Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization
Abstract
Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.
- Authors:
-
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Instituto Politecnico National - CITEDI, Tijuana (Mexico)
- CICESE, Ensenada (Mexico)
- Instituto Tecnologico de Tijuana, Tijuana (Mexico)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1266690
- Alternate Identifier(s):
- OSTI ID: 1250056
- Report Number(s):
- LLNL-JRNL-657541
Journal ID: ISSN 0030-4018
- Grant/Contract Number:
- AC52-07NA27344
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Optics Communications
- Additional Journal Information:
- Journal Volume: 338; Journal Issue: C; Journal ID: ISSN 0030-4018
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; object recognition; composite correlation filters; multi-objective evolutionary algorithm; combinatorial optimization
Citation Formats
Awwal, Abdul, Diaz-Ramirez, Victor H., Cuevas, Andres, Kober, Vitaly, and Trujillo, Leonardo. Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization. United States: N. p., 2014.
Web. doi:10.1016/j.optcom.2014.10.038.
Awwal, Abdul, Diaz-Ramirez, Victor H., Cuevas, Andres, Kober, Vitaly, & Trujillo, Leonardo. Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization. United States. https://doi.org/10.1016/j.optcom.2014.10.038
Awwal, Abdul, Diaz-Ramirez, Victor H., Cuevas, Andres, Kober, Vitaly, and Trujillo, Leonardo. Thu .
"Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization". United States. https://doi.org/10.1016/j.optcom.2014.10.038. https://www.osti.gov/servlets/purl/1266690.
@article{osti_1266690,
title = {Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization},
author = {Awwal, Abdul and Diaz-Ramirez, Victor H. and Cuevas, Andres and Kober, Vitaly and Trujillo, Leonardo},
abstractNote = {Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.},
doi = {10.1016/j.optcom.2014.10.038},
journal = {Optics Communications},
number = C,
volume = 338,
place = {United States},
year = {Thu Oct 23 00:00:00 EDT 2014},
month = {Thu Oct 23 00:00:00 EDT 2014}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Cited by: 10 works
Citation information provided by
Web of Science
Web of Science
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
Speeded-Up Robust Features (SURF)
journal, June 2008
- Bay, Herbert; Ess, Andreas; Tuytelaars, Tinne
- Computer Vision and Image Understanding, Vol. 110, Issue 3
Visual tracking and learning using speeded up robust features
journal, December 2012
- Li, Jingyu; Wang, Yulei; Wang, Yanjie
- Pattern Recognition Letters, Vol. 33, Issue 16
Multiclass pattern recognition using adaptive correlation filters with complex constraints
journal, March 2012
- Diaz-Ramirez, Victor H.
- Optical Engineering, Vol. 51, Issue 3
Design of correlation filters for pattern recognition using a noisy reference
journal, March 2012
- Aguilar-González, Pablo Mario; Kober, Vitaly
- Optics Communications, Vol. 285, Issue 5
Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming
journal, June 2011
- Olague, Gustavo; Trujillo, Leonardo
- Image and Vision Computing, Vol. 29, Issue 7
Target recognition under nonuniform illumination conditions
journal, January 2009
- Diaz-Ramirez, Victor H.; Kober, Vitaly
- Applied Optics, Vol. 48, Issue 7
Optical correlator based target detection, recognition, classification, and tracking
journal, January 2012
- Manzur, Tariq; Zeller, John; Serati, Steve
- Applied Optics, Vol. 51, Issue 21
Optimized pre-processing input plane GPU implementation of an optical face recognition technique using a segmented phase only composite filter
journal, February 2013
- Ouerhani, Y.; Jridi, M.; Alfalou, A.
- Optics Communications, Vol. 289
Hardware accelerated optical alignment of lasers using beam-specific matched filters
journal, January 2009
- Awwal, Abdul A. S.; Rice, Kenneth L.; Taha, Tarek M.
- Applied Optics, Vol. 48, Issue 27
Distortion-tolerant minimum-mean-squared-error filter for detecting noisy targets in environmental degradation
journal, August 2000
- Towghi, Nasser
- Optical Engineering, Vol. 39, Issue 8
Design of correlation filters for recognition of linearly distorted objects in linearly degraded scenes
journal, January 2007
- Ramos-Michel, Erika M.; Kober, Vitaly
- Journal of the Optical Society of America A, Vol. 24, Issue 11
Accuracy of location measurement of a noisy target in a nonoverlapping background
journal, January 1996
- Kober, Vitaly; Campos, Juan
- Journal of the Optical Society of America A, Vol. 13, Issue 8
Decision optimization for face recognition based on an alternate correlation plane quantification metric
journal, January 2012
- Alfalou, A.; Brosseau, C.; Katz, P.
- Optics Letters, Vol. 37, Issue 9
Selecting a composite correlation filter design: a survey and comparative study
journal, June 2008
- Vijaya Kumar, B. V. K.
- Optical Engineering, Vol. 47, Issue 6
Tutorial survey of composite filter designs for optical correlators
journal, January 1992
- Vijaya Kumar, B. V. K.
- Applied Optics, Vol. 31, Issue 23
Unconstrained correlation filters
journal, January 1994
- Mahalanobis, Abhijit; Vijaya Kumar, B. V. K.; Song, Sewoong
- Applied Optics, Vol. 33, Issue 17
Signal-to-noise ratio considerations in modified matched spatial filters
journal, January 1986
- Vijaya Kumar, B. V. K.; Pochapsky, E.
- Journal of the Optical Society of America A, Vol. 3, Issue 6
Multivariant technique for multiclass pattern recognition
journal, January 1980
- Hester, Charles F.; Casasent, David
- Applied Optics, Vol. 19, Issue 11
Visual object tracking using adaptive correlation filters
conference, June 2010
- Bolme, Dav; Beveridge, J. Ross; Draper, Bruce A.
- 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Correlation filters with controlled scale response
journal, July 2006
- Kerekes, R. A.; Kumar, B. V. K. V.
- IEEE Transactions on Image Processing, Vol. 15, Issue 7
Optimal trade-off synthetic discriminant function filters for arbitrary devices
journal, January 1994
- Kumar, B. V. K. Vijaya; Mahalanobis, Abhijit; Carlson, Daniel W.
- Optics Letters, Vol. 19, Issue 19
Multiobjective evolutionary algorithms: A survey of the state of the art
journal, March 2011
- Zhou, Aimin; Qu, Bo-Yang; Li, Hui
- Swarm and Evolutionary Computation, Vol. 1, Issue 1
From few to many: illumination cone models for face recognition under variable lighting and pose
journal, June 2001
- Georghiades, A. S.; Belhumeur, P. N.; Kriegman, D. J.
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, Issue 6
Support-vector networks
journal, September 1995
- Cortes, Corinna; Vapnik, Vladimir
- Machine Learning, Vol. 20, Issue 3
Works referencing / citing this record:
Estimation of the 3D Pose of an Object Using Correlation Filters and CMA-ES
book, March 2018
- Dibene, Juan Carlos; Picos, Kenia; Díaz-Ramírez, Victor H.
- Applications of Evolutionary Computation
Correlation Coefficients of Hesitant Fuzzy Sets and Their Application Based on Fuzzy Measures
journal, January 2015
- Meng, Fanyong; Chen, Xiaohong
- Cognitive Computation, Vol. 7, Issue 4