Detecting objects in images using a soft cascade

Image analysis – Pattern recognition – Classification

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C382S181000, C382S228000

Reexamination Certificate

active

07634142

ABSTRACT:
Systems, methods, and computer program products, implementing techniques for detecting objects using a soft cascade. The techniques include receiving a digital data segment and determining whether the digital data segment resembles an object of interest by passing the digital data segment through a cascade. The cascade includes an ordered sequence of stages and a rejection function after each stage that determines whether to reject the digital data segment at the current stage as not resembling the object of interest, or to allow the digital data segment to pass to the next stage of evaluation. The rejection function allows the digital data segment to fail the current stage and still pass to the next stage.

REFERENCES:
patent: 4975969 (1990-12-01), Tal
patent: 5164992 (1992-11-01), Turk et al.
patent: 5440676 (1995-08-01), Alappat et al.
patent: 6770441 (2004-08-01), Dickinson et al.
patent: 6858394 (2005-02-01), Chee et al.
patent: 7020337 (2006-03-01), Viola et al.
patent: 7024033 (2006-04-01), Li et al.
patent: 7033754 (2006-04-01), Chee et al.
patent: 7050607 (2006-05-01), Li et al.
patent: 7076473 (2006-07-01), Moghaddam
patent: 7194114 (2007-03-01), Schneiderman
patent: 7274832 (2007-09-01), Nicponski
patent: 7286707 (2007-10-01), Liu et al.
patent: 7421114 (2008-09-01), Brandt
patent: 7440587 (2008-10-01), Bourdev
patent: 7440930 (2008-10-01), Brandt
patent: 2003/0110147 (2003-06-01), Li et al.
patent: 2004/0066966 (2004-04-01), Schneiderman
patent: 2004/0186816 (2004-09-01), Lienhart et al.
patent: 2005/0102246 (2005-05-01), Movellan et al.
patent: 2006/0062451 (2006-03-01), Li et al.
patent: 2006/0147107 (2006-07-01), Zhang et al.
patent: 2006/0222239 (2006-10-01), Bargeron et al.
patent: 2006/0248029 (2006-11-01), Liu et al.
patent: 2007/0101269 (2007-05-01), Hua et al.
patent: 2007/0110308 (2007-05-01), Hwang et al.
patent: 2008/0095445 (2008-04-01), Brandt
R. Xiao et al., “Boosting Chain Learning for Object Detection,” IEEE Computer Society, Proceedings of the Ninth IEEE International Conference on Computer Vision, (ICCV 2003) 2- vol. Set 0-7695-1950-4/03, 7 pages.
Jones, Michael et al., “Model-Based Matching by Linear Combinations of Prototypes,” 1995, (pp. 1-9).
King, Andrew, “A Survey of Methods for Face Detection,” Mar. 3, 2003, (pp. 1-32).
Viola, Paul et al., “Rapid Object Detection Using a Boosted Cascade of Simple Features” Conference on Computer Vision and Pattern Recognition 2001, (pp. 1-9).
Viola, Paul et al., “Robust Real-Time Object Detection,” Second International Workshop on Statistical and Computational Theories of Vision—Modeling, Learning, Computing, and Sampling, Vancouver, Canada, Jul. 31, 2001 (pp. 1-20).
Wu, Jianxin et al., “Learning A Rare Event Detection Cascase by Direct Feature Selection,” College of Computing and GVU Center, Georgia Institute of Technology, 2003, (pp. 1-8).
Yen-Yu Lin, et al., “Fast Object Detection with Occlusions,” Institute of Information Science, Academia Sinica, Nankang, Taipei, Taiwan, 2004 (pp. 402-413).
Camus et al. “Reliable and Fast Eye Finding in Close-Up Images”, IEEE, pp. 389-394, 2002.
Crow, “Summed Area Tables for Texture Mapping”, SIGGRAPH, 1984.
Feraud et al., “A Fast and Accurate Face Detector Based on Neural Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, Jan. 2001.
Freund et al., “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting”, Journal of Computer and System Sciences 55:119-139, 1997.
Fukai et al., “Facial Feature Point Extraction Method Based on Combination of Shape Extraction and Pattern Matching”, Jan. 1999.
