Integration of fuzzy Markov random field and local information for separation of moving objects and shadows

Subudhi, Bidyadhar; Ghosh, Susmita; Cho, Sung-Bae; Ghosh, Ashish

doi:10.1016/j.ins.2015.10.031

Cited by 10 publications

(2 citation statements)

References 36 publications

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“…To get back the data from the shadow area there are three main techniques of image enhancement like linear correlation, histogram matching, and gamma correlation is used. Over the past decades, Various color-based [1], edge-based [2], change in texture-based [3], and other methodology [4][5][6][7] for shadow detection and removal algorithms have successfully brought the desired objectives. In 2014, Song et al [8] discussed example based learning methods for shadow detection and removal which models the relation based on Markov random field.…”

Section: Figure 2 Umbra and Penumbra Formationmentioning

confidence: 99%

Optimization of Shadow Detection and Removal using Multilevel Thresholds and Improved Artificial Bee Colony Algorithm

Das¹,

Sudan²

2019

IJRTE

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Shadow Detection and removal from images is a challenging task in visual surveillance and computer vision applications. The appearance of shadows creates severe problems. There are various methods already exists but scope in this area is wide and open. In this paper, Optimization of Shadow Detection and Removal using Improved Artificial Bee Colony Algorithm (IABC) is proposed. The proposed method uses edge map, multilevel thresholds, masking, boundaries evaluation and, IABC algorithm. First data pre-processing is applied to find the correlation between the pixels then three level low, medium and high value of thresholds and the corresponding value of masking and boundaries are calculated to accurately differentiate pixels as foreground. The edge response, curvature, gradient are applied to find the true location of boundaries. Finally, IABC has been applied for detecting the shadow and median filter is used to remove the shadow. The results show improvement as compared to other existing methods

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Section: Figure 2 Umbra and Penumbra Formationmentioning

confidence: 99%

Optimization of Shadow Detection and Removal using Multilevel Thresholds and Improved Artificial Bee Colony Algorithm

Das¹,

Sudan²

2019

IJRTE

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“…In the literature [10], He et al put forward the MRF-based segmentation method with edge penalties and adaptive weight parameter aiming at solving the problem of fixed label factor β. To settle the problem of 'hard' segmentation, fuzzy MRF models were proposed for SAR image segmentation [11][12][13]. Owing to its powerful ability to represent the directional information of image, multi-scale analysis methods have been applied in SAR image segmentation as well [14], especially Contourlet transform [15,16].…”

Section: Introductionmentioning

confidence: 99%

SAR image segmentation algorithm based on Contourlet domain AFMRF model

Wang

Shi

2018

IET image process

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Combining the advantage of Contourlet transform with adaptive fuzzy Markov random field (MRF) model, a novel segmentation algorithm is proposed in this study to achieve precise and continuous division for synthetic aperture radar (SAR) image. The classical MRF model is modified to obtain the edge and process adaptive label factor. The adaptive fuzzy MRF (AFMRF) model is proposed to equilibrate the smooth regions segmentation with texture regions segmentation. At the same time, Contourlet domain hidden Markov tree (HMT) model is introduced to perform multi-scale directional filtering and intra-scale training on the coefficients of SAR images to achieve precise texture segmentation at each scale. Finally, the AFMRF model is integrated into inter-scale and intra-scale HMT training results and the segmented image can be obtained. To verify the validity of the proposed algorithm, several SAR images are experimented and compared with the state-of-the-art algorithms. The experimental results and analysis show that the proposed algorithm can achieve better results on noise suppression, smoothness of target regions, precise and continuous segmentation of fuzzy texture.

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Concept of Hybrid Models in Background Subtraction: A Review of Recent Trends

Maurya

Choudhry

2021

Proceedings of Data Analytics and Management

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Integration of fuzzy Markov random field and local information for separation of moving objects and shadows

Cited by 10 publications

References 36 publications

Optimization of Shadow Detection and Removal using Multilevel Thresholds and Improved Artificial Bee Colony Algorithm

Optimization of Shadow Detection and Removal using Multilevel Thresholds and Improved Artificial Bee Colony Algorithm

SAR image segmentation algorithm based on Contourlet domain AFMRF model

Concept of Hybrid Models in Background Subtraction: A Review of Recent Trends

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