Hierarchical method and system for pattern recognition and...

Image analysis – Pattern recognition

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C382S191000, C382S282000, C382S305000

Reexamination Certificate

active

07738705

ABSTRACT:
A method and a system for pattern recognition utilizes an ensemble of reference patterns to represent the possible instances of the models to be recognized; constructs a hierarchy of estimators to simplify and enhance the recognition of the models of interest; approximates complex reference patterns with linear compositions of simpler patterns; fragments complex patterns into local patterns so that interference between the local patterns is sufficiently small for linearization methods to be applicable; constructs estimators during an offline stage to offload calculations from the online signal processing stage; designs model estimators based on optimization principles to enhance performance and to provide performance metrics for the estimated model instances; generates a hierarchy of reference descriptors during the offline stage, which are used for the design and construction of the model estimators. Specific examples are provided for the recognition of image features such as edges and junctions.

REFERENCES:
patent: 4771469 (1988-09-01), Wittenburg
patent: 5170440 (1992-12-01), Cox
patent: 5210799 (1993-05-01), Rao
patent: 5881170 (1999-03-01), Araki et al.
patent: 5933529 (1999-08-01), Kim
patent: 5987172 (1999-11-01), Michael
patent: 6408109 (2002-06-01), Silver et al.
patent: 6430551 (2002-08-01), Thelen et al.
patent: 6584221 (2003-06-01), Moghaddam et al.
patent: 6690842 (2004-02-01), Silver et al.
patent: 6753965 (2004-06-01), Kumar et al.
patent: 7142693 (2006-11-01), Zhang et al.
patent: 7165174 (2007-01-01), Ginter et al.
Ulf Grenander. Foundations of Pattern Analysis. Quarterly of Applied Math. vol. 27, No. 1, Apr. 1969.
Song—Chun Zhu. Statistical Modeling and Conceptualization of Visual Patterns. IEEE Trans. on PAMI. vol. 25, No. 6, Jun. 2003.
Karl Rohr. Recognizing Corners by Fitting Parametric Models. Intl. J. of Computer Vision, 9:3. 1992.
Baker, Nayar, Murase. Parametric Feature Detection. Intl J of Computer Vision, 27(1), 1998.
Baker, Nayar, Murase. Parametric Feature Detection. IEEE Conf on CVPR, 1996, p. 471-477.
Deriche, Blaszka. Recovering and characterizing image features using an efficient model based approach. IEEE Conf. on CVPR, 1993.
Blaszka, Deriche, Recovering and characterizing image features using an efficient model based approach. INRIA n 2422, Nov. 1994.
Blaszka, Deriche, A model based method for characterization and location of curved image features. INRIA n 2451, Dec. 1994.
Parida, Geiger Junctions: Detection, Classification, and reconstruction. IEEE Trans. on PAMI. vol. 20, No. 7, Jul. 1998.
Haralick Digital step of edges from zero crossing of second directional derivatives. IEEE Trans. on PAMI. vol. 6, No. 1, Jul. 1984.
Nalwa, BinFord. On Detecting Edges. IEEE Trans. on PAMI, 8:6, Nov. 1986.
Casadei. Robust Detection of Curves in Images. PhD Thesis, MIT, May 1995.
Casadei, Mitter. Hierarchical Curve Reconstruction. Part 1: Bifurcation Analysis and Recovery of Smooth Curves: Lecture Notes in Computer Science, v 1064, 1996, p. 199.
Casadei, Mitter. An efficient and provably correct algorithm for the multiscale estimation of image contours by means of polygonal lines. IEEE Trans. on Inf. T, 45:3, 1999.
Casadei, Mitter. Beyond the uniqueness assumption: ambiguity representation and redundancy elimination of . . . cycles. Computer Vision and Im Underst. 76:1, Oct. 1999.
Casadei, Mitter. Hierarchical image segmentation—Part I: Detection of regular curves in a vector graph. Intl J of Computer Vision, 27:1, 1998.
Parent, Zucker. Trace Inference, Curvature Consistency, and Curve Detection. IEEE Trans. of PAMI. 11:8, Aug. 1989.
Zucker, David, Dobbins and Iverson. The organization of Curve Detection: Coarse Tangent Fields and fine spline coverings. Intl Conf Comp Vision, 1989.
Zucker, Dobbins and Iverson. Two Stages of Curve Detection Suggest Two Styles of Visual Computation. Neural Computation, 1. 1989.
Elder Krupnik and Johnston. Contour Grouping with Prior Models. IEEE Trans on PAMI, 25:6. Jun. 2003.
Elder and Zucker Computing Contour Closure. European Conf on Computer Vision, 1996, vol. 1.
