Image analysis – Image enhancement or restoration – Image filter
Reexamination Certificate
2008-05-13
2008-05-13
Bella, Matthew C. (Department: 2624)
Image analysis
Image enhancement or restoration
Image filter
Reexamination Certificate
active
11048536
ABSTRACT:
A computer implemented method models a background in a sequence of frames of a video. For each frame, the method detects static corners using an array of pixels of the frame, and extracts, for each static corner, features from a window of pixels around the static corner. For each static corner, a descriptor is determined from the corresponding features. Each static corner and corresponding descriptor is stored in a memory, and each static corner is classified as a background or foreground according to the descriptor to model a background in the video.
REFERENCES:
C. Harris and M. Stephens, “A combined corner and edge detector”, Fourth Alvey Vision Conference, pp. 147-151, 1988.
D.G.Lowe. Object recognition from local scale invariant features,, ICCV'99, Sep. 1999.
C.R. Wren, A. Azarbayejani, T.J. Darrell, and A.P. Pentland, “Pfinder: Real-time tracking of the human body”, PAMI, 19(7):780-785, Jul. 1997.
N. Friedman and S. Russell, “Image segmentation in video sequences: A probabilistic approach”, In Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI), Aug. 1997.
W.E.L. Grimson, C. Stauffer, R. Romano, and L. Lee, “Using adaptive tracking to classify and monitor activities in a site”, In CVPR'98, CA, 1998.
Elgammal, D. Harwood, L. S. Davis, “Non-parametric model for background subtraction”, ECCV'00. Dublin, Ireland, Jun./Jul. 2000.
Anurag Mittal, Nikos Paragios, “Motion-based background subtraction using adaptive kernel density estimation”, CVPR'04 vol. 2 Jun. 27-Jul. 2, 2004 Washington, DC, USA. pp. 302-309.
Dieter Koller, Joseph Weber, and Jitendra Malik, “Robust multiple car tracking with occlusion reasoning”, ECCV'94, Stockholm, Sweden, May 1994.
K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. “Wallflower: Principles and practice of background maintenance”. ICCV'99, Greece, Sep. 1999.
G. Doretto A. Chiuso, S. Soatto, Y.N. Wu, “Dynamic textures”, IJCV 51(2):91-109, 2003.
Antoine Monnet, Anurag Mittal, Nikos Paragios, Visvanathan Ramesh, “Background modeling and subtraction of dynamic scenes”, ICCV'03, Oct. 13-16, 2003 Nice, France. p. 1305.
Jing Zhong and Stan Sclaroff, “Segmenting foreground objects from a dynamic textured background via a robust Kalman Filter”, ICCV'03, pp. 44-50, 2003.
M. Harville, “A framework for high-level feedback to adaptive, per-pixel, Mixture-of-Gaussian background models”, ECCV'02, vol. 3, pp. 543-560, Copenhagen, Denmark, May 2002.
K. Mikolajczyk, C. Schmid, “A performance evaluation of local descriptors”, CVPR'03, Jun. 18-20, 2003, Madison, Wisconsin, pp. 257-265.
D.G.Lowe. “Object recognition from local scale invariant features”, ICCV'99, Sep. 1999.
J. Davis and G. Bradski, “Real-time Motion Template Gradients using Intel CVLib”, IEEE ICCV Workshop on Frame-rate Vision, Sep. 1999.
Avidan Shmuel
Zhu Qiang
Bella Matthew C.
Brinkman Dirk
Cunningham Greg F.
Mitsubishi Electric Research Laboratories Inc.
Mueller Clifton D.
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