Improved image segmentation algorithm based on GrabCut

Zhou, Liangfen; He, Junpeng

doi:10.3724/sp.j.1087.2013.00049

Cited by 7 publications

(2 citation statements)

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“…BP neural network is a multi-layer forward network, which is composed of an input layer, output layer, and hidden layer (one layer or multi-layer). [6][7] A typical three-layer BP neural network model is shown in Fig. 2.…”

Section: Experimental Methodsmentioning

confidence: 99%

Fiber Identification in Cross Section of Blended Yarn on Back Propagation Neural Network

Zhang

Ding

et al. 2021

AATCC Journal of Research

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An intelligent recognition algorithm was developed to identify fibers in the cross sections of blended yarn containing meta-aramid 1313 (Nomex), poly(phenylene-1,3,4-oxadiazole) (POD), flame resistant viscose, and flame-resistant vinylon. The yarn cross section image was obtained at x400 magnification. Drawing software was used to manually isolate single fiber images for training the back propagation (BP) neural network model in Matlab language image processing software. The GrabCut algorithm was used to de-noise the image and separate the target from the background. Finally, single fiber images and fiber distributions were obtained through the program. The result showed that the BP neural network model with the GrabCut algorithm can identify fiber type in a complex background more easily and more accurately than traditional algorithms.

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Section: Experimental Methodsmentioning

confidence: 99%

Fiber Identification in Cross Section of Blended Yarn on Back Propagation Neural Network

Zhang

Ding

et al. 2021

AATCC Journal of Research

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“…GrabCut algorithm is improved based on Graph Cuts. The Graph Cuts mainly uses image gray information, While GrabCut algorithm replaces the histogram to describe the color distribution with Gaussian mixture model, and thus its scope extended to the color chart; for Graph Cuts algorithm, the energy minimum of ( split ) is once achieved, and GrabCut algorithm uses the interactive iteration process of learning a constantly split estimation and model parameters; Graph Cuts algorithm needs the user to specify some the seed points of the object and the background, but GrabCut algorithm only needs to provide a set of pixels of the background area [3]. The GrabCut algorithm requires simple human-computer interaction.…”

Section: Traditional Grabcut Algorithmmentioning

confidence: 99%

Improved Color Image Segmentation Algorithm Based on GrabCut

Wang¹,

Peng²,

Li³

et al. 2013

AMM

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In realistic image processing, it is a problem of image foreground extraction. For a large number of color image processing, an important requirement is the automation of the extraction process. In this paper, by automatically setting foreground seed, we improve the image existing segmentation algorithm; by automatically searching image segmentation region, we accomplish image segmentation with the GrabCut algorithm, which is based on Gaussian Mixture Model and boundary computing. The improved algorithm in this paper can achieve the automation of image segmentation, without user participation in the implementation process, at the same time, it improves the efficiency of image segmentation, and gets a good result of image segmentation in complex background.

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Portrait Image Segmentation Based on Improved Grabcut Algorithm

Zheng

Chen

et al. 2015

Advances in Swarm and Computational Intelligence

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Improved image segmentation algorithm based on GrabCut

Cited by 7 publications

References 0 publications

Fiber Identification in Cross Section of Blended Yarn on Back Propagation Neural Network

Fiber Identification in Cross Section of Blended Yarn on Back Propagation Neural Network

Improved Color Image Segmentation Algorithm Based on GrabCut

Portrait Image Segmentation Based on Improved Grabcut Algorithm

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