Source: http://www.google.com/patents/US8239220?dq=5822523
Timestamp: 2016-10-27 03:29:42
Document Index: 250085776

Matched Legal Cases: ['application No. 2007258684', 'application No. 2', 'application No. 2', 'application No. 07809319', 'application No. 2007800212253', 'application No. 200780021225']

Patent US8239220 - Method and apparatus for obtaining photogrammetric data to estimate impact ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsIn one embodiment, the present invention includes a computer-implemented method to collect information to determine damage to a vehicle involved in a collision using photogrammetric techniques. When determined, this vehicle damage information, which may be in the form of crush measurement information...http://www.google.com/patents/US8239220?utm_source=gb-gplus-sharePatent US8239220 - Method and apparatus for obtaining photogrammetric data to estimate impact severityAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS8239220 B2Publication typeGrantApplication numberUS 11/519,560Publication dateAug 7, 2012Filing dateSep 12, 2006Priority dateJun 8, 2006Fee statusPaidAlso published asCA2653385A1, CN101467153A, CN101467153B, EP2030139A2, EP2030139A4, US9228834, US20070288135, US20120265698, WO2007145931A2, WO2007145931A3Publication number11519560, 519560, US 8239220 B2, US 8239220B2, US-B2-8239220, US8239220 B2, US8239220B2InventorsScott D. Kidd, Darrin A. SmithOriginal AssigneeInjury Sciences LlcExport CitationBiBTeX, EndNote, RefManPatent Citations (34), Non-Patent Citations (15), Referenced by (11), Classifications (8), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetMethod and apparatus for obtaining photogrammetric data to estimate impact severity
P c =RP o +T o where R is a 3�3 rotation matrix and To is the position of the object reference frame with respect to the camera. This can also be written as:
P I = K [ r 1 r 2 r 3 t ] { P Ox P Oy 0 1 } = K [ r 1 r 2 t ] { P Ox P Oy 1 } or P I = H { P Ox P Oy 1 } Here, the 3�3 matrix, H, is called a planar homography and it maps points from the calibration plane to their corresponding image coordinates. Given an image of the calibration pattern, this homography can be estimated.
{right arrow over (ω)}�=[ω11 ω12 ω22 ω13 ω23 ω33]T Using the estimated homography of each calibration image and the constraints on the calibration matrix, a set of linear equations can be written in {right arrow over (ω)}, i.e.
[ v 12 T ( v 11 - v 22 ) T ] ω -> = V ω -> = 0 -> For n images, the matrix V is a 2n�6 matrix. This system can be solved if there are at least 3 images of the calibration plane.
min R , t ∑ i = 1 n [ ( u ^ i - r 1 T p i + t x r 3 T p i + t z ) 2 + ( v ^ i - r 2 T p i + t y r 3 T p i + t z ) 2 ] Here, (�i, {circumflex over (v)}i) are the coordinates of the control point observed in the normalized image plane.
M ( R ) = q ′ ( R ) � p ′ = ∑ i = 1 n q i ′ ( R ) p i ′ T where p′i=pi− p and q′1(R)=qi(R)− q(R)
( x c y c ) = ∑ i = 1 n ∑ j = 1 m g ij ( x i y j ) ∑ i = 1 n ∑ j = 1 m g ij where xi, yi are the pixel coordinates and gij are the intensity levels within a (n�m) window covering the target region.
where K is the 3�3 camera calibration matrix, R is the 3�3 rotation matrix from object space to camera space, and C is the position of the camera with respect to the object.
x�(PX)=0
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