The present disclosure is directed to an automated optical inspection system for components using simultaneous multiple camera images to create a zoom factor for a narrow field of view camera. Particularly, the disclosure is directed to obtaining an accurate lens zoom factor for the narrow field of view camera by coupling another camera having a wider field of view to move simultaneously with the narrow field of view camera.
Gas turbine engine components, such as blades, vanes, disks, gears, and the like, may suffer irregularities during manufacture or wear and damage during operation, for example, due to erosion, hot corrosion (sulfidation), cracks, dents, nicks, gouges, and other damage, such as from foreign object damage. Detecting this damage may be achieved by images or videos for aircraft engine blade inspection, power turbine blade inspection, internal inspection of mechanical devices, and the like. A variety of techniques for inspecting by use of images or videos may include capturing and displaying images or videos to human inspectors for manual defect detection and interpretation. Human inspectors may then decide whether any defect exists within those images or videos. When human inspectors look at many similar images of very similar blades of an engine stage or like components of any device, they may not detect defects, for example, because of fatigue or distraction experienced by the inspector. Missing a defect may lead to customer dissatisfaction, transportation of an expensive engine back to service centers, lost revenue, or even engine failure. The damaged blades are currently inspected manually by visual inspection, see
In accordance with the present disclosure, there is provided a multi-camera system for component inspection comprising a rigid surface; a first camera having a narrow field-of-view lens; a second camera having a wide field-of-view lens linked to the first camera, wherein the first camera and the second camera are configured to move along a common axis relative to the rigid surface; and a pre-defined pattern defined on the rigid surface.
In another and alternative embodiment, the first camera comprises at least one first camera extrinsic parameter with respect to the pre-defined pattern.
In another and alternative embodiment, the second camera comprises at least one second camera extrinsic parameter with respect to the pre-defined pattern.
In another and alternative embodiment, each of the first camera extrinsic parameter and the second camera extrinsic parameter is determined responsive to at least one of an upward movement and a downward movement relative to the rigid surface.
In another and alternative embodiment, each of the first camera extrinsic parameter and the second camera extrinsic parameter are configured to obtain a lens zoom factor.
In another and alternative embodiment, the second camera is configured, upon a calibration, for image-to-model registration of a component being viewed.
In another and alternative embodiment, the image-to-model registration of the component being translated to the first camera via a precomputed extrinsic relationship between the first camera and the second camera.
In another and alternative embodiment, each of the first camera extrinsic parameter and the second camera extrinsic parameter comprises a vertical distance between a center of each of the first and second cameras and a plane of the pattern on the rigid surface at two respective locations within the pattern.
In another and alternative embodiment, the pre-defined image comprises a checkerboard pattern.
In another and alternative embodiment, at least one of the first camera and the second camera comprises a microscope camera.
In accordance with the present disclosure, there is provided a method of use for a multi-camera system, comprising viewing a pre-defined pattern mounted on a rigid surface with each of: a first camera having a narrow field-of-view lens and a second camera having a wide field-of-view lens coupled to the first camera; moving simultaneously the first camera and the second camera relative to the rigid surface; and calculating a lens zoom factor between the first camera and the second camera.
In another and alternative embodiment, the method of use for a multi-camera system further comprises calibrating the second camera.
In another and alternative embodiment, the method of use for a multi-camera system further comprises estimating at least one extrinsic parameter of the first camera with respect to the pre-defined pattern; and estimating at least one extrinsic parameter of the second camera with respect to the pre-defined pattern.
In another and alternative embodiment, each of the first camera extrinsic parameter and the second camera extrinsic parameter comprises a vertical distance between a center of each of the first and second cameras and a plane of the pattern on the rigid surface at two respective locations within the pattern.
In another and alternative embodiment, the step of creating a zoom factor between the first camera and the second camera utilizes each of the first camera extrinsic parameter and the second camera extrinsic parameter to obtain the lens zoom factor.
In another and alternative embodiment, the pre-defined image comprises a checkerboard pattern.
In another and alternative embodiment, the method of use for a multi-camera system further comprises creating an image-to-model registration of a component being viewed.
In another and alternative embodiment, the method of use for a multi-camera system further comprises translating the image-to-model registration of the part being viewed to the first camera via a precomputed extrinsic relationship between the first camera and the second camera.
In another and alternative embodiment, at least one of the first camera and the second camera comprises a microscope camera.
Other details of the multi-camera system for simultaneous registration and zoomed imaging are set forth in the following detailed description and the accompanying drawings wherein like reference numerals depict like elements.
