highly organized zone within the object.

25. The method according to claim 24 wherein the step of creating maximum measure of similarity matrices utilizing the full description set of matrices and the target dichotomy matrix, comprises the steps of:

selecting a threshold degree of overlap number;

comparing a matrix of the full description set with the target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number;

repeating the above steps for each matrix in an intersection level of the full description set;

selecting a matrix from the matrices selected from an intersection level of the full description set that has the highest measure of similarity with the target dichotomy matrix; and

performing the above steps beginning at the minimum number intersection level and proceeding to a preselected intersection level or a base level of the full description set of matrices.

26. The method according to claim 24 wherein the step of creating maximum measure of similarity matrices utilizing the full description set of matrices and the target dichotomy matrix, comprises the steps of:

selecting a threshold degree of overlap number and a threshold measure of similarity number;

comparing a matrix of the full description set with the target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number and the measure of similarity number;

repeating the above steps for each matrix in an intersection level of the full description set;

selecting a matrix from the matrices selected from an intersection level of the full description set that has the highest measure of similarity with the target dichotomy matrix; and

performing the above steps beginning at an intersection level corresponding to the minimum number and proceeding to an intersection level that does not satisfy the threshold measure of similarity number, a preselected intersection level, or a base level of the full description set of matrices.

27. The method according to claim 24 wherein the step of creating a multi-level maximum measure of similarity matrix comprises the step of selecting a matrix from the maximum measure of similarity matrices that has the highest measure of similarity with the target dichotomy matrix.

28. The method according to claim 2 wherein the step of creating a matrix that establishes spatial coherences among features of the object utilizing the full description set of matrices comprises the steps of:

creating a plurality of target dichotomy matrices;

creating aggregate maximum measure of similarity matrices utilizing the full description set of matrices and the plurality of target dichotomy matrices;

creating a multi-level maximum measure of similarity matrix utilizing the aggregate maximum measure of similarity matrices; and

displaying the multi-level maximum measure of similarity matrix to establish spatial coherences among features of the object.

29. The method according to claim 28 wherein the step of creating aggregate maximum measure of similarity matrices utilizing the full description set of matrices and the plurality of target dichotomy matrices, comprises the steps of:

selecting a threshold degree of overlap number;

comparing a matrix of the full description set with a target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number;

repeating the above steps for each matrix in an intersection level of the full description set;

selecting a matrix from the matrices selected from an intersection level of the full description set that has the highest measure of similarity with the target dichotomy matrix;

repeating the above steps for each target dichotomy matrix of the plurality of target dichotomy matrices;

developing a maximum similarity number for each cell of a maximum measure of similarity matrix utilizing for each target dichotomy matrix the matrix selected as having the highest measure of similarity with the target dichotomy matrix; and

performing the above steps beginning at the minimum number intersection level and proceeding to a preselected intersection level or a base level of the full description set of matrices.

30. The method according to claim 28 wherein the step of creating aggregate maximum measure of similarity matrices utilizing the full description set of matrices and the plurality of target dichotomy matrices, comprises the steps of:

selecting a threshold degree of overlap number and a threshold measure of similarity number;

comparing a matrix of the full description set with a target dichotomy matrix;

selecting the matrix if it satisfies the threshold degree of overlap number and the measure of similarity number;

repeating the above steps for each matrix in an intersection level of the full description set;

selecting a matrix from the matrices selected from an intersection level of the full description set that has the highest measure of similarity with the target dichotomy matrix;

repeating the above steps for each target dichotomy matrix of the plurality of target dichotomy matrices;

developing a maximum similarity number for each cell of a maximum measure of similarity matrix utilizing for each target dichotomy matrix the matrix selected as having the highest measure of similarity with the target dichotomy matrix; and

performing the above steps beginning at an intersection level corresponding to the minimum number and proceeding to an intersection level that does not satisfy the threshold measure of similarity number, a preselected intersection level, or a base level of the full description set of matrices.

31. The method according to claim 28 wherein the step of creating a multi-level maximum measure of similarity matrix comprises the step selecting a matrix from the aggregate maximum measure of similarity matrices that has the highest measure of similarity with the target dichotomy matrix.