Cravier. A probabilistic Method for Extracting Chains of Collinear Segments. Computer Vision and Image Understanding, 76:1. Oct. 1999.
Sarkar and Boyer. Perceptual Organization in Computer Vision: A Review and a Proposal for a Classification Structure. IEEE Trans. on Systems, Man and Cybern. 23:2. 1993.
Sarkar and Boyer. A Computational Structure for Preattentive Perc. Organization: Graphical Enumeration and Voting Methods. IEEE Trans. on Systems, Man and Cyb. 24:2, 1994.
Mi-Suen Lee and Medioni. Grouping . . . into Regions, Curves and Junctions. Computer Vision and Image Understanding, 76:1, Oct. 1999.
Guy and Medioni. Inferring Global Perceptual Contours from Local Features. Intl J of Computer Vision, 20(1/2), 1996.
Saund. Labeling of Curvilinear Structure across Scales by Token Grouping. Intl Conf Computer Vision, 1992.
Saund. Perceptual Organization of Occluding Contours of Opaque Surfaces. Computer Vision and Image Understanding, 76:1. Oct. 1999.
Hancock and Kittler Edge-labeling Using Dictionary-Based Relaxation. IEEE Trans. on PAMI, 12:2 Feb. 1990.
Matalas, Benjamin and Kitney. An Edge Detection Technique Using the Facet Model and Parameterized Relaxation Labeling. IEEE T. on PAMI, 19:4, Apr. 1997.
Bienenstock, Geman and Potter Compositionality, MDL Priors, and Object Recognition. Adv in Neural Inf Proc Sys 9, 1997.
Steger. Removing the Bias from Line Detection, CVPR 1997.
Steger. An Unbiased Detector of Curvilinear Structures. PAMI 20:2, Feb. 1998.
Steger. Extracting Curvilinear Structures: A Differential Geometry Approach. Europ Conf on Computer Vision, 1996.
Steger. Extraction of curved lines from images, ICCV, 1996.
Steger. Evaluation of Subpixel Line and Edge Detection Precision and Accuracy.
Iverson and Zucker Logical/Linear Operators for Image Curves. PAMI, 17:10, Oct. 1995.
Kothe Integrated Edge and Junction Detection with the Boundary Tensor, ICCV, 2003.
Malik, Belongie, Shi, and Leung. Textons, Contours, and Regions: Cue Integration in Image Segmentation. ICCV, 1999.
Bergaud and Mallat. Matching Pursuit of Images. SPIE vol. 2491, 1995.
Mallat and Zhang. Matching Pursuits with Time-Frequency Dictionaries. IEEE Trans. on Signal Proc. 41:12, Dec. 1993.
Donoho. Wedgelets: Nearly-Minimax Estimation of Edges. Presented at “Asymptotic Methods in Stochastic Dynamics and Nonparam stat.”, Humboldt U, Berlin, Sep. 2-4, 1996.
Romberg, Wakin, and Baraniuk. Multiscale wedgelet image analysis: fast decompositions and modeling. IEEE Intl Conf on Image Processing, 2002.
Coifman and Wickerhauser. Entropy-based Algorithms for Best Basis Selection. IEEE Trans on Info. Th. 38:2p2, Mar. 1992.
Donoho. Can recent innovations in harmonic analysis “explain” key findings in natural image statistics? Network: Computation in Neural Systems, v 12, n 3, Aug. 2001, p. 371-93.
Chen, Donoho, and Saunders. Atomic Decomposition by Basis Pursuit. SIAM Review, v 43, n 1, 2001, p. 129-59.
Rebollo-Neira. Backward Adaptive Biorthogonalization. IEEE Signal Processing Letters, v 11, n 9, Sep. 2004, p. 705-708.
Fergus, Perona and Zissermann. A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition.
Helmer and Lowe. Object class Recognition with many Local Features.
Mohan, Papageorgiou and Poggio. Example-based Object Detection in Images by Components. PAMI, 23:4, Apr. 2001.
Selinger and Nelson. A Perceptual Grouping Hierarchy for Appearance-Based 3D Object Recognition. Computer Vision and Image Understanding, 76:1. Oct. 1999.
Singh, Arora and Ahuja. A Robust Probabilistic Estimation Framework for Parametric Image Models.
Guo, Zhu, and Wu. Towards a Mathematical Theory of Primal Sketch and Sketchability. ICCV 2003.
Nitzberg and Mumford. The 2.1 Sketch. ICCV 1990.
Brooks, Chojnacki, Gawley and Van Den Hengel. What value covariance information in estimating vision parameters? ICCV, 2001.
Zhou, Comaniciu and Krishnan. Conditional feature sensitivity: a unifying view on active recognition and feature selection. ICCV, 2003.

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

Hierarchical method and system for pattern recognition and... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Hierarchical method and system for pattern recognition and..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Hierarchical method and system for pattern recognition and... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-4205738

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