Referring to
The imaging device 16 may be any measurement device capable of rendering 2D arrays of measurements and is explicitly contemplated as comprising a visible spectrum camera, an infrared camera, and the like.
In exemplary embodiments, the imaging device 16 can be a camera, and include a one-dimensional (1D) or 2D sensor and/or a combination and/or array thereof. Imaging device 16 may be operable in any single frequency or band of frequencies in the electromagnetic spectrum. Imaging device 16 may provide various characteristics of the sensed electromagnetic spectrum including intensity, spectral characteristics, polarization, etc.
In various embodiments, imaging device 16 may include an image capture device, such as an optical device having an optical lens, such as a camera, a microscope camera, mobile video camera, or other imaging device or image sensor, capable of capturing 2D still images or video images.
In various embodiments, imaging device 16 may include a line sensor, a linear image sensor, or other 1D sensor. Further, imaging device 16 may include a 2D sensor, and optical inspection system 10 may extract 1D information from the 2D sensor data or synthesize 2D information from the 1D sensor data. The extracting may be achieved by retaining only a subset of the data such as keeping only that data that is in focus. The synthesizing may be achieved by tiling or mosaicking the data. Even further, imaging device 16 may include a position and/or orientation sensor such as an inertial measurement unit (IMU) that may provide position and/or orientation information about component 26 with respect to a coordinate system or other imaging device 16. The position and/or orientation information may be beneficially employed in aligning 1D or 2D information to a reference model as discussed elsewhere herein.
The coordinate measuring machine 14 includes a base 20 coupled to the table 12. The base 20 supports an arm mechanism or simply arm 22 that can be articulated about 3 different axes to provide six degrees of freedom (6DOF). In an alternative embodiment, the base 20 is coupled to a surface that is itself coupled to, but not coplanar with, table 12. The arm 22 supports a fixture 24 configured to attach a component 26. The coordinate measuring machine 14 is a device for measuring the physical geometrical characteristics of an object. This machine may be manually controlled by an operator or it may be computer controlled. The CMM arm 22 can be calibrated using vendor-supplied techniques.
In an exemplary embodiment, the fixture 24 can be transparent allowing all surfaces of the component 26 to be viewed/imaged by the imaging device 16. In an exemplary embodiment, the fixture 24 is made of an optically transparent material. Such material preferably has high transparency and high toughness. One example material would be Polymethylmethacrylate (PMMA). The material need not be highly rigid providing it does not change the relative position or orientation of component 26 during use more than can be compensated by a registration process (described below). In an alternate embodiment fixture 24 may be made of metallic glass, Gorilla Glass™, sapphire, polycarbonate, and the like. The fixture 24 has known physical dimensions. The fixture 24 can include a shape that conforms to the shape of the component 26. In an exemplary embodiment, if the component 26 is a blade, the fixture 24 can have a convoluted shape that conforms to the fir tree of the root of the blade, such that the root of the blade is supported by the fixture 24. The component 26 can then be precisely located relative to fixture 24 which, in turn, is precisely located relative to the CMM 14 and, hence, to the table 12.
The component 26, such as a turbine engine blade, is coupled to the fixture 24 of the arm 22 of the CMM 14, at a location typically occupied by a probe (not shown). Measurements of the component 26 are defined by the component attached to the arm of the CMM 14. In one non-limiting embodiment, the CMM 14 provides data that reports the component 26 location and pose in all 6 degrees of freedom (6 DOF). In another non-limiting embodiment, CMM 14 provides data comprising one or more of three location measurements and 3 rotation measurements.
Referring also to
In various embodiments, processor 30 may receive or construct image information 32 corresponding to the component 26. Processor 30 may further include a reference model 34 stored, for example, in memory of processor 30. Reference model 34 may be generated from a CAD model, a 3D CAD model, and/or 3D information, such as from a 3D scan or 3D information of an original component or an undamaged component, and the like. In various alternative embodiments, reference model 22 may comprise 1D or 2D information from a projection of a 2D or 3D model, prior 1D or 2D information from sensors 16, and the like. Reference model 34 may be a theoretical model, may be based on historical information about component 26, may be based on current information about component 26, and the like. Reference model 34 may be adjusted and updated as component 26 and/or similar components are scanned and inspected. Thus, reference model 34 may be a learned model of a component and may include, for example, 3D information including shape and surface features of the component.
