System and method for estimating the epipolar geometry between images

Patent 6771810 Issued on August 3, 2004. Estimated Expiration Date:

June 16, 2020. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.

Abstract Claims Description Full Text

Patent References

Apparatus and method for recreating and manipulating a 3D object based on a 2D projection thereof
Patent #: 5821943
Issued on: 10/13/1998
Inventor: Shashua

Image processing apparatus
Patent #: 6516099
Issued on: 02/04/2003
Inventor: Davison, et al.

System and method for determining structure and motion from two-dimensional images for multi-resolution object modeling Patent #: 6614429
Issued on: 09/02/2003
Inventor: Zhang , et al.

Inventors

Assignee

Microsoft Corporation

Application

No. 09594806 filed on 06/16/2000

US Classes:

382/154, 3-D or stereo imaging analysis382/285, Mapping 2-D image onto a 3-D surface382/294, Registering or aligning multiple images to one another345/427, Space transformation345/473, Animation348/42STEREOSCOPIC

Examiners

Primary: Johnson, Timothy M.
Assistant: Bayat, Ali

Attorney, Agent or Firm

International Class

G06K 900

Abstract

The present invention is embodied in a system and method for estimating epipolar geometry, in terms of a fundamental matrix, between multiple images of an object for stereo vision processing. The fundamental matrix embodies the epipolar geometry between the images. In general, the present invention includes a method for estimating epipolar geometry between multiple images of an original space given an initial estimate of the fundamental matrix found using a standard linear estimation technique. Namely, the system and method of the present invention estimates the fundamental matrix by transforming image points of multiple images into projective space. After this transformation is performed, nonlinear optimization is used with one parameterization of the fundamental matrix. The images are then inverse transformed back to the original space with a final estimate of the fundamental matrix. In addition, the original noise information is preserved during the optimization in the projective space.

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