Image cropping

1. A method of executing computer instructions on at least one computing device without further human interaction in which the at least one computing device includes at least one processor and at least one memory, comprising:

fetching computer instructions from the at least one memory of the at least one computing device for execution on the at least one processor of the at least one computing device;

executing the fetched computer instructions on the at least one processor of the at least one computing device; and

storing in the at least one memory of the at least one computing device any results of having executed the fetched computer instructions on the at least one processor of the at least one computing device;

wherein the computer instructions to be executed comprise instructions for determining a cropping window for a region of an image;

wherein the executing the fetched instructions further comprises:

generating a composite saliency map from a plurality of pre-composite saliency maps, including one or more saliency maps to electronically model human eye-attention via at least randomly thresholding one or more of a plurality of color channels of the image, for the region of the image; and

determining the cropping window for the region of the image based, at least in part, on the composite saliency map, and based, at least in part, on an aspect ratio of the image and a width of the cropping window relative to a width of the image.

2. The method of claim 1 , wherein the image comprises a digital image.

3. The method of claim 1 , wherein the generating the composite saliency map comprises generating the plurality of pre-composite saliency maps, including the one or more saliency maps to electronically model human eye-attention.

4. The method of claim 3 , wherein the plurality of pre-composite saliency maps comprise the one or more saliency maps to electronically model human eye-attention, one or more face-type saliency maps, one or more position-type saliency maps, or any combination thereof.

5. The method of claim 1 , further comprising determining the region of the image, wherein the region comprises a contiguous portion of the image.

6. The method of claim 1 , further comprising determining the region of the image, wherein the region comprises non-contiguous portions of the image.

7. The method of claim 1 , further comprising:

detecting one or more separating boundaries in the image; and

determining the region based, at least in part, on the one or more detected separating boundaries.

8. The method of claim 7 , wherein the determining the region comprises identifying a candidate region having an aspect ratio and having a width and/or height related to the aspect ratio, and further comprises determining whether the width and/or height of the candidate region exceeds a candidate region threshold.

9. The method of claim 8 , wherein the candidate region threshold is specified at least in part based on a width and/or height of the image.

10. The method of claim 1 , wherein the image comprises an original image.

11. The method of claim 8 , wherein the separating boundaries indicate one or more borders between two or more regions, and wherein the determining the region further comprises selecting the region from the two or more regions at least in part in response to a determination that the width and/or height of the candidate region does not exceed the candidate region threshold.

12. The method of claim 8 , further comprising:

wherein the determining the region comprises selecting the candidate region as the region at least in part in response to a determination that the width and/or height of the candidate region exceeds the candidate region threshold.

13. The method of claim 12 , wherein the candidate region threshold comprises a user specified threshold, a programmable threshold, or an adaptive threshold, or any combination thereof.

14. An apparatus, comprising:

at least one computing device;

the at least one computing device to include at least one processor and at least one memory;

the at least one computing device to execute computer instructions on the at least one processor without further human intervention;

the computer instructions to be executed to have been fetched from the at least one memory for execution on the at least one processor, and the at least one computing device to store in the at least one memory of the at least one computing device any results to be generated from the execution on the at least one processor of the to be executed computer instructions;

the computer instructions to be executed to comprise instructions to determine a cropping window for a region of an image;

wherein the instructions to be executed to:

generate a composite saliency map from a plurality of pre-composite saliency maps to include one or more a saliency maps to electronically model human eye-attention for the region of the image, wherein to electronically model human eye-attention, at least randomly threshold one or more of a plurality of color channels of the image; and

determine the cropping window for the region of the image based, at least in part, on the composite saliency map, and based, at least in part, on an aspect ratio of the image and a width of the cropping window relative to a width of the image.

15. The apparatus of claim 14 , wherein the instructions to be executed further to compute the plurality of pre-composite saliency maps, to include the one or more saliency maps to electronically model human eye-attention, to compute the composite saliency map.

16. The apparatus of claim 15 , wherein the plurality of pre-composite saliency maps to comprise the one or more saliency maps to model human eye-attention, one or more face-type saliency maps, one or more position-type saliency maps, or any combination thereof.

17. The apparatus of claim 14 , wherein the at least one processor further to determine the region of the image, wherein the region to comprise a contiguous portion of the image.

18. The apparatus of claim 14 , the processor further to determine the region of the image, wherein the region comprises a non-contiguous portion of the image.

19. The apparatus of claim 14 , wherein the at least one processor to detect one or more separating boundaries in the image and to determine the region of the image based, at least in part, on the one or more separating boundaries to be detected.

20. The apparatus of claim 14 , wherein the at least one processor to identify a candidate region to have an aspect ratio and to have a width and/or height to be related to the aspect ratio, and wherein the at least one processor further to determine whether the width and/or height of the candidate region exceeds a candidate region threshold.

21. An apparatus, comprising:

means for executing computer instructions on at least one computing device without further human interaction in which the at least one computing device includes at least one processor and at least one memory, comprising:

means for fetching computer instructions from the at least one memory of the at least one computing device for execution on the at least one processor of the at least one computing device;

means for executing the fetched computer instructions on the at least one processor of the at least one computing device; and

means for storing in the at least one memory of the at least one computing device any results of having executed the fetched computer instructions on the at least one processor of the at least one computing device;

wherein the computer instructions comprise instructions for determining a cropping window for a region of an image;

wherein the means for executing the fetched instructions further comprises:

means for generating a composite saliency map from a plurality of pre-composite saliency maps, including one or more a saliency maps to electronically model human eye-attention via at least randomly thresholding one or more of a plurality of color channels of the image, for the region of the image; and

means for determining the cropping window for the region of the image based, at least in part, on the composite saliency map, and based, at least in part, on an aspect ratio of the image and a width of the cropping window relative to the width of the image.

22. The apparatus of claim 21 , wherein the plurality of pre-composite saliency maps comprise the one or more saliency maps to electronically model human eye-attention, one or more face-type saliency maps, one or more position-type saliency maps, or any combination thereof.