Differential Evolution as a viable tool for satellite image registration

Falco, Ivanoe De; Cioppa, Antonio Della; Maisto, Domenico; Tarantino, Ernesto

doi:10.1016/j.asoc.2007.10.013

Cited by 87 publications

(27 citation statements)

References 53 publications

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“…DE combines simple arithmetic operators with the classical crossover, mutation and selection operators within an easy to implement scheme and with few control parameters. Indeed, these advantages could influence the recent publication of several contributions that use the DE scheme for tackling the IR problem (De Falco et al 2008;Salomon et al 2001;Telenczuk et al 2006;Xu and Dony 2004). The reported results showed a competitive performance against traditional approaches and they also achieved a fast ratio of convergence.…”

Section: Chcmentioning

confidence: 97%

Performance evaluation of memetic approaches in 3D reconstruction of forensic objects

et al. 2008

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Different tasks in forensics require the use of 3D models of forensic objects (skulls, bones, corpses, etc.) captured by 3D range scanners. Since a whole object cannot be completely scanned in a single image using a range scanner, multiple acquisitions from different views are needed to supply the information to construct the 3D model by a range image registration method. There is an increasing interest in adopting evolutionary algorithms as the optimization technique for image registration methods. However, the image registration community tends to separate global and local searches in two different stages, named sequential hybridization approach, which is opposite to the scheme adopted by the memetic framework. In this work, we aim to analyze the capabilities of memetic algorithms (Moscato in On evolution, search, optimization, genetic algorithms and martial arts: towards memeticalgorithms. Report 826, Caltech Concurrent Computation Program, Pasadena, 1989) for tackling a really complex and challenging real-world problem as the 3D reconstruction of forensic objects. Our intention is threefold: firstly, designing new memetic-based methods for tackling a real-world problem and subsequently carrying out a performance and behavioral analysis of the results; secondly, comparing their performance with the one achieved by other methods based on the classical sequential hybridization approach; and thirdly, concluding the experimental study by highlighting the outcomes achieved by the best method in tackling the real-world problem. Several realworld 3D reconstruction problems from the Physical Anthropology Lab at the University of Granada, Spain, were used to support the evaluation study.

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Section: Chcmentioning

confidence: 97%

Performance evaluation of memetic approaches in 3D reconstruction of forensic objects

et al. 2008

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“…In its various forms DE has been applied to many real world problems (e.g. [11][12][13][14]). In our experiments we used the scheme classified as DE/rand/bin [9].…”

Section: Differential Evolutionmentioning

confidence: 99%

A new asynchronous parallel global optimization method based on simulated annealing and differential evolution

Olenšek

Tuma

Puhan

et al. 2011

Applied Soft Computing

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“…There exist two different IR approaches, each one working in a different solution space: (i) to search for the optimal point matching between two images (Cordón et al 2008;Liu 2004); and (ii) to directly search in the space of the registration transformation parameters (de Falco et al 2008;Silva et al 2005). While the former takes a combinatorial optimization approach, the latter does the same from a numerical (binary, integer, or real coded) optimization standpoint.…”

Section: Introductionmentioning

confidence: 99%

GRASP and path relinking hybridizations for the point matching-based image registration problem

et al. 2011

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In the last decade, image registration has proven to be a very active research area when tackling computer vision problems, especially in medical applications. In general, image registration methods aim to find a transformation between two images taken under different conditions. Point matching is an image registration approach based on searching for the right pairing of points between the two images, which involves a combinatorial optimization problem. From this matching, the registration transformation can be inferred by means of numerical methods.In this paper, we tackle the medical image registration problem by means of a recent hybrid metaheuristic composed of two well-known optimization methods: GRASP and path relinking. Several designs based on this new hybrid approach have been tested. Our experimentation with real-world problems shows the combination of J. Santamaría ( ) J. Santamaría et al.GRASP and evolutionary path relinking performs well when compared to previous state-of-the-art image registration approaches adopting both the point matching and transformation parameter approaches.

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Differential Evolution as a viable tool for satellite image registration

Cited by 87 publications

References 53 publications

Performance evaluation of memetic approaches in 3D reconstruction of forensic objects

Performance evaluation of memetic approaches in 3D reconstruction of forensic objects

A new asynchronous parallel global optimization method based on simulated annealing and differential evolution

GRASP and path relinking hybridizations for the point matching-based image registration problem

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