In various embodiments, processor 30 of optical inspection system 10 may classify the damage and determine the probability of damage and/or if the damage meets or exceeds a threshold 36. Threshold 36 may be an input parameter based on reference model 34, based on user input, based on data from sensor(s) 16, and the like. Processor 30 may provide an output 38 to a user interface 40 indicating the status of the component 26. User interface 40 may include a display. Optical inspection system 10 may display an indication of the defect to component 26, which may include an image and/or a report. In addition to reporting any defects in the component, output 38 may also relay information about the type of defect, the location of the defect, size of the defect, and the like. If defects are found in the inspected component 26, an indicator may be displayed on user interface 40 to alert personnel or users of the defect.
With reference to
A pre-defined pattern 50 can be defined on the table top 48. The pre-defined pattern 50 can be a checkerboard image or other dimensioned image. In an exemplary embodiment, the pre-defined pattern 50 can include a fiducial marker. A fiducial marker can be an object placed in the field of view of an imaging system which appears in the image produced, for use as a point of reference or a measure. Fiducial markers can be either something placed into or on the imaging subject, or a mark or set of marks in the reticle of an optical instrument.
The first camera 42 can include at least one first camera extrinsic parameter 52 with respect to the pre-defined pattern 50. The second camera 44 can include at least one second camera extrinsic parameter 54 with respect to the pre-defined pattern 50. Each of the first camera 42 and second camera 44 each can have intrinsic parameters that are internal to and fixed to a particular camera/imaging device setup. The first and second camera extrinsic parameters 52, 54 are camera parameters that are external to each camera and change with respect to the coordinates of the cameras 42, 44 with respect to the table top 48. The extrinsic parameters 52, 54 are utilized to define a location and orientation of the cameras 42, 44 with respect to the table top 48. The first camera extrinsic parameter 52 and the second camera extrinsic parameter 54 can be determined responsive to one or more of an upward movement and a downward movement of the cameras 42, 44 relative to the table top 48. In an exemplary embodiment each of the first camera extrinsic parameter 52 and the second camera extrinsic parameter 54 comprises a vertical distance d between a center 55 of each of the first and second cameras 42, 44 and a plane 56 of the pattern 50 on the table top 48 at two respective locations within the pattern 50.
Each of the first camera extrinsic parameter 52 and the second camera extrinsic parameter 54 are configured to obtain a lens zoom factor 58. The lens zoom factor 58 can be understood as a factor by which an image is given more detail (magnified) or given less detail (shrunk or de-magnified) in its display from a normal presentation in an original configuration. The second camera 44 can be configured, upon a calibration, for an image-to-model registration 60 of a part/component 26 being viewed. The image-to-model registration 60 of the part/component 26 is translated to the first camera via a precomputed extrinsic relationship 62 between the first camera 42 and the second camera 44. In an exemplary embodiment, the accurate lens zoom factor 58 for the narrow field of view camera 42 can be obtained by coupling the second camera 44 having a wider field of view lens 45 to move simultaneously with the narrow field of view camera 42. The simultaneous multiple camera images can also be utilized to create accurate 2D-3D registration.
In an exemplary embodiment, a first set of images 64 are recorded from each camera 42, 44. The cameras 42, 44 are physically moved an identical distance X along their common axis 46 closer or further from the pattern 50 or fiducial marks, then a second set of images 66 are recorded. The ratio of change of size between the first set of images 64 and the second set of images 66 in the second camera 44 to the change of size between the first set of images 64 and the second set of images 66 in the first camera 42 is the zoom factor 58 ((depicted schematically as a hexagon which may be considered as having a zoomed size relative to a predefined standard size).
The location and image size of a known pattern 50 or fiducial marks may be found in an image by template matching techniques including a random consensus (RANSAC) of features where the features include scale-invariant feature transform (SIFT), Speed-Up Robust Feature (SURF) algorithm, Affine Scale Invariant Feature Transform (ASIFT), other SIFT variants, a Harris Corner Detector, a Smallest Univalue Segment Assimilating Nucleus (SUSAN) algorithm, a Features from Accelerated Segment Test (FAST) corner detector, a Phase Correlation, a Normalized Cross-Correlation, a Gradient Location Orientation Histogram (GLOH) algorithm, a Binary Robust Independent Elementary Features (BRIEF) algorithm, a Center Surround Extremas (CenSure/STAR) algorithm, and an Oriented and Rotated BRIEF (ORB) algorithm.