Heisele et al., “Hierarchical Classification and Feature Reduction for Fast Face Detection with Support Vector Machines”, Pattern Recognition, vol. 36, 2003.
Hwang, “Pupil Detection in Photo ID”, Image Processing: Algorithms and Systems, III, Proc. of SPIE-IS&T Electronic Imaging, S298:82-87, 2004.
Kawaguchi et al., “Iris detection using intensity and edge information”, Pattern Recognition 36:549-562, 2003.
Li et al., “FloatBoost Learning and Statistical Face Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, Sep. 2004.
Luo et al., “An Efficient Automatic Red-Eye Detection and Correction Algorithm”, Proceedings of the 17thInternational Conference on Pattern Recognition (ICPR'04), 4 pages, 2004.
Meir et al., “An Introduction to Boosting and Leveraging”, Department of Electrical Engineering, Research School of Information Sciences and Engineering, Technion, Haifa 3200, Israel. Downloaded from the Internet, URL: <http://www-ee.technion.ac.il/!˜meir>, 66 pages, 2003.
Rizon et al., “Automatic Eye Detection Using Intensity and Edge Information”, IEEE, pp. II415-II420, 2000.
Rosenfeld et al., “Coarse-to-Fine Template Marching”, IEEE Trans. Syst. Man Cybernet, vol. 2, 1997.
Rowley et al., “Neural Network-Based Face Detection”, IEEE Pattern Analysis and Machine Intelligence, vol. 20, 1998.
Sahbi et al., “Coarse-to-Fine Support Classifiers for Face Detection”, ICPR, 2002.
Scassellati, “Eye Finding via Face Detection for a Foveated, Active Vision System”, Proceedings of 15thNational Conference of Artificial Intelligence, 1998.
Schapire et al., “Boosting the margin: a new explanation for the effectiveness of voting methods”, Proc. 14thIntl. Conf. Machine Learning, pp. 322-330, 1997.
Schapire et al., “Improved Boosting Algorithms Using Confidence-Rated Predictions”, Proceedings of the 11thAnnual Conference of Computational Learning Theory, pp. 80-91, 1998.
Schapire, “The Boosting Approach to Machine Learning”, Princeton University, downloaded from the Internet, URL: <www.cs.princeton.edu/˜schapire>, 32 pages, 2002.
Schapire, “The Strength of Weak Learnability”, Machine Learning 5:197-227, 1990.
Schneiderman et al., “Object Detection Using the Statistics of Parts”, International Journal of Computer Vision, 2002.
Schneiderman, “A Statistical Approach to 3D Object Detection Applies to Faces and Cars”, Ph.D. Thesis, CMI, May 2000.
Schneiderman, “Feature centric evaluation for efficient cascaded object detection”, IEEE Conf. Computer Vision and Pattern Recognition, 2004.
Smolka et al., “Towards automatic redeye effect removal”, Pattern Recognition Letters 24:1767-1785, 2003.
Sobottka et al., “A novel method for automatic face segmentation facial feature extraction and tracking”, Signal Processing: Image Communication 12:263-281, 1998.
Sun et al., “Automatic cascade training with perturbation bias”, IEEE Conf. Computer Vision and Pattern Recognition, 2004.
Sun et al., “Quantized Wavelet Features and Support Vector Machines for On-Road Vehicle Detection”, Seventh International Conference on Control, Automation, Robotics and Vision, 2002.
Valiant, “A Theory of the Learnable”, Comm. ACM 27(11):1134-1142, 1984.
Yang et al., “Detecting Faces in Images: A Survey”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, No. 1, Jan. 2002, 25 pages.
Zhu et al., “A fast automatic extraction algorithm of elliptic object groups from remote sensing images”, Pattern Recognition Letters 25:1471-1478, 2004.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Detecting objects in images using a soft cascade does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Detecting objects in images using a soft cascade, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Detecting objects in images using a soft cascade will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-4069788

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.