Apparatus and method for characterizing digital images using a two axis image sorting technique
First Claim
Patent Images
1. A method of characterizing an M-column by N-row array of image pixels, said method comprising:
- (1) reading the amplitudes of said pixels;
(2) assigning column and row indices to said amplitudes;
(3) organizing said amplitudes into segment amplitude sets for M columns and N rows;
(4) organizing segment period sets for M columns and N rows(5) calculating mean value sets for said segment amplitude sets;
(6) calculating standard deviation sets for said mean value sets;
(7) using said mean value sets and said standard deviation sets to characterize said array of image pixels, and(8) determining registration errors through calculation of difference coefficients d(c) and d(r) defined by the following equations;
D(c)=[Ma(c,A)−
Ma(c,B)][Da(c,A)−
Da(c,B)][Mt(c,A)−
Mt(c,B)][Dt(c,A)−
Dt(c,B)]
D(r)=[Ma(r,A)−
Ma(r,B)][Da(r,A)−
Da(r,B)][Mt(r,A)−
Mt(r,B)][Dt(r,A)−
Dt(r,B)]where;
Ma(c,X)=column amplitude mean axis function for image X, where “
c”
denotes a particular columnDa(c,X)=column amplitude deviation axis function for image X, where “
c”
denotes a particular columnMt(c,X)=column period mean axis function for image X, where “
c”
denotes a particular columnDt(c,X)=column period deviation axis function for image X, where “
c”
denotes a particular columnMa(r,X)=row amplitude mean axis function for image X, where “
r”
denotes a particular row,Da(r,X)=row amplitude deviation axis function for image X, where “
r”
denotes a particular row,Mt(r,X)=row period mean axis function for image X, where “
r”
denotes a particular row,Dt(r,X)=row period deviation axis function for image X, where “
r”
denotes a particular row.
4 Assignments
0 Petitions
Accused Products
Abstract
An automatic digital image characterization system has a feature extractor, including a segment processor and a feature processor. The segment processor is connected for receiving an image in the form of digitized pixel values; each pixel value having an amplitude and being associated with positional information in the form of column and row values. The feature processor converts the image information into column and row axis functions having calculated values of statistical mean amplitude and standard deviation. A system processor registers images, senses image changes, locates objects and detects hidden information.
-
Citations
5 Claims
-
1. A method of characterizing an M-column by N-row array of image pixels, said method comprising:
-
(1) reading the amplitudes of said pixels; (2) assigning column and row indices to said amplitudes; (3) organizing said amplitudes into segment amplitude sets for M columns and N rows; (4) organizing segment period sets for M columns and N rows (5) calculating mean value sets for said segment amplitude sets; (6) calculating standard deviation sets for said mean value sets; (7) using said mean value sets and said standard deviation sets to characterize said array of image pixels, and (8) determining registration errors through calculation of difference coefficients d(c) and d(r) defined by the following equations;
D(c)=[Ma(c,A)−
Ma(c,B)][Da(c,A)−
Da(c,B)][Mt(c,A)−
Mt(c,B)][Dt(c,A)−
Dt(c,B)]
D(r)=[Ma(r,A)−
Ma(r,B)][Da(r,A)−
Da(r,B)][Mt(r,A)−
Mt(r,B)][Dt(r,A)−
Dt(r,B)]where;
Ma(c,X)=column amplitude mean axis function for image X, where “
c”
denotes a particular columnDa(c,X)=column amplitude deviation axis function for image X, where “
c”
denotes a particular columnMt(c,X)=column period mean axis function for image X, where “
c”
denotes a particular columnDt(c,X)=column period deviation axis function for image X, where “
c”
denotes a particular columnMa(r,X)=row amplitude mean axis function for image X, where “
r”
denotes a particular row,Da(r,X)=row amplitude deviation axis function for image X, where “
r”
denotes a particular row,Mt(r,X)=row period mean axis function for image X, where “
r”
denotes a particular row,Dt(r,X)=row period deviation axis function for image X, where “
r”
denotes a particular row. - View Dependent Claims (2, 3, 4, 5)
-
Specification
-
- Resources
Litigation Campaign Assessment
Thank you for your request. You will receive a custom alert email when the Litigation Campaign Assessment is available.
×
-
Current AssigneeCACI MTL Systems, Inc. (CACI International Incorporated)
-
Original AssigneeCACI MTL Systems, Inc. (CACI International Incorporated)
-
InventorsHill, Richard K., Hirsch, Herbert L.
-
Primary Examiner(s)Wu; Jingge
-
Assistant Examiner(s)KUHN, JORDAN M
-
Application NumberUS10/193,837Publication NumberTime in Patent Office1,803 DaysField of Search382/305, 382/181US Class Current382/181CPC Class CodesG06V 10/42 Global feature extraction b...
