Detection of small objects in clutter using a GA-RBF neural network

Leung, Henry; Dubash, Neville; Xie, Nan

doi:10.1109/7.993232

Cited by 95 publications

(39 citation statements)

References 19 publications

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“…It is called Pittsburgh representation scheme. In the specialized bibliography, there are some proposals which represents a solution by means this Pittsburgh scheme: with binary encoded (Sergeev et al 1998;Vesin and Gruter 1999;Moechtar et al 1999;Dawson et al 2000;Sumathi et al 2001;Du and Zhang 2008), integer (Billings and Zheng 1995) or real encoded (Leung et al 2002;Esposito et al 2000b) chromosomes.…”

Section: Rbfns and The Classification Problem Frameworkmentioning

confidence: 99%

CO2RBFN: an evolutionary cooperative–competitive RBFN design algorithm for classification problems

et al. 2009

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This paper presents a new evolutionary cooperative-competitive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, CO 2 RBFN, promotes a cooperativecompetitive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final solution. The proposal considers, in order to measure the credit assignment of an individual, three factors: contribution to the output of the complete RBFN, local error and overlapping. In addition, to decide the operators' application probability over an RBF, the algorithm uses a Fuzzy Rule Based System. It must be highlighted that the evolutionary algorithm considers a distance measure which deals, without loss of information, with differences between nominal features which are very usual in classification problems. The precision and complexity of the network obtained by the algorithm are compared with those obtained by different soft computing methods through statistical tests. This study shows that CO 2 RBFN obtains RBFNs with an appropriate balance between accuracy and simplicity, outperforming the other methods considered.

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Section: Rbfns and The Classification Problem Frameworkmentioning

confidence: 99%

CO2RBFN: an evolutionary cooperative–competitive RBFN design algorithm for classification problems

et al. 2009

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“…Leung et al [68] report that they used the GA RBF to employ the GA to search for the optimum centres, variance and the number of hidden nodes. The parameters are encoded onto the chromosome as real numbers and following Billings and Zheng [44] the network complexity is controlled by an approximation of Akaike's information criterion [23].…”

Section: A Search For An Optimal Subset And/or An Optimal Architecturementioning

confidence: 99%

A review of genetic algorithms applied to training radial basis function networks

Harpham

Dawson

Brown

2004

Neural Comput & Applic

113

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The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.

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“…In [23], Ponsford et al present the design and implementation of an integrated maritime surveillance system based on high-frequency surface wave radars. Leung et al [24] combine genetic algorithm and radial basis function neural network to search optimal values of a detector model. This model is then used to detect small surface targets in various sea conditions.…”

Section: Traditional Approachmentioning

confidence: 99%

Detection and tracking of moving objects in a maritime environment using level set with shape priors

Frost

Tapamo

2013

J Image Video Proc

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Over the years, maritime surveillance has become increasingly important due to the recurrence of piracy. While surveillance has traditionally been a manual task using crew members in lookout positions on parts of the ship, much work is being done to automate this task using digital cameras coupled with a computer that uses image processing techniques that intelligently track object in the maritime environment. One such technique is level set segmentation which evolves a contour to objects of interest in a given image. This method works well but gives incorrect segmentation results when a target object is corrupted in the image. This paper explores the possibility of factoring in prior knowledge of a ship's shape into level set segmentation to improve results, a concept that is unaddressed in maritime surveillance problem. It is shown that the developed video tracking system outperforms level set-based systems that do not use prior shape knowledge, working well even where these systems fail.

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Detection of small objects in clutter using a GA-RBF neural network

Cited by 95 publications

References 19 publications

CO2RBFN: an evolutionary cooperative–competitive RBFN design algorithm for classification problems

CO2RBFN: an evolutionary cooperative–competitive RBFN design algorithm for classification problems

A review of genetic algorithms applied to training radial basis function networks

Detection and tracking of moving objects in a maritime environment using level set with shape priors

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