In another exemplary embodiment, a simple ratio between sizes is not sufficient to calculate the zoom factor 58 accurately due to inherent lens distortions in one or both cameras 42, 44. In this case, one or both of the intrinsic and extrinsic parameters of one or both cameras 42, 44 are computed. The calibration of intrinsic and/or extrinsic parameters may be performed as needed. The extrinsic parameters 52, 54 of one or both cameras 42, 44 are first computed, the cameras 42, 44 are physically moved an identical distance along their common axis 46 closer or further from the pattern or fiducial marks 50, and the extrinsic parameters 52, 54 are recomputed. The zoom factor 58 may be computed from the first and recomputed extrinsic parameters 52, 54. In one nonlimiting embodiment, the cameras 42, 44 are moved a known, but non-identical, distance along their common axis. In this case, the images from either camera may be scaled by the ratio or inverse ratio of distances such that the images are substantially as if the cameras had been moved an identical distance. In yet another non-limiting embodiment, common axis 46 need not be orthogonal to table 12, but the angle to the orthogonal direction is known. In this case, the images from either camera may be scaled by the trigonometric relationship of the angle to the orthogonal and the distances moved such that the images are substantially as if the cameras had been moved an identical distance in an orthogonal direction.
Referring also to
There has been provided a multi-camera system for simultaneous registration and zoomed imaging. While the multi-camera system for simultaneous registration and zoomed imaging has been described in the context of specific embodiments thereof, other unforeseen alternatives, modifications, and variations may become apparent to those skilled in the art having read the foregoing description. Accordingly, it is intended to embrace those alternatives, modifications, and variations which fall within the broad scope of the appended claims.
Number | Name | Date | Kind |
---|---|---|---|
3804397 | Neumann | Apr 1974 | A |
4402053 | Kelley et al. | Aug 1983 | A |
4403294 | Hamada et al. | Sep 1983 | A |
4873651 | Raviv | Oct 1989 | A |
5064291 | Reiser | Nov 1991 | A |
5119678 | Bashyam et al. | Jun 1992 | A |
5345514 | Mandavieh et al. | Sep 1994 | A |
5345515 | Nishi et al. | Sep 1994 | A |
5351078 | Lemelson | Sep 1994 | A |
5963328 | Yoshida et al. | Oct 1999 | A |
6023637 | Liu et al. | Feb 2000 | A |
6153889 | Jones | Nov 2000 | A |
6177682 | Bartulovic et al. | Jan 2001 | B1 |
6271520 | Tao et al. | Aug 2001 | B1 |
6399948 | Thomas | Jun 2002 | B1 |
6434267 | Smith | Aug 2002 | B1 |
6462813 | Haven et al. | Oct 2002 | B1 |
6690016 | Watkins et al. | Feb 2004 | B1 |
6737648 | Fedder et al. | May 2004 | B2 |
6759659 | Thomas et al. | Jul 2004 | B2 |
6804622 | Bunker et al. | Oct 2004 | B2 |
6907358 | Suh et al. | Jun 2005 | B2 |
6965120 | Beyerer et al. | Nov 2005 | B1 |
7026811 | Roney, Jr. et al. | Apr 2006 | B2 |
7064330 | Raulerson et al. | Jun 2006 | B2 |
7119338 | Thompson et al. | Oct 2006 | B2 |
7122801 | Favro et al. | Oct 2006 | B2 |
7129492 | Saito et al. | Oct 2006 | B2 |
7164146 | Weir et al. | Jan 2007 | B2 |
7190162 | Tenley et al. | Mar 2007 | B2 |
7220966 | Saito et al. | May 2007 | B2 |
7233867 | Pisupati et al. | Jun 2007 | B2 |
7240556 | Georgeson et al. | Jul 2007 | B2 |
