Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

Published in Journal Articles

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. Moreover, 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.

  1. Victor H Diaz-Ramirez, Andres Cuevas, Vitaly Kober, Leonardo Trujillo and Abdul Awwal. Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization. Optics Communications 338:77–89, 2015. URL, DOI BibTeX

    @article{Diaz-Ramirez2015,
    	author = "Diaz-Ramirez, Victor H. and Cuevas, Andres and Kober, Vitaly and Trujillo, Leonardo and Awwal, Abdul",
    	doi = "10.1016/j.optcom.2014.10.038",
    	file = ":home/emigdio/Documents/Mendeley Desktop/2015/Diaz-Ramirez et al. - 2015 - Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimiza.pdf:pdf",
    	issn = 00304018,
    	journal = "Optics Communications",
    	keywords = "Combinatorial optimization,Composite correlation filters,Multi-objective evolutionary algorithm,Object recognition",
    	pages = "77--89",
    	publisher = "Elsevier",
    	title = "{Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization}",
    	url = "http://linkinghub.elsevier.com/retrieve/pii/S0030401814009547",
    	volume = 338,
    	year = 2015
    }
    
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Customizable execution environments for evolutionary computation using BOINC + virtualization

Published in Journal Articles

Abstract

Evolutionary algorithms (EAs) consume large amounts of computational resources, particularly when they are used to solve real-world problems that require complex fitness evaluations. Beside the lack of resources, scientists face another problem: the absence of the required expertise to adapt applications for parallel and distributed computing models. Moreover, the computing power of PCs is frequently underused at institutions, as desktops are usually devoted to administrative tasks. Therefore, the proposal in this work consists of providing a framework that allows researchers to massively deploy EA experiments by exploiting the computing power of their instituions’ PCs by setting up a Desktop Grid System based on the BOINC middleware. This paper presents a new model for running unmodified applications within BOINC with a web-based centralized management system for available resources. Thanks to this proposal, researchers can run scientific applications without modifying the application’s source code, and at the same time manage thousands of computers from a single web page. Summarizing, this model allows the creation of on-demand customized execution environments within BOINC that can be used to harness unused computational resources for complex computational experiments, such as EAs. To show the performance of this model, a real-world application of Genetic Programming was used and tested through a centrally-managed desktop grid infrastructure. Results show the feasibility of the approach that has allowed researchers to generate new solutions by means of an easy to use and manage distributed system.

  1. Francisco Fernández Vega, Gustavo Olague, Leonardo Trujillo and Daniel Lombraña Gonzalez. Customizable execution environments for evolutionary computation using BOINC + virtualization. Natural Computing 12(2):163-177, 2013. BibTeX

    @article{DBLP:journals/nc/VegaOTG13,
    	author = "Francisco Fern{\'a}ndez de Vega and Gustavo Olague and Leonardo Trujillo and Daniel Lombra{\~n}a Gonzalez",
    	title = "Customizable execution environments for evolutionary computation using BOINC + virtualization",
    	journal = "Natural Computing",
    	volume = 12,
    	number = 2,
    	year = 2013,
    	pages = "163-177",
    	ee = "http://dx.doi.org/10.1007/s11047-012-9343-8",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    

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