POST-MANUFACTURE CAMERA CALIBRATION

• US 20160104284A1
• Filed: 10/10/2014
• Published: 04/14/2016
• Est. Priority Date: 10/10/2014
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Maguire, Yael, Shen, Shizhe
• Granted Patent: US 9,892,514 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06T 7/80 Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
• CPC Class Codes: H04N 1/00 Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
• CPC Class Codes: H04N 1/00172 directly from a still digital camera or from a storage medium mounted in a still digital camera H04N1/0018 takes precedence
• CPC Class Codes: H04N 17/002 for television cameras
• CPC Class Codes: H04N 23/57 Mechanical or electrical details of cameras or camera modules specially adapted for being embedded in other devices
• CPC Class Codes: H04N 23/661 Transmitting camera control signals through networks, e.g. control via the Internet

Abstract

Some embodiments include a method of operating a calibration server for a camera module. The method can include: receiving, by the computing server, a first training image taken by the camera module in a mobile device and a corresponding image-context attribute from the mobile device; aggregating, by the computing device, the first training image into a set of contextually similar images based on the image-context attribute; computing a calibration parameter model based on the set of contextually similar images utilizing dimension reduction statistical analysis; and scheduling to update the calibration parameter model to configure an image processor to adjust a raw photograph of the camera module according to the calibration parameter model.

Claims

1. A computer-implemented method comprising:

receiving a first training image taken by a camera module of a mobile device and a corresponding image-context attribute from the mobile device;

aggregating the first training image into a set of contextually similar images based on the image-context attribute;

computing a calibration parameter model based on the set of contextually similar images utilizing dimension reduction statistical analysis; and

causing an image processor to calibrate a raw photograph of the camera module by providing the calibration parameter model to the image processor.

2. The computer-implemented method of claim 1, further comprising providing a current version of the calibration parameter model to the image processor for deployment;

and wherein the image processor is part of the mobile device; and

wherein the image processor is configured to process raw photographs taken by the camera module prior to saving or displaying the raw photographs.

3. The computer-implemented method of claim 1, wherein the image processor is part of a computer server that performs said computing of the calibration parameter model;

wherein the computing server is configured as an online storage of raw photographs taken by the camera module of the mobile device; and

wherein the image processor is configured to adjust automatically the raw photographs in the online storage and subsequently uploaded photographs according to the calibration parameter model.

4. The computer-implemented method of claim 1, wherein the image processor is part of an external server separate from the computer server and the mobile device;

and further comprising;

receiving, via the external server, the raw photograph taken by the camera module from the mobile device; and

processing, via the external server, the raw photograph based on the calibration parameter model to present in a network accessible interface.

5. The computer-implemented method of claim 1, further comprising determining whether a training set of digital images including the set provides a sufficient amount of data samples to compute the calibration parameter model utilizing the dimension reduction statistical analysis;

and wherein computing the calibration parameter model is in response to determining that the training set does provide the sufficient amount.

6. The computer-implemented method of claim 1, wherein computing the calibration parameter model is in response to receiving the first training image.

7. The computer-implemented method of claim 1, wherein computing the calibration parameter model is according to a preset schedule.

8. The computer-implemented method of claim 1, further comprising:

adjusting a test image based on a current version of the calibration parameter model to determine whether the calibration parameter model has improved in accuracy over a previous version of the calibration parameter; and

wherein providing the calibration parameter model for the image processor is in response to determining that the current version has improved in accuracy.

9. The computer-implemented method of claim 1, wherein said receiving the first training image, said aggregating the first training image, said computing the calibration parameter model, and said causing the image processor to calibrate are performed by the mobile device.

10. The computer-implemented method of claim 1, wherein the first training image is a low spatial frequency image.

11-20. -20. (canceled)

21. A computer-readable data storage medium storing computer-executable instructions that, when executed, cause a computer system to perform a computer-implemented method, the computer-executable instructions comprising:

instructions for receiving a first training image taken by a camera module of a mobile device and a corresponding image-context attribute from the mobile device;

instructions for aggregating the first training image into a set of contextually similar images based on the image-context attribute;

instructions for computing a calibration parameter model based on the set of contextually similar images utilizing dimension reduction statistical analysis; and

instructions for causing an image processor to calibrate a raw photograph of the camera module by providing the calibration parameter model to the image processor.

22. The computer-readable data storage medium of claim 21, wherein the computer-executable instructions further comprises instructions for providing a current version of the calibration parameter model to the image processor for deployment;

and wherein the image processor is part of the mobile device; and

wherein the image processor is configured to process raw photographs taken by the camera module prior to saving or displaying the raw photographs.

23. The computer-readable data storage medium of claim 21, wherein the image processor is part of a computer server that performs said computing of the calibration parameter model;

wherein the computing server is configured as an online storage of raw photographs taken by the camera module of the mobile device; and

wherein the image processor is configured to adjust automatically the raw photographs in the online storage and subsequently uploaded photographs according to the calibration parameter model.

24. The computer-readable data storage medium of claim 21, wherein the image processor is part of an external server separate from the computer server and the mobile device;

and wherein the computer-executable instructions further comprises;

instructions for receiving, via the external server, the raw photograph taken by the camera module from the mobile device; and

instructions for processing, via the external server, the raw photograph based on the calibration parameter model to present in a network accessible interface.

25. The computer-readable data storage medium of claim 21, wherein the computer-executable instructions further comprises instructions for determining whether a training set of digital images including the set provides a sufficient amount of data samples to compute the calibration parameter model utilizing the dimension reduction statistical analysis;

and wherein computing the calibration parameter model is in response to determining that the training set does provide the sufficient amount.

26. The computer-readable data storage medium of claim 21, wherein computing the calibration parameter model is in response to receiving the first training image.

27. The computer-readable data storage medium of claim 21, wherein computing the calibration parameter model is according to a preset schedule.

28. The computer-readable data storage medium of claim 21, wherein the computer-executable instructions further comprises:

instructions for adjusting a test image based on a current version of the calibration parameter model to determine whether the calibration parameter model has improved in accuracy over a previous version of the calibration parameter; and

wherein providing the calibration parameter model for the image processor is in response to determining that the current version has improved in accuracy.

29. The computer-readable data storage medium of claim 21, wherein said receiving the first training image, said aggregating the first training image, said computing the calibration parameter model, and said causing the image processor to calibrate are performed by the mobile device.

30. A computer system, comprising:

a non-transitory memory device configured to store executable instructions;

a processor configured by the executable instructions to;

receive a first training image taken by a camera module of a mobile device and a corresponding image-context attribute from the mobile device;

aggregate the first training image into a set of contextually similar images based on the image-context attribute;

compute a calibration parameter model based on the set of contextually similar images utilizing dimension reduction statistical analysis; and

cause an image processor to calibrate a raw photograph of the camera module by providing the calibration parameter model to the image processor.