Nonhuman animal integument pixel classification

Details Nonhuman animal integument pixel classification Nonhuman animal integument pixel classification

2006-06-21

2011-11-22

Mariam, Daniel (Department: 2624)

Image analysis

Image segmentation

C382S162000

Reexamination Certificate

active

08064694

ABSTRACT:
Systems and methods of classifying pixels into a nonhuman animal integument class are described. In one aspect, values of features in a discriminant space are determined for respective pixels of the input image. The pixels are classified into a nonhuman animal integument class based on the respective feature values and a mapping that segments the discriminant space into one or more regions of feature values representing pixels associated with a nonhuman animal integument characteristic and one or more regions of feature values representing pixels unassociated with the nonhuman animal integument characteristic.

REFERENCES:
patent: 5432863 (1995-07-01), Benati et al.
patent: 6009209 (1999-12-01), Acker et al.
patent: 6016354 (2000-01-01), Lin et al.
patent: 6292574 (2001-09-01), Schildkraut et al.
patent: 6307964 (2001-10-01), Lin et al.
patent: 6529630 (2003-03-01), Kinjo
patent: 6728401 (2004-04-01), Hardeberg
patent: 6766053 (2004-07-01), Fan et al.
patent: 6873743 (2005-03-01), Steinberg
patent: 6917703 (2005-07-01), Steffens et al.
patent: 7023447 (2006-04-01), Luo et al.
patent: 7162095 (2007-01-01), Chen et al.
patent: 2001/0046311 (2001-11-01), Okano et al.
patent: 2002/0136450 (2002-09-01), Chen et al.
patent: 2002/0176623 (2002-11-01), Steinberg
patent: 2003/0007687 (2003-01-01), Nesterov et al.
patent: 2003/0044042 (2003-03-01), King et al.
patent: 2003/0202105 (2003-10-01), Gaubatz et al.
patent: 2004/0184670 (2004-09-01), Jarman et al.
patent: 2004/0213476 (2004-10-01), Luo et al.
patent: 2005/0047655 (2005-03-01), Luo et al.
patent: 2005/0047656 (2005-03-01), Luo et al.
patent: 2005/0175227 (2005-08-01), Paschalakis
patent: WO99/17254 (1999-04-01), None
Gaubatz. M. et al., “Automatic red-eye detection and correction,” 2002 International Conference on Image Processing, vol. 1, pp. 804-807 (2002).
R-L Hsu, M. Abdel-Mottaleb and A.K. Jain, “Face Detection in Color Images,” ICIP, Thessaloniki, 2001.
“Better Pictures, More Choices,” Eastman Kodak Company, (Feb. 5, 2002).
“Industry's first completely automatic red eye detection and reduction system launched,” Pictos Technologies, Inc. (Apr. 2002).
“Red-Eye Removal,” Pixology—Product Categories, (before Apr. 2003).
Paul Viola and Michael J. Jones, “Robust Real-Time Object Detection,” Cambridge Research Reports Technical Report Series (Feb. 2001).
Chrisophe Garcia & Georgios Tziritas, “Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis,” IEEE Transactions on Multimedia, vol. 1, No. 3 S.
R. Ulichney. M. Gaubatz, JM Van Thong, “RedBot—a tool for improving red-eye correction”, Eleventh Color Imaging Conf., (The Society for Imaging Science and Technology), Scot.
Jonathan Yen et al., “Redeye selection in semiautomatic mode,” Research Disclosure, p. 546, May 2005.
Minh N. Do et al., “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans Image Proc., vol. 14. No. 12 (Dec. 2005).
Duncan D.-Y. Po et al., “Directional Multiscale Modeling of Images using the Contourlet Transform,” IEEE Trans Image Proc., vol. 15, No. 6 (Jun. 2006).
A.C. Bovik et al., “Multichannel texture analysis using localized spatial filters,” IEEE Trans Pattern Analysis & Mach. Intell., vol. 12, No. 1, pp. 55-73 (Jan. 1990).
Ramin Eslami et al., “On low bit-rate coding using the contourlet transform,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, Nov. 2003.
Jia Li et al., “Context-based multiscale classification of document images using wavelet coefficient distributions,” IEEE Trans Image Proc., vol. 9, No. 9,1604-16(Sep. 2000).
Kenny Teng et al., “Face recognition using wavelet representations obtained from different pruning strategies,” Dept. of ECE Carnegie Mellon University (Dec. 2005).
John R. Smith et al., “Transform features for texture classification and discrimination in large image databases,” Proc. IEEE Int'l Conf on Image Proc (ICIP-94) (1994).
Nasir Rajpoot, “Texture classification using discriminant wavelet packet subbands,” MWSCAS'2002, Tulsa, Oklahoma (USA) (Aug. 2002).
Nicu Sebe et al., “Wavlet based texture classification,” International Conference on Pattern Recognition (ICPR'00), vol. III, pp. 959-962 (2000).

Affiliated with

Also associated with

Rating

Nonhuman animal integument pixel classification does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Nonhuman animal integument pixel classification, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Nonhuman animal integument pixel classification will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-4273195