7272529 | Hogan et al. | Sep 2007 | B2 |
7313961 | Tenley et al. | Jan 2008 | B2 |
7415882 | Fetzer et al. | Aug 2008 | B2 |
7446886 | Aufmuth et al. | Nov 2008 | B2 |
7489811 | Brummel et al. | Feb 2009 | B2 |
7602963 | Nightingale et al. | Oct 2009 | B2 |
7689030 | Suh et al. | Mar 2010 | B2 |
7724925 | Shepard | May 2010 | B2 |
7738725 | Raskar et al. | Jun 2010 | B2 |
7823451 | Sarr | Nov 2010 | B2 |
7966883 | Lorraine et al. | Jun 2011 | B2 |
8050491 | Vaidyanathan | Nov 2011 | B2 |
8204294 | Alloo et al. | Jun 2012 | B2 |
8208711 | Venkatachalam et al. | Jun 2012 | B2 |
8221825 | Reitz et al. | Jul 2012 | B2 |
8239424 | Haigh et al. | Aug 2012 | B2 |
8431917 | Wang et al. | Apr 2013 | B2 |
8449176 | Shepard | May 2013 | B2 |
8520931 | Tateno | Aug 2013 | B2 |
8528317 | Gerez et al. | Sep 2013 | B2 |
8692887 | Ringermacher et al. | Apr 2014 | B2 |
8744166 | Scheid et al. | Jun 2014 | B2 |
8761490 | Scheid et al. | Jun 2014 | B2 |
8781209 | Scheid et al. | Jul 2014 | B2 |
8781210 | Scheid et al. | Jul 2014 | B2 |
8792705 | Scheid et al. | Jul 2014 | B2 |
8913825 | Taguchi et al. | Dec 2014 | B2 |
8983794 | Motzer et al. | Mar 2015 | B1 |
9037381 | Care | May 2015 | B2 |
9046497 | Kush et al. | Jun 2015 | B2 |
9066028 | Koshti | Jun 2015 | B1 |
9080453 | Shepard et al. | Jul 2015 | B2 |
9116071 | Hatcher, Jr. et al. | Aug 2015 | B2 |
9134280 | Cataldo et al. | Sep 2015 | B2 |
9146205 | Renshaw et al. | Sep 2015 | B2 |
9151698 | Jahnke et al. | Oct 2015 | B2 |
9154743 | Hatcher, Jr. et al. | Oct 2015 | B2 |
9240049 | Ciurea et al. | Jan 2016 | B2 |
9251582 | Lim et al. | Feb 2016 | B2 |
9300865 | Wang et al. | Mar 2016 | B2 |
9305345 | Lim et al. | Apr 2016 | B2 |
9458735 | Diwinsky et al. | Oct 2016 | B1 |
9465385 | Kamioka et al. | Oct 2016 | B2 |
9467628 | Geng et al. | Oct 2016 | B2 |
9471057 | Scheid et al. | Oct 2016 | B2 |
9476798 | Pandey et al. | Oct 2016 | B2 |
9476842 | Drescher et al. | Oct 2016 | B2 |
9483820 | Lim et al. | Nov 2016 | B2 |
9488592 | Maresca et al. | Nov 2016 | B1 |
9519844 | Thompson et al. | Dec 2016 | B1 |
9594059 | Brady et al. | Mar 2017 | B1 |
9734568 | Vajaria et al. | May 2017 | B2 |
9785919 | Diwinsky et al. | Oct 2017 | B2 |
9804997 | Sharp et al. | Oct 2017 | B2 |
9808933 | Lin et al. | Nov 2017 | B2 |
9981382 | Strauss et al. | May 2018 | B1 |
10438036 | Reome | Oct 2019 | B1 |
20020121602 | Thomas et al. | Sep 2002 | A1 |
20020167660 | Zaslaysky | Nov 2002 | A1 |
20030117395 | Yoon | Jun 2003 | A1 |
20030205671 | Thomas et al. | Nov 2003 | A1 |
20040089811 | Lewis et al. | May 2004 | A1 |
20040089812 | Favro et al. | May 2004 | A1 |
20040139805 | Antonelli et al. | Jul 2004 | A1 |
20040201672 | Varadarajan | Oct 2004 | A1 |
20040240600 | Freyer et al. | Dec 2004 | A1 |
20040245469 | Favro et al. | Dec 2004 | A1 |
20040247170 | Furze et al. | Dec 2004 | A1 |
20050008215 | Shepard | Jan 2005 | A1 |
20050113060 | Lowery | May 2005 | A1 |
20050151083 | Favro et al. | Jul 2005 | A1 |
20050167596 | Rothenfusser et al. | Aug 2005 | A1 |
20050276907 | Arris et al. | Dec 2005 | A1 |
20060012790 | Furze et al. | Jan 2006 | A1 |
20060078193 | Brummel et al. | Apr 2006 | A1 |
20060086912 | Weir et al. | Apr 2006 | A1 |
20070007733 | Hogarth et al. | Jan 2007 | A1 |
20070017297 | Georgeson et al. | Jan 2007 | A1 |
20070045544 | Favro et al. | Mar 2007 | A1 |
20080022775 | Sathish et al. | Jan 2008 | A1 |
20080053234 | Staroselsky et al. | Mar 2008 | A1 |
