Techniques for navigation among multiple images

• US 9,046,996 B2
• Filed: 10/17/2013
• Issued: 06/02/2015
• Est. Priority Date: 10/17/2013
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Gallup, David, Chen, Liyong, Zhou, Shuchang, Seitz, Steven Maxwell
• Primary Examiner(s): Chu, David H
• Application Number:
  US14/056,527
• Publication Number:
  US 20150113474A1
• Time in Patent Office: 593 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 3/04845 for image manipulation, e.g. dragging, rotation, expansion or change of colour
• CPC Class Codes: G06F 3/0485 Scrolling or panning

Abstract

Aspects of the disclosure relate generally to providing a user with an image navigation experience. In order to do so, a reference image may be identified. A set of potential target images for the reference image may also be identified. A drag vector for user input relative to the reference image is determined. For particular image of the set of target images an associated cost is determined based at least in part on a cost function and the drag vector. A target image is selected based on the determined associated costs.

Claims

1. A computer-implemented method comprising:

identifying, by one or more computing devices, a reference image;

identifying, by the one or more computing devices, a set of potential target images for the reference image;

determining, by the one or more computing devices, a drag vector for user input relative to the reference image;

after the drag vector is determined, for each particular potential target image of the set of potential target images, determining, by the one or more computing devices, an associated cost based at least in part on a cost function and the drag vector; and

selecting, by the one or more computing devices, a target image from the set of potential target images based on the determined associated costs.

2. The method of claim 1, further comprising providing the selected target image for display to a user.

3. The method of claim 1, wherein the cost function is determined as a weighted sum of one or more cost terms.

4. The method of claim 1, wherein selecting the target image includes selecting the particular potential target image of the set of potential target images having a lowest-valued associated cost.

5. The method of claim 1, wherein:

determining the associated cost for each particular potential target image of the set of potential target images includes determining a drag flow cost term between the reference image and that particular potential target image; and

the drag flow cost term is configured to be minimized when an observed flow of points of the reference image to that particular potential target image is equal to the drag vector.

6. The method of claim 1, wherein:

determining the associated cost for each particular target image of the set of potential target images includes determining a rotation cost term between the reference image and that particular potential target image; and

the rotation cost term is configured to be minimized when a rotation component of an observed flow of points of the reference image to that particular potential target image is equal to the drag vector.

7. The method of claim 1, wherein:

determining the associated cost for each particular potential target image of the set of potential target images includes determining a rotation cost term between the reference image and that particular potential target image; and

the rotation cost term is configured to be minimized when a rotation component of an observed flow of points of the reference image to that particular potential target image is opposite of the drag vector.

8. The method of claim 1, wherein:

determining the associated cost for each particular potential target image of the set of potential target images includes determining a rotation cost term between the reference image and that particular potential target image; and

the rotation cost term is configured to be minimized when a rotation component of an observed flow of points of the reference image to that particular potential target image is zero.

9. The method of claim 1, wherein:

determining the associated cost for each particular potential target image of the set of potential target images includes determining a translation cost term between the reference image and that particular potential target image; and

the translation cost term is configured to be minimized when a translation component of an observed flow of points of the reference image to that particular potential target image is equal to the drag vector.

10. The method of claim 1, wherein:

determining the associated cost for each particular potential target image of the set of potential target images includes determining a translation cost term between the reference image and that particular potential target image; and

the drag flow cost term is configured to be minimized when a translation component of an observed flow of points of the reference image to that particular potential target image is opposite of the drag vector.

11. The method of claim 1, wherein:

determining associated cost for each particular potential target image of the set of potential target images includes determining a translation cost term between the reference image and that particular potential target image; and

the drag flow cost term is configured to be minimized when a translation component of an observed flow of points of the reference image to that particular potential target image is zero.

12. The method of claim 1, wherein determining the associated cost for each particular potential target image of the set of target images includes determining an overlap cost term based on an amount of overlap of points between the reference image and that particular potential target image.

13. The method of claim 1, wherein:

determining the associated cost further includes determining weights for one or more terms of the cost function based on a particular motion result; and

the motion result is selected from the group consisting of a panning motion, a sliding motion, and an orbiting motion.

14. The method of claim 1, further comprising:

receiving, from a client computing device, a request for a target image, the request identifying the drag vector and the reference image;

retrieving the selected target image based on the association with the drag vector and the reference image; and

providing the selected target image to the client computing device.

15. A system comprising one or more computing devices configured to:

identify a reference image;

identify a set of potential target images for the reference image;

determine a drag vector for user input relative to the reference image;

after the drag vector is determined, for each particular potential target image of the set of potential target images, determine an associated cost based at least in part on a cost function and the drag vector; and

select a target image based on the determined associated costs.

16. The system of claim 15, wherein the one or more computing devices are configured to determine the cost functions as weighted sum of one or more cost terms.

17. The system of claim 15, wherein the one or more computing devices are further configured to select the target image by selecting the particular potential target image of the set of potential target images having a lowest-valued associated cost.

18. The system of claim 15, wherein the one or more computing devices are configured to provide the selected target image for display to a user.

19. The system of claim 15, wherein the one or more computing devices are configured to:

receive, from a client computing device, a request for a target image, the request identifying the drag vector and the reference image;

retrieve the selected target image based on the association with the drag vector and the reference image; and

provide the selected target image to the client computing device.

20. A non-transitory, computer-readable storage device on which computer readable instructions of a program are stored, the instructions, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising:

identifying a reference image;

identifying a set of potential target images for the reference image;

determining a drag vector for user input relative to the reference image;

after the drag vector is determined, for each particular potential target image of the set of potential target images, determining, by the one or more computing devices, an associated cost based at least in part on a cost function and the drag vector; and

selecting a target image from the set of potential target images based on the determined associated costs.