Front-end architecture for image processing
Systems and methods for image processing may perform one or more operations including, but not limited to: receiving raw image data from at least one imaging device; computing at least one image depth distance from the raw image data; computing one or more image validity flags from the raw image data; generating at least one data validity mask from the one or more image validity flags; determining a background imagery estimation from at least one image depth distance; generating at least one foreground mask from the background imagery estimation and the at least one image depth distance; generating at least one region-of-interest mask from the data validity mask and the foreground mask; and generating filtered raw image data from the raw image data and at least one region of interest mask.
1. A method for image processing comprising:
receiving raw image data from at least one imaging device;
computing at least one image depth distance from the raw image data;
computing one or more image validity flags from the raw image data;
generating at least one data validity mask from the one or more image validity flags;
determining a background imagery estimation from the at least one image depth distance;
generating at least one foreground mask from the background imagery estimation and the at least one image depth distance;
generating at least one region-of-interest mask from the at least one data validity mask and the at least one foreground mask using prioritized rules that indicate conditions for assigning values to elements of the at least one region-of-interest mask based on corresponding values of elements of the at least one data validity mask and corresponding values of elements of the at least one foreground mask; and
generating filtered raw image data from the raw image data and the at least one region of interest mask.
2. The method of claim 1 , wherein the at least one imaging device includes:
at least one time-of-flight camera.
3. The method of claim 1 , wherein computing one or more image validity flags from the raw image data includes:
comparing at least one pixel attribute to at least one threshold value.
4. The method of claim 1 , wherein generating at least one data validity mask from the one or more image validity flags includes:
correlating at least one image validity flag to at least one element of the data validity mask.
5. The method of claim 4 , wherein generating at least one data validity mask from the one or more image validity flags further includes:
removing one or more connected sub-regions of the at least one data validity mask.
6. The method of claim 4 , wherein generating at least one data validity mask from the one or more image validity flags further includes:
filling one or more connected inverted sub-regions of the at least one data validity mask.
7. The method of claim 1 , wherein generating at least one region-of-interest mask from the data validity mask and the at least one foreground mask includes:
segmenting one or more pixels in an image into one or more pixel clusters.
8. The method of claim 7 , wherein segmenting one or more pixels in an image into one or more pixel clusters includes:
segmenting one or more pixels in an image into one or more pixel clusters according to a k-medoids algorithm.
9. The method of claim 1 , further comprising:
computing at least one image amplitude from the raw image data;
computing at least one filtered image depth distance;
generating a filtered image from the at least one image amplitude, the at least one filtered image depth distance, the background imagery estimation and the at least one region-of-interest mask.
10. A system for image processing comprising:
a memory including computer readable instructions; and
at least one processing device to execute the computer readable instructions to,
receive raw image data from at least one imaging device;
compute at least one image depth distance from the raw image data;
compute one or more image validity flags from the raw image data;
generate at least one data validity mask from the one or more image validity flags;
determine a background imagery estimation from the at least one image depth distance;
generate at least one foreground mask from the background imagery estimation and the at least one image depth distance;
generate at least one region-of-interest mask from the at least one data validity mask and the at least one foreground mask using prioritized rules that indicate conditions for assigning values to elements of the at least one region-of-interest mask based on corresponding values of elements of the at least one data validity mask and corresponding values of elements of the at least one foreground mask; and
generate filtered raw image data from the raw image data and the at least one region of interest mask.
11. The system of claim 10 , wherein the at least one imaging device includes:
at least one time-of-flight camera.
12. The system of claim 10 , wherein the at least one processing device computes the one or more image validity flags from the raw image data by comparing at least one pixel attribute to at least one threshold value.
13. The system of claim 10 , wherein the at least one processing device generates the at least one data validity mask from the one or more image validity flags by correlating at least one image validity flag to at least one element of the at least one data validity mask.
14. The system of claim 13 , wherein the at least one processing device generates the at least one data validity mask from the one or more image validity flags by removing one or more connected sub-regions of the at least one data validity mask.
15. The system of claim 13 , wherein at least one processing device generates the at least one data validity mask from the one or more image validity flags by filling one or more connected inverted sub-regions of the at least one data validity mask.
16. The system of claim 10 , wherein the at least one processing device generates the at least one region-of-interest mask from the data validity mask and the at least one foreground mask by segmenting one or more pixels in an image into one or more pixel clusters.
17. The system of claim 16 , wherein the at least one processing device segments the one or more pixels in an image into one or more pixel clusters according to a k-medoids algorithm.
18. The system of claim 10 , wherein the at least one processing device executes the computer readable instructions to:
compute at least one image amplitude from the raw image data;
compute at least one filtered image depth distance;
generate a filtered image from the at least one image amplitude, the at least one filtered image depth distance, the background imagery estimation and the at least one region-of-interest mask.
19. A non-transitory computer readable medium including one or more processing device-executable instructions for:
receiving raw image data from at least one imaging device;
computing at least one image depth distance from the raw image data;
computing one or more image validity flags from the raw image data;
generating at least one data validity mask from the one or more image validity flags;
determining a background imagery estimation from the at least one image depth distance;
generating at least one foreground mask from the background imagery estimation and the at least one image depth distance;
generating at least one region-of-interest mask from the at least one data validity mask and the at least one foreground mask using prioritized rules that indicate conditions for assigning values to elements of the at least one region-of-interest mask based on corresponding values of elements of the at least one data validity mask and corresponding values of elements of the at least one foreground mask; and
generating filtered raw image data from the raw image data and the at least one region of interest mask.
20. A system for image processing comprising:
an imaging device configured for generating raw image data;
a memory including computer readable instructions; and
a processing device to execute the computer readable instructions to:
receive the raw image data from at least one imaging device;
compute at least one image depth distance from the raw image data;
compute one or more image validity flags from the raw image data;
generate at least one data validity mask from the one or more image validity flags;
determine a background imagery estimation from the at least one image depth distance;
generate at least one foreground mask from the background imagery estimation and the at least one image depth distance;
generate at least one region-of-interest mask from the at least one data validity mask and the at least one foreground mask using prioritized rules that indicate conditions for assigning values to elements of the at least one region-of-interest mask based on corresponding values of elements of the at least one data validity mask and corresponding values of elements of the at least one foreground mask; and
generate filtered raw image data from the raw image data and the at least one region of interest mask.