Object classification using image segmentation

Details Object classification using image segmentation Object classification using image segmentation

2004-07-23

2008-10-21

Wu, Jingge (Department: 2624)

Image analysis

Applications

Target tracking or detecting

C382S118000

Reexamination Certificate

active

07440586

ABSTRACT:
A method represents a class of objects by first acquiring a set of positive training images of the class of objects. A matrix A is constructed from the set of positive training images. Each row in the matrix A corresponds to a vector of intensities of pixels of one positive training image. Correlated intensities are grouped into a set of segments of a feature mask image. Each segment includes a set of pixels with correlated intensities. From each segment, a subset of representative pixels is selected. A set of features is assigned to each pixel in each subset of representative pixels of each segment of the feature mask image to represent the class of objects.

REFERENCES:
patent: 5657397 (1997-08-01), Bokser
patent: 6081766 (2000-06-01), Chapman et al.
patent: 6788827 (2004-09-01), Makram-Ebeid
patent: 7136844 (2006-11-01), Wrobel et al.
patent: 2001/0026631 (2001-10-01), Slocum et al.
patent: 2003/0026485 (2003-02-01), Gotsman et al.
patent: 2004/0095374 (2004-05-01), Jojic et al.
patent: 2004/0145592 (2004-07-01), Twersky
patent: 2004/0213439 (2004-10-01), Blake et al.
L. Sirovich and M. Kirby. Low-dimensional procedure for the characterisation of human face. In journal of the optical society of America 4, p. 510-524.
Athanasios Papoulis and S. Unnikrishna Pillai. Probability, Random Variables and Stochastic Processes. Fourth Edition, 2002.
B. Heisele, T. Serre, S. Mukherjee, and T. poggio. Feature reduction and hierarchy of classifiers for fast object detection in video images. In Proc. CVPR, vol. 2, pp. 1824, 2001.
NIST/SEMATECH, “Engineering statistics, E-Handbook of statistical methods, 2003, paragraph [6.5.4.1]”.
Modelon et al. “Clinical Laboratory Science program, University of Louisville, Jun. 1999”.
Shai Avidan. EigenSegments: A spatio-temporal decomposition of an ensemble of image. In European Conference on Computer Vision (ECCV) , May 2002, Copenhagen, Denmark.
Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In Computational Learning Theory: Eurocolt 95, pp. 2337. Springer-Verlag, 1995.
M. Elad, Y. Hel-Or and R. Keshet. Rejection based classifier for face detection. Pattern Recognition Letters 23 (2002) 1459-1471.
D. Keren, M. Osadchy, and C. Gotsman. Antifaces: A novel, fast method for image detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23 (7) :747761, 2001.
S.Z. Li, L. Zhu, Z.Q. Zhang, A. Blake, H.J. Zhang and H. Shum. Statistical Learning of Multi-View Face Detection. InProceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, May 2002.
Henry Schneiderman and Takeo Kanade. A statistical model for 3d object detection applied to faces and cars. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Jun. 2000.
K.-K. Sung and T. Poggio. Example-based Learning for View-Based Human Face Detection. InIEEE Transactions on Pattern Analysis and Machine Intelligence20 (1) :39-51, 1998.
M. Turk and A. Pentland. Eigenfaces for recognition. InJournal of Cognitive Neuroscience, vol. 3, No. 1, 1991.
S. Romdhani, P. Torr, B. Schoelkopf, and A. Blake. Computationally efficient face detection. In Proc. Intl. Conf. Computer Vision, pp. 695700, 2001.
H. A. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20 (1) :2338, 1998.
P. Viola and M. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. InIEEE Conference on Computer Vision and Pattern Recognition, Hawaii, 2001.
J. Wu, J. M. Rehg, and M. D. Mullin. Learning a Rare Event Detection Cascade by Direct Feature Selection. To appear in Advances in Neural Information Processing Systems 16 (NIPS*2003), MIT Pr.

Affiliated with

Also associated with

Rating

Object classification using image segmentation does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Object classification using image segmentation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Object classification using image segmentation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3992121