Computer graphics processing and selective visual display system – Computer graphics processing – Three-dimension
Reexamination Certificate
2011-08-09
2011-08-09
Hajnik, Daniel F (Department: 2628)
Computer graphics processing and selective visual display system
Computer graphics processing
Three-dimension
C382S154000
Reexamination Certificate
active
07995055
ABSTRACT:
In some implementations, a computer-implemented method of classifying image data includes receiving a plurality of data points corresponding to three-dimensional image data; creating from the plurality of data points a first subset of data points that are above a ground plane in a scene represented by the plurality of data points; identifying a second subset of data points associated with an object in the scene, from the first subset of data points; identifying a plurality of features associated with the second subset of data points and determining a signature for the identified plurality of features; and classifying the second set of data points according to a correspondence between the calculated signature and a reference signature.
REFERENCES:
patent: 5821943 (1998-10-01), Shashua
patent: 6701005 (2004-03-01), Nichani
patent: 6911995 (2005-06-01), Ivanov et al.
patent: 7103213 (2006-09-01), Hirvonen et al.
patent: 7149677 (2006-12-01), Jayaram et al.
patent: 2002/0106135 (2002-08-01), Iwane
patent: 2003/0023614 (2003-01-01), Newstrom et al.
patent: 2003/0147553 (2003-08-01), Chen et al.
patent: 2004/0051711 (2004-03-01), Dimsdale et al.
patent: 2004/0184655 (2004-09-01), Ziegler et al.
patent: 2005/0031329 (2005-02-01), Krumm
patent: 2006/0047471 (2006-03-01), Krumm
patent: 2006/0061566 (2006-03-01), Verma et al.
patent: 2007/0280528 (2007-12-01), Wellington et al.
patent: 2008/0111815 (2008-05-01), Graves et al.
C. Bauckhage. Tree-based signatures for shape classification. In Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 2105-2108, 2006.
Jain, A. K., Murty, M. N., and Flynn, P. J. 1999. Data clustering: a review. ACM Comput. Surv. 31, 3 (Sep. 1999), 264-323. DOI=http://doi.acm.org/10.1145/331499.331504.
Wang, Z. & Schenk, T. (2000) “Building Extraction and Reconstruction from LIDAR Data”, In proceedings of IAPRS, July, vol. 33, part B3, p. 958-964.
Christopher Jaynes, Edward Riseman, and Allen Hanson. 2003. Recognition and reconstruction of buildings from multiple aerial images. Comput. Vis. Image Underst. 90, 1 (Apr. 2003), 68-98.
A Tutorial on Clustering Algorithms [online]. [retrieved on Oct. 26, 2006]. Retrieved from the Internet: <URL: www.elet.polimi.it/upload/matteuce/Clustering/tutorial—html/kmeans.html> 3 pages.
Burges, Christopher, “A Tutorial on Support Vector Machines for Pattern Recognition,”Data Mining and Knowledge Discovery, 2, 121-167, 1998, 48 pages.
Csurka, et al. “Visual Categorization with Bags of Keypoints,”Xerox Research Center Europe, 16, 2004 , 16 pages.
Data Clustering from Wikipedia [online]. [retrieved on Oct. 26, 2006]. Retrieved from the Internet: <URL: http://en.wikipedia.org/wiki/Data—clustering> 11 pages.
Deng, et al., “Construction 3D Urban Model from LIDAR and Image Sequence,”The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jul. 12-23, 2004, vol. 34, Part XXX, available at http://www.isprs.org/istanbul2004/, 4 pages.
Frueh et al., “3D Model Generation for Cities Using Aerial Photographs and Ground Level Laser Scans,”IEEE Conference on Computer Vision and Pattern Recognition, Kauai, USA 2001, pp. 11-31-38, vol. 2.2, 8 pages.
Hu, et al. “Approaches to Large-Scale Urban Modeling,”IEEE Computer Graphics and Applications, Nov./Dec. 2003, 8 pages.
Johnson et al., “Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, No. 5, May 1999, pp. 433-449, 17 pages.
Lombardi, et al. “Unified Stereovision for Ground, Road, and Obstacle Detection,” Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, Jun. 6-8, 2005, 6 pages.
Madhavan, et al. “Robust Detection and Recognition of Buildings in Urban Environments fro LADAR Data,”Proceedings of the 33rdApplied Imagery Pattern Recognition Workshop(AIP 2004) 6 pages.
Nguyen et al.. Kernel Matrix Evaluation,IJCAI-07, Jan. 6-12, 2007, pp. 987-992, available at http://www.informatik.uni-trier.de/˜ley/db/conf/ijcai/ijcai2007.html#NguyenH07, 6 pages.
Rubner et al., “A Metric for Distribution with Applications to Image Databases,”Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India, 8 pages.
Rubner et al., “The Earth Mover's Distance as a Metric for Image Retrieval,”International Journal of Computer Vision40(2), 99-121, 2000, 23 pages.
Thesis of Andrew Edie Johnson, “Spin-Images: A Representation for 3-D Surface Matching,”, Aug. 13, 1997, 308 pages.
UCLA Urban Simulation Laboratory Receives First Delivery of SGI InfiniteReality4, Available at http://www.sgi.com/company—info
ewsroom/press—releases/2002/july/ucla.html, retrieved Oct. 16, 2006, 2 pages.
UCLA-Urban Stimulation Team at UCLA, Available at http://www.ust.ucla.edu, retrieved Oct. 16, 2006, 3 pages.
UCLA-Urban Stimulation Team Members—About Us, Available at http://www.ust.ucla.edu/ustweb/team.html, retrieved Oct. 16, 2006, 1 page.
UCLA-Urban Stimulation Team—About Us, Available at http://www.ust.ucla.edu/ustweb/about—us.html, , retrieved Oct. 16, 2006, 4 pages.
Zhang et al., “Constructing 3D Reality Models of Urban Scenes from Ground-Based Synchronized Laser Data and Visual Data,”Proceedings of the 7thIASTED International Conference, Computer Graphics and Imaging, Aug. 17-19, 2004, Kaua, Hawaii, USA, 6 pages.
Zhang et al., “Local Features and Kernels for Classification of Texture and Object Categories: An In-Depth Study,”INRIAOct. 2005, 42 pages.
Zhang et al., “Constructing 3D Reality Models of Urban Scenes from Ground-Based Synchronized Laser Data and Visual Data,” fromProceedings(426)Computer Graphics and Imaging—2004, Available at http://www.actapress.com/PaperInfo.aspx?PaperID=16935 retrieved Oct. 16, 2006, 1 page.
Filip Daniel
Liu Lingyun
Ma Xiaoxu
Uhlik Christopher R.
Vincent Luc
Fish & Richardson P.C.
Google Inc.
Hajnik Daniel F
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