Fault-Aware Matched Filter and Optical Flow

Abstract

In one embodiment, a fault-aware matched filter augments the output of a component matched filter to provide both fault-aware matched filter output and a measure of confidence in the accuracy of the fault-aware matched filter output. In another embodiment, an optical flow engine derives, from a plurality of images, both optical flow output and a measure of confidence in the optical flow output. The measure of confidence may be derived using a fault-aware matched filter.

1 . A method performed by at least one computer processor, the method comprising:
 (A) receiving a first image input;   (B) receiving a second image input;   (C) receiving an image coregistration data set input defining a coregistration between the first image input and the second image input, wherein the image coregistration data set input includes a plurality of elements;   (D) at the at least one computer processor, applying a filter to each of the plurality of elements of the image coregistration data set input and at least one of the first image input and the second image input, comprising:
 (1) deriving a match statistic map (MSM), wherein the MSM is a two-dimensional measure of similarity between the first image input and the second image input; 
 (2) identifying a value PK 1  representing a peak similarity response in the MSM; and 
 (3) identifying a value PK 2  representing a value in the MSM other than the peak similarity response in the MSM; and 
   (E) at the at least one computer processor, generating, for each of the plurality of elements in the image coregistration data set input, a first corresponding confidence estimate metric based on the ratio PK 1 /PK 2 .   
     
     
         2 . The method of  claim 1 , further comprising:
 (F) generating, for each of the plurality of elements in the image coregistration data set input, a corresponding fault warning using at least one of the confidence estimate metrics generated in (E); and   (G) applying a filter to each element of the image coregistration data set and at least one of the first image input and the second image input, based on the fault warning, thereby removing samples which are in-fault.   
     
     
         3 . The method of  claim 1 , wherein the MSM comprises a plurality of similarity values, and wherein (3) comprises identifying the value PK 2  based on the plurality of similarity values in the MSM. 
     
     
         4 . The method of  claim 3 , wherein identifying the value PK 2  based on the plurality of similarity values comprises identifying the value PK 2  as an average of the plurality of similarity values. 
     
     
         5 . The method of  claim 3 , wherein identifying the value PK 2  based on the plurality of similarity values comprises identifying the value PK 2  as a standard deviation of the plurality of similarity values. 
     
     
         6 . The method of  claim 1 , further comprising:
 (F) at the at least one computer processor, generating a second confidence estimate metric BV, wherein the second confidence estimate metric BV comprises a Boolean value, wherein:
   BV=(index(PK1)>RAD) and (index(PK1)< N −RAD−1);
 
 the value index (PK 1 ) is an image coordinate corresponding to PK 1 ; 
 the value RAD is a distance from the border of one of the first image input and the second image input; and 
 the value N is a dimension of the first image input and the second image input. 
   
     
     
         7 . A non-transitory computer-readable medium comprising computer program instructions executable by at least one computer processor to perform a method, the method comprising:
 (A) receiving a first image input;   (B) receiving a second image input;   (C) receiving an image coregistration data set input defining a coregistration between the first image input and the second image input, wherein the image coregistration data set input includes a plurality of elements;   (D) at the at least one computer processor, applying a filter to each of the plurality of elements of the image coregistration data set input and at least one of the first image input and the second image input, comprising:
 (1) deriving a match statistic map (MSM), wherein the MSM is a two-dimensional measure of similarity between the first image input and the second image input; 
 (2) identifying a value PK 1  representing a peak similarity response in the MSM; and 
 (3) identifying a value PK 2  representing a value in the MSM other than the peak similarity response in the MSM; and 
   (E) at the at least one computer processor, generating, for each of the plurality of elements in the image coregistration data set input, a first corresponding confidence estimate metric based on the ratio PK 1 /PK 2 .   
     
     
         8 . The A non-transitory computer-readable medium of  claim 7 , wherein the method further comprises:
 (F) generating, for each of the plurality of elements in the image coregistration data set input, a corresponding fault warning using at least one of the confidence estimate metrics generated in (E); and   (G) applying a filter to each element of the image coregistration data set and at least one of the first image input and the second image input, based on the fault warning, thereby removing samples which are in-fault.   
     
     
         9 . The non-transitory computer-readable medium of  claim 7 , wherein the MSM comprises a plurality of similarity values, and wherein (3) comprises identifying the value PK 2  based on the plurality of similarity values in the MSM. 
     
     
         10 . The A non-transitory computer-readable medium of  claim 9 , wherein identifying the value PK 2  based on the plurality of similarity values comprises identifying the value PK 2  as an average of the plurality of similarity values. 
     
     
         11 . The A non-transitory computer-readable medium of  claim 9 , wherein identifying the value PK 2  based on the plurality of similarity values comprises identifying the value PK 2  as a standard deviation of the plurality of similarity values. 
     
     
         12 . The A non-transitory computer-readable medium of  claim 7 , wherein the method further comprises:
 (F) at the at least one computer processor, generating a second confidence estimate metric BV, wherein the second confidence estimate metric BV comprises a Boolean value, wherein:
   BV=(index(PK1)>RAD) and (index(PK1)< N −RAD−1);
 
 the value index (PK 1 ) is an image coordinate corresponding to PK 1 ; 
 the value RAD is a distance from the border of one of the first image input and the second image input; and 
 the value N is a dimension of the first image input and the second image input.