20080111074 | Weir et al. | May 2008 | A1 |
20080183402 | Malkin et al. | Jul 2008 | A1 |
20080229834 | Bossi et al. | Sep 2008 | A1 |
20080247635 | Davis et al. | Oct 2008 | A1 |
20080247636 | Davis et al. | Oct 2008 | A1 |
20090000382 | Sathish et al. | Jan 2009 | A1 |
20090010507 | Geng | Jan 2009 | A1 |
20090066939 | Venkatachalam et al. | Mar 2009 | A1 |
20090128643 | Kondo | May 2009 | A1 |
20090252987 | Greene, Jr. | Oct 2009 | A1 |
20090279772 | Sun et al. | Nov 2009 | A1 |
20090312956 | Zombo et al. | Dec 2009 | A1 |
20100212430 | Murai et al. | Aug 2010 | A1 |
20100220910 | Kaucic et al. | Sep 2010 | A1 |
20110062339 | Ruhge et al. | Mar 2011 | A1 |
20110083705 | Stone et al. | Apr 2011 | A1 |
20110119020 | Key | May 2011 | A1 |
20110123093 | Alloo et al. | May 2011 | A1 |
20110299752 | Sun | Dec 2011 | A1 |
20110302694 | Wang et al. | Dec 2011 | A1 |
20120154599 | Huang | Jun 2012 | A1 |
20120188380 | Drescher et al. | Jul 2012 | A1 |
20120249959 | You et al. | Oct 2012 | A1 |
20120275667 | Lu | Nov 2012 | A1 |
20120293647 | Singh et al. | Nov 2012 | A1 |
20130028478 | St-Pierre et al. | Jan 2013 | A1 |
20130041614 | Shepard et al. | Feb 2013 | A1 |
20130070897 | Jacotin | Mar 2013 | A1 |
20130113914 | Scheid et al. | May 2013 | A1 |
20130113916 | Scheid et al. | May 2013 | A1 |
20130163849 | Jahnke et al. | Jun 2013 | A1 |
20130235897 | Bouteyre et al. | Sep 2013 | A1 |
20130250067 | Laxhuber | Sep 2013 | A1 |
20140022357 | Yu | Jan 2014 | A1 |
20140056507 | Doyle et al. | Feb 2014 | A1 |
20140098836 | Bird | Apr 2014 | A1 |
20140184786 | Georgeson et al. | Jul 2014 | A1 |
20140185912 | Lim et al. | Jul 2014 | A1 |
20140198185 | Haugen | Jul 2014 | A1 |
20140200832 | Troy et al. | Jul 2014 | A1 |
20140350338 | Tanaka et al. | Nov 2014 | A1 |
20150041654 | Barychev et al. | Feb 2015 | A1 |
20150046098 | Jack et al. | Feb 2015 | A1 |
20150086083 | Chaudhry et al. | Mar 2015 | A1 |
20150128709 | Stewart et al. | May 2015 | A1 |
20150138342 | Brdar et al. | May 2015 | A1 |
20150185128 | Chang et al. | Jul 2015 | A1 |
20150233714 | Kim | Aug 2015 | A1 |
20150253266 | Lucon et al. | Sep 2015 | A1 |
20150314901 | Murray et al. | Nov 2015 | A1 |
20160012588 | Taguchi | Jan 2016 | A1 |
20160043008 | Murray et al. | Feb 2016 | A1 |
20160109283 | Broussais-Colella et al. | Apr 2016 | A1 |
20160178532 | Lim et al. | Jun 2016 | A1 |
20160241793 | Ravirala | Aug 2016 | A1 |
20160284098 | Okumura et al. | Sep 2016 | A1 |
20160314571 | Finn et al. | Oct 2016 | A1 |
20160328835 | Maresca, Jr. et al. | Nov 2016 | A1 |
20160334284 | Kaplun Mucharrafille et al. | Nov 2016 | A1 |
20170011503 | Newman | Jan 2017 | A1 |
20170023505 | Maione et al. | Jan 2017 | A1 |
20170052152 | Tat et al. | Feb 2017 | A1 |
20170085760 | Ernst et al. | Mar 2017 | A1 |
20170090458 | Lim et al. | Mar 2017 | A1 |
20170122123 | Kell et al. | May 2017 | A1 |
20170142302 | Shaw et al. | May 2017 | A1 |
20170184469 | Chang et al. | Jun 2017 | A1 |
20170184549 | Reed et al. | Jun 2017 | A1 |
20170184650 | Chang et al. | Jun 2017 | A1 |
20170211408 | Ahmadian et al. | Jul 2017 | A1 |
20170219815 | Letter et al. | Aug 2017 | A1 |
20170221274 | Chen et al. | Aug 2017 | A1 |
20170234837 | Hall et al. | Aug 2017 | A1 |
20170241286 | Roberts et al. | Aug 2017 | A1 |
20170258391 | Finn et al. | Sep 2017 | A1 |
20170262965 | Kiong et al. | Sep 2017 | A1 |
20170262977 | Finn et al. | Sep 2017 | A1 |
