Moving object detection using Markov Random Field and Distributed Differential Evolution

Ghosh, Ashish; Mondal, Ajoy; Ghosh, Susmita

doi:10.1016/j.asoc.2013.10.021

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…Ghosh et al [108] introduced a moving object detection method that solves a task by integrating spatial and temporal segmentation. With spatial segmentation, a segmentation is regarded as a pixel labeling problem, which is solved by the maximum a posteriori (MAP) estimation [95] S e c t i o n 10.1…”

Section: Othersmentioning

confidence: 99%

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Application of evolutionary and swarm optimization in computer vision: a literature survey

Nakane

Bold

Sun

et al. 2020

IPSJ T Comput Vis Appl

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Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in solving combinatorial and NP-hard optimization problems in various research fields. However, in the field of computer vision, related surveys have not been updated during the last decade. In this study, inspired by the recent development of deep neural networks in computer vision, which embed large-scale optimization problems, we first describe a literature survey conducted to compensate for the lack of relevant research in this area. Specifically, applications related to the genetic algorithm and differential evolution from EAs, as well as particle swarm optimization and ant colony optimization from SAs and their variants, are mainly considered in this survey.

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Section: Othersmentioning

confidence: 99%

“…Author Section GA Rodehorst and Hellwich [107] S e c t i o n 11 DE Ghosh et al [108] S e c t i o n 11…”

Section: Algorithmmentioning

confidence: 99%

Application of evolutionary and swarm optimization in computer vision: a literature survey

Nakane

Bold

Sun

et al. 2020

IPSJ T Comput Vis Appl

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“…For instance, in 2013 Nyirarugira et al [19] designed an adaptive DE approach for a real time object tracking system. In 2014 Ghosh et al [13] proposed a DE algorithm for detecting moving objects in video streams. The details on the DE strategy can be found in [6], [11].…”

Section: Differential Evolutionmentioning

confidence: 99%

3D Microscopy Vision Using Multiple View Geometry and Differential Evolutionary Approaches

Tafti

Kirkpatrick

Owen

et al. 2014

Advances in Visual Computing

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Abstract. The Scanning Electron Microscope (SEM) as 2D imaging equipment has been widely used in biology and material sciences to determine the surface attributes of a microscopic object. Having 3D surfaces from SEM images would provide true anatomic shapes of micro samples which allow for quantitative measurements and informative visualization of the systems being investigated. In this contribution, we present a Differential Evolutionary (DE) approach for both SEM extrinsic calibration and 3D surface reconstruction. We show that the SEM extrinsic calibration and its 3D shape model can be accurately estimated in a global optimization platform. Several experiments from various perspectives are performed on real and synthetic data to validate the speed, reliability and accuracy of the proposed system. The present work is expected to stimulate more interest and draw attentions from the computer vision community to the fast-growing SEM application area.

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“…The DE algorithm has gradually become more popular and has been used in many practical cases, mainly because it has demonstrated good convergence properties and is principally easy to understand. DE has been successfully applied in diverse fields of engineering (Tang et al 2013;Ghosh et al 2014;Tang et al 2012;Storn 1996). Our research objective is to optimize the parameters without degrading the SVM classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid classification framework using SVM and differential evolution

Wang

2016

Soft Comput

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The kernel parameters setting for SVM in a training process impacts on the classification accuracy. The hybrid framework based on SVM and ensemble differential evolution is proposed to enhance the classification accuracy. The ensemble differential evolution is utilized to optimize the kernel parameters. The ensemble differential evolution algorithm employs two trial vector generation strategies and two control parameter settings. Ten real-world datasets using the proposed hybrid framework are tested. Compared with the SVM variants, the proposed hybrid model improves the classification accuracy of SVM.

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Moving object detection using Markov Random Field and Distributed Differential Evolution

Cited by 13 publications

References 30 publications

Application of evolutionary and swarm optimization in computer vision: a literature survey

Application of evolutionary and swarm optimization in computer vision: a literature survey

3D Microscopy Vision Using Multiple View Geometry and Differential Evolutionary Approaches

A novel hybrid classification framework using SVM and differential evolution

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