Learnable object segmentation

Details Learnable object segmentation Learnable object segmentation

2007-04-10

2007-04-10

Do, Anh Hong (Department: 2624)

Image analysis

Image segmentation

C382S171000, C382S155000, C382S175000

Reexamination Certificate

active

10410063

ABSTRACT:
A segmentation method receives a learning image and an objects of interest specification. A segmentation learning method creates a segmentation recipe output. It performs a segmentation application using the second image and the segmentation recipe to create a segmentation result output. The segmentation learning method includes an object region of interest segmentation learning step and an object type specific segmentation learning step. The segmentation application method includes an object region of interest segmentation step and an object type specific segmentation step. The learnable object segmentation method further comprises an online learning and a feedback learning step that allows the update of the segmentation recipe automatically or under user direction.

REFERENCES:
patent: 5048095 (1991-09-01), Bhanu et al.
patent: 5953055 (1999-09-01), Huang et al.
patent: 6195121 (2001-02-01), Huang et al.
patent: 6396939 (2002-05-01), Hu et al.
patent: 6904159 (2005-06-01), Porikli
patent: 6985612 (2006-01-01), Hahn
patent: 7092548 (2006-08-01), Laumeyer et al.
Lee, JSJ, Haralick, RM and Shapiro, LG, “Morphologic Edge Detection,” IEEE Trans. Robotics and Automation RA3(2):142-56, 1987.
Haralick RM and Shapiro, LG, “Survey Image Segmentation Techniques,” Comput. Vision, Graphics Image Processing, vol. 29: 100-132, 1985.
Haralick, RM, Shapiro, RG, “Computer and Robot Vision”, vol. I, pp. 511-535, Addison Wesley, 1992.
Otsu N, “A Threshold Selection Method for Gray-level Histograms,” IEEE Trans. System Man and Cybernetics, vol. SMC-9, No. 1, Jan. 1979, pp. 62-66.
Hall, M. A. Correlation-based feature selection for discrete and numeric class machine learning. Proceedings of the Seventeenth International Conference on Machine Learning, Stanford University, CA. Morgan Kaufman Publishers, 2000.
Devijver and Kittler, Pattern Recognition: A Statistical Approach, PHI, NJ, 1982 pp. 212-219.
Breiman L., Friedman J. H., Olshen R. A. and Stone C. J., “Classification And Regression Trees”, Chapman &Hall/CRC, 1984, pp. 18-58.
Quinlan J. R., “C4.5 Programs For Machine Learning”, Morgan Kaufmann, 1993, pp. 17-26.
Robert E. Schapire, “The boosting approach to machine learning: An overview.” In MSRI Workshop on Nonlinear Estimation and Classification, 2001.
Dietterich, T. G. Ensemble Methods in Machine Learning. In J. Kittler and F. Roli (Ed.) First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science (pp. 1-15). New York: Springer Verlag, 2000.
Hu, MK, “Visual Pattern Recognition by Moment Invariants”, IRE Transactions on Information Theory, pp. 179-187. Feb. 1962.
Haralick, RM, Shapiro, RG, “Computer and Robot Vision”, vol. I, pp. 28-48, Addison Wesley, 1992.
Serra, J, “Image analysis and mathematical morphology,” London: Academic, 1982, p. 395, pp. 385-387.

Affiliated with

Also associated with

Rating

Learnable object 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 Learnable object segmentation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Learnable object segmentation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3741242