20170262979 | Xiong et al. | Sep 2017 | A1 |
20170262985 | Finn et al. | Sep 2017 | A1 |
20170262986 | Xiong et al. | Sep 2017 | A1 |
20170270651 | Bailey et al. | Sep 2017 | A1 |
20170297095 | Zalameda et al. | Oct 2017 | A1 |
20170284971 | Hall | Nov 2017 | A1 |
20180002039 | Finn et al. | Jan 2018 | A1 |
20180005362 | Wang et al. | Jan 2018 | A1 |
20180013959 | Slavens et al. | Jan 2018 | A1 |
20180019097 | Harada et al. | Jan 2018 | A1 |
20180098000 | Park | Apr 2018 | A1 |
20180111239 | Zak et al. | Apr 2018 | A1 |
20190299542 | Webb | Oct 2019 | A1 |
Number | Date | Country |
---|---|---|
2820732 | Dec 2014 | CA |
19710743 | Sep 1998 | DE |
1961919 | Aug 2008 | EP |
2545271 | Jun 2017 | GB |
06235700 | Aug 1994 | JP |
2015161247 | Sep 2015 | JP |
191452 | Jul 2013 | SG |
WO-2013088709 | Jun 2013 | WO |
2016112018 | Jul 2016 | WO |
2016123508 | Aug 2016 | WO |
2016176524 | Nov 2016 | WO |
Entry |
---|
Blachnio et al, “Assessment of Technical Condition Demonstrated by Gas Turbine Blades by Processing of Images of Their Surfaces”, Journal of KONBiN, 1(21), 2012, pp. 41-50. |
Raskar et al., ‘A Non-photorealistic Camera: Depth Edge Detection and Stylized Rendering using Multi-flash Imaging’ ACM Transactions on Graphics, 2004 http://www.merl.com/publications/docs/TR2006-107.pdf. |
Feris et al., ‘Specular Reflection Reduction with Multi-Flash Imaging’, 17th Brazilian Symposium on Computer Graphics and Image Processing, 2004. http://rogerioferis.com/publications/FerisSIB04.pdf. |
Holland, “First Measurements from a New Broadband Vibrothermography Measurement System”, AIP Conference Proceedings, 894 (2007), pp. 478-483. http://link.aip.org/link/doi/10.1063/1.2718010\. |
Gao et al., ‘Detecting Cracks in Aircraft Engine Fan Blades Using Vibrothermography Nondestructive Evaluation’, RESS Special Issue on Accelerated Testing, 2014, http://dx.doi.org/10.1016/j.ress.2014.05.009. |
Gao et al., ‘A Statistical Method for Crack Detection from Vibrothermography Inspection Data’, Statistics Preprints. Paper 68. http://lib.dr.iastate.edu/stat_las_preprints/68. |
Holland, ‘Thermographic Signal Reconstruction for Vibrothermography’, Infrared Physics & Technology 54 (2011) 503-511. |
Li et al., ‘Statistical Methods for Automatic Crack Detection Based on Vibrothermography Sequence-of-Images Data’, Statistics Preprints. Paper 69. http://lib.dr.iastate.edu/stat_las_preprints/69. |
Tian et al., ‘A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images’, Technometrics, 59, 247-261. |
Henneke et al. ‘Detection of Damage in Composite Materials by Vibrothermography’, ASTM special technical publication (696), 1979, pp. 83-95. |
http://www.npl.co.uk/commercial-services/sector-case-studies/thermal-imaging-reveals-the-invisible. |
Gao et al., ‘A Statistical Method for Crack Detection from Vibrothermography Inspection Data’,(2010) Statistics Preprints. Paper 68. http://lib.dr.iastate.edu/stat_las_preprints/68. |
Li1 Ming; Holland1 Stephen D.; and Meeker1 William Q.1 “Statistical Methods for Automatic Crack Detection Based on Vibrothermography Sequence-of-Images Data” (2010). Statistics Preprints. 69. |
Henneke et al. ‘Detection of Damage in Composite Materials by Vibrothermography’, ASTM special technical publication (696), American Society for Testing and Materials, 1979, pp. 83-95. |
http://www.npl.co.uk/commercial-services/sector-case-studies/thermal-imaging-reveals-the-invisible; Apr. 17, 2012. |
