A novel evolutionary algorithm inspired by the states of matter for template matching

Cuevas, Erik; Echavarría, Alonso; Zaldívar, Daniel; Pérez‐Cisneros, Marco

doi:10.1016/j.eswa.2013.05.055

Cited by 53 publications

(23 citation statements)

References 20 publications

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“…This mechanism, which provides great help to detect object in cluttered background, can enhance the contrast of sensory information and reduce low-frequency noises [10]. Experimental results confirm that SFS is more capable than several intelligent algorithms such as IFABC [11], BEABC [12], SMS [13], and ICA [7] in this LI-based template matching scheme. The rest of the paper is structured as follows.…”

Section: Introductionsupporting

confidence: 54%

“…In this section, we verify the performance of our proposed algorithm through a series of comparative experiments with those produced by the IFABC method [11], BEABC method [12], SMS method [13], and ICA method [7]. Simulations have been executed across seven images 1-7 that are shown in Figures 3-9, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The maximum function evolution numbers (MaxFEs) are set to 300. Such a standard of stopping has been carefully selected to assure compatibility between similar works reported in the literature [8,13].…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, researchers have gradually shifted their interests towards the utilization of intelligent algorithms, such as artificial bee colony (ABC) [10], internal-feedback artificial bee colony (IFABC) [11], balance-evolution artificial bee colony (BEABC) [12], states of matter search (SMS) algorithm [13], and imperialist competitive algorithm (ICA) [7] which have been proposed for this template matching problem. Although these algorithms aim to reduce the computation load of the global optimums searching, none of them can completely guarantee the derivation of suboptimal matching results.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition

Luo

Zhang

Zhou

2017

Scientific Programming

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Template matching is a basic and crucial process for image processing. In this paper, a hybrid method of stochastic fractal search (SFS) and lateral inhibition (LI) is proposed to solve complicated template matching problems. The proposed template matching technique is called LI-SFS. SFS is a new metaheuristic algorithm inspired by random fractals. Furthermore, lateral inhibition mechanism has been verified to have good effects on image edge extraction and image enhancement. In this work, lateral inhibition is employed for image preprocessing. LI-SFS takes both the advantages of SFS and lateral inhibition which leads to better performance. Our simulation results show that LI-SFS is more effective and robust for this template matching mission than other algorithms based on LI.

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Section: Introductionsupporting

confidence: 54%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition

Luo

Zhang

Zhou

2017

Scientific Programming

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“…Evolutionary Algorithms have proven their ability to overcome this drawback by means of minimizing computational costs in template matching (Cuevas et al, 2013) (Cuevas et al, 2017) (Oliva et al, 2014. A smart search strategy balancing exploration and exploitation of the search space lowers the number of similarity function calls thus lowering computational costs.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Algorithm for Fast Intensity Based Image Matching Between Optical and Sar Satellite Imagery

Fischer

Schuegraf

Merkle

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

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Locust Search Algorithm Applied for Template Matching

Cuevas

Fausto

González

2019

Intelligent Systems Reference Library

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A novel evolutionary algorithm inspired by the states of matter for template matching

Cited by 53 publications

References 20 publications

Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition

Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition

An Evolutionary Algorithm for Fast Intensity Based Image Matching Between Optical and Sar Satellite Imagery

Locust Search Algorithm Applied for Template Matching

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