Methods and systems for automatically generating backdrop imagery for a graphical user interface

Patent metadata

Field	Value
Publication number	US-12198353-B2
Application number	US-202418416485-A
Country	US
Kind code	B2
Filing date	Jan 18, 2024
Priority date	Aug 31, 2021
Publication date	Jan 14, 2025
Grant date	Jan 14, 2025

Abstract

In one aspect, an example method for generating a candidate image for use as backdrop imagery for a graphical user interface is disclosed. The method includes receiving a raw image and determining an edge image from the raw image using edge detection. The method also includes identifying a candidate region of interest (ROI) in the raw image based on the candidate ROI enclosing a portion of the edge image having edge densities exceeding a threshold edge density. The method also includes manipulating the raw image relative to a backdrop imagery canvas for a graphical user interface based on a location of the candidate ROI within the raw image. The method also includes generating, based on the manipulating, a set of candidate backdrop images in which at least a portion of the candidate ROI occupies a preselected area of the backdrop imagery canvas, and storing the set of candidate backdrop images.

First claim

The invention claimed is:

1. A method for generating a candidate image for use as backdrop imagery for a graphical user interface, the method comprising:
receiving, by a computing system, a raw image;
identifying, by the computing system, a location of at least one face in the raw image;
identifying, by the computing system, a candidate region of interest (ROI) in the raw image based on the location of the at least one face in the raw image;
manipulating, by the computing system, the raw image relative to a backdrop imagery canvas for a graphical user interface based on a location of the candidate ROI within the raw image;
generating, by the computing system and based on the manipulating, a set of candidate backdrop images in which at least a portion of the candidate ROI occupies a preselected area of the backdrop imagery canvas; and
storing, by the computing system, in non-transitory computer-readable memory, the set of candidate backdrop images.

2. The method of claim 1 , wherein identifying, by the computing system, the location of the at least one face in the raw image comprises:
applying a machine learning predictor program to the raw image to recognize the at least one face; and
determining coordinates in the raw image of a bounding box that includes the at least one face.

3. The method of claim 2 , wherein the at least one face is two or more faces,
and wherein determining the coordinates in the raw image of the bounding box that includes the at least one face comprises:
determining respective bounding boxes for the two or more faces; and
determining the bounding box as a union of the respective bounding boxes.

4. The method of claim 1 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image further comprises manipulating the raw image relative to the backdrop imagery canvas further based on a vertical midline of the backdrop imagery canvas.

5. The method of claim 4 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image further comprises scaling the raw image.

6. The method of claim 4 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image further comprises cropping the raw image.

7. The method of claim 1 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image comprises performing a manipulation action to cause the at least a portion of the candidate ROI to occupy the preselected area of the backdrop imagery canvas,
wherein the manipulation action is at least one of shifting the raw image, cropping the raw image, or scaling the raw image.

8. A method for generating a candidate image for use as backdrop imagery for a graphical user interface, the method comprising:
receiving, by a computing system, a raw image;
using, by the computing system, a machine learning region of interest (ROI) program to identify a candidate ROI in the raw image, wherein the machine learning ROI program is configured to predict ROI characteristics for the raw image based on expected ROI characteristics represented in a set of training raw images;
manipulating, by the computing system, the raw image relative to a backdrop imagery canvas based on a location of the candidate ROI within the raw image;
generating, by the computing system and based on the manipulating, a set of candidate backdrop images in which at least a portion of the candidate ROI occupies a preselected area of the backdrop imagery canvas; and
storing, by the computing system, in non-transitory computer-readable memory, the set of candidate backdrop images.

9. The method of claim 8 , wherein the ROI characteristics for the raw image comprise cropping characteristics, wherein cropping characteristics are at least one of cropping boundaries, or image size.

10. The method of claim 8 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image further comprises manipulating the raw image relative to the backdrop imagery canvas further based on a vertical midline of the backdrop imagery canvas.

11. The method of claim 10 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image further comprises scaling the raw image.

12. The method of claim 10 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image further comprises cropping the raw image.

13. The method of claim 8 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image comprises performing a manipulation action to cause the at least a portion of the candidate ROI to occupy the preselected area of the backdrop imagery canvas,
wherein the manipulation action is at least one of shifting the raw image, cropping the raw image, or scaling the raw image.

14. A computing system comprising:
a processor; and
a non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by the processor, cause the computing system to perform a set of operations comprising:
receiving a raw image;
identifying a location of at least one face in the raw image;
identifying a candidate region of interest (ROI) in the raw image based on the location of the at least one face in the raw image;
manipulating the raw image relative to a backdrop imagery canvas for a graphical user interface based on a location of the candidate ROI within the raw image;
generating, based on the manipulating, a set of candidate backdrop images in which at least a portion of the candidate ROI occupies a preselected area of the backdrop imagery canvas; and
storing in non-transitory computer-readable memory, the set of candidate backdrop images.

15. The computing system of claim 14 , wherein identifying the location of the at least one face in the raw image comprises:
applying a machine learning predictor program to the raw image to recognize the at least one face; and
determining coordinates in the raw image of a bounding box that includes the at least one face.

16. The computing system of claim 15 , wherein the at least one face is two or more faces,
and wherein determining the coordinates in the raw image of the bounding box that includes the at least one face comprises:
determining respective bounding boxes for the two or more faces; and
determining the bounding box as a union of the respective bounding boxes.

17. The computing system of claim 14 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image further comprises manipulating the raw image relative to the backdrop imagery canvas further based on a vertical midline of the backdrop imagery canvas.

18. The computing system of claim 17 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image further comprises scaling the raw image.

19. The computing system of claim 17 , wherein manipulating the raw image relative to the backdrop imagery canvas based on the location of the candidate ROI within the raw image further comprises croppi

Assignees

• Gracenote Inc

Inventors

• Vartakavi Aneesh
• Scott Jeffrey

Classifications

• G06V10/25

  Determination of region of interest [ROI] or a volume of interest [VOI] · CPC title

• G06T7/149

  involving deformable models, e.g. active contour models · CPC title

• G06T3/403

  Edge-driven scaling; Edge-based scaling · CPC title

• G06V40/161

  Detection; Localisation; Normalisation · CPC title

• G06V10/945

  User interactive design; Environments; Toolboxes · CPC title