Tian et al., ‘A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images’, Technometrics, 59, 247-261. Feb. 1, 2017. |
Emmanuel J. Cand'es1,2, Xiaodong LI2, Yi MA3,4, and John Wright4, “Robust Principal Component Analysis”, (1)Department of Statistics, Stanford University, Stanford, CA; (2)Department of Mathematics, Stanford University, Stanford, CA; (3, 4) Electrical and Computer Engineering, UIUC, Urbana, IL (4) Microsoft Research Asia, Beijing, China, Dec. 17, 2009. |
Sebastien Parent; “From Human to Machine: How to Be Prepared for Integration of Automated Visual Inspection” Quality Magazine, https://www.qualitymag.com/articles/91976. Jul. 2, 2014. |
http://www.yxlon.com/products/x-ray-and-ct-inspection-systems/yxlon-mu56-tb, 2016. |
E J. Candès, X. Li, Y. Ma, and J. Wright, “Robust Principal Component Analysis”, submitted. http://www-stat.stanford.edu/˜candes/papers/RobustPCA.pdf. |
M. Sznaier, O. Camps, N. Ozay, T. Ding, G. Tadmor and D. Brooks, “The Role of Dynamics in Extracting Information Sparsely Encoded in High Dimensional Data Streams”, in Dynamics of Information Systems, Hirsch, M.J.; Pardalos, P. M.; Murphey, R. (Eds.), pp. 1-28, Springer Verlag, 2010. |
M. Fazel, H. Hindi, and S. Boyd, “A Rank Minimization Heuristic with Application to Minimum Order System Approximation”, American Control Conference, Arlington, Virginia, pp. 4734-4739, Jun. 2001. |
Meola et al., ‘An Excursus on Infrared Thermography Imaging’, J. Imaging 2016, 2, 36 http://www.mdpi.com/2313-433X/2/4/36/pdf. |
Yu et al., ‘ASIFT: An Algorithm for Fully Affine Invariant Comparison’, Image Processing on Line on Feb. 24, 2011. http://www.ipol.im/pub/art/2011/my-asift/article.pdf. |
Schemmel et al., ‘Measurement of Direct Strain Optic Coefficient of Ysz Thermal Barrier Coatings at Ghz Frequencies’, Optics Express, v. 25, n. 17, Aug. 21, 2017, https://doi.org/10.1364/OE.25.019968. |
Jean-Yves Bouguet, “Camera Calibration Toolbox for Matlab”, http://www.vision.caltech.edu/bouguetj/calib_doc/, accessed on Nov. 10, 2017. |
https://www.qualitymag.com/articles/91976-from-human-to-machine-how-to-be-prepared-for-integration-of-automated-visual-inspection. |
http://www.yxlon.com/products/x-ray-and-ct-inspection-systems/yxlon-mu56-tb. |
Yu et al. ‘Shadow Graphs and 3D Texture Reconstruction’, IJCV, vol. 62, No. 1-2, 2005, pp. 35-60. |
U.S. Non-Final Office Action dated Nov. 29, 2019 for corresponding U.S. Appl. No. 15/971,242. |
U.S. Non-Final Office Action dated Nov. 26, 2019 for corresponding U.S. Appl. No. 15/971,194. |
U.S. Non-Final Office Action dated Apr. 30, 2020 issued for corresponding U.S. Appl. No. 15/970,944. |
U.S. Non-Final Office Action dated Mar. 12, 2020 for corresponding U.S. Appl. No. 15/971,194. |
U.S. Final Office Action dated Aug. 27, 2020 issued for corresponding U.S. Appl. No. 15/970,944. |
U.S. Non-Final Office Action dated May 21, 2020 issued for corresponding U.S. Appl. No. 15/971,236. |
U.S. Non-Final Office Action dated Aug. 28, 2020 issued for corresponding U.S. Appl. No. 15/971,194. |
U.S. Non-Final Office Action dated Jun. 23, 2020 issued for corresponding U.S. Appl. No. 15/971,205. |
U.S. Notice of Allowance dated Oct. 19, 2020 issued for corresponding U.S. Appl. No. 15/971,270. |
U.S. Non-Final Office Action dated Mar. 5, 2019 for corresponding U.S. Appl. No. 15/971,227. |
Number | Date | Country | |
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20190342499 A1 | Nov 2019 | US |