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Ak Nandi - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    Fuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms.NaTional NaTural Science FoundaTion of China (GranT Number: GranT 61871259, GranT 61861024); NaTural Science Basic Research Program of Shaanxi (GranT Number: 2021JC-47); Key Research and DevelopmenT Program of Shaanxi (GranT Number: 2021ZDLGY08-07)

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    This arTicle has been accepTed for publicaTion in a fuTure issue of This journal, buT has noT been fully ediTed. ConTenT may change prior To final publicaTion. CiTaTion informaTion: DOI 10.1109/TFUZZ.2021.3099560, IEEE TransacTions on Fuzzy SysTemsFuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms

Lei T - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    Fuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms.NaTional NaTural Science FoundaTion of China (GranT Number: GranT 61871259, GranT 61861024); NaTural Science Basic Research Program of Shaanxi (GranT Number: 2021JC-47); Key Research and DevelopmenT Program of Shaanxi (GranT Number: 2021ZDLGY08-07)

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    This arTicle has been accepTed for publicaTion in a fuTure issue of This journal, buT has noT been fully ediTed. ConTenT may change prior To final publicaTion. CiTaTion informaTion: DOI 10.1109/TFUZZ.2021.3099560, IEEE TransacTions on Fuzzy SysTemsFuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms

Jia X - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    Fuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms.NaTional NaTural Science FoundaTion of China (GranT Number: GranT 61871259, GranT 61861024); NaTural Science Basic Research Program of Shaanxi (GranT Number: 2021JC-47); Key Research and DevelopmenT Program of Shaanxi (GranT Number: 2021ZDLGY08-07)

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    This arTicle has been accepTed for publicaTion in a fuTure issue of This journal, buT has noT been fully ediTed. ConTenT may change prior To final publicaTion. CiTaTion informaTion: DOI 10.1109/TFUZZ.2021.3099560, IEEE TransacTions on Fuzzy SysTemsFuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms

Xue D - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    Fuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms.NaTional NaTural Science FoundaTion of China (GranT Number: GranT 61871259, GranT 61861024); NaTural Science Basic Research Program of Shaanxi (GranT Number: 2021JC-47); Key Research and DevelopmenT Program of Shaanxi (GranT Number: 2021ZDLGY08-07)

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    This arTicle has been accepTed for publicaTion in a fuTure issue of This journal, buT has noT been fully ediTed. ConTenT may change prior To final publicaTion. CiTaTion informaTion: DOI 10.1109/TFUZZ.2021.3099560, IEEE TransacTions on Fuzzy SysTemsFuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms

Wang Q - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    Fuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms.NaTional NaTural Science FoundaTion of China (GranT Number: GranT 61871259, GranT 61861024); NaTural Science Basic Research Program of Shaanxi (GranT Number: 2021JC-47); Key Research and DevelopmenT Program of Shaanxi (GranT Number: 2021ZDLGY08-07)

  • Fuzzy STudenT’s T-DisTribuTion Model Based on Richer SpaTial CombinaTion
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Lei T, Jia X, Xue D, Wang Q, Meng H, Ak Nandi
    Abstract:

    This arTicle has been accepTed for publicaTion in a fuTure issue of This journal, buT has noT been fully ediTed. ConTenT may change prior To final publicaTion. CiTaTion informaTion: DOI 10.1109/TFUZZ.2021.3099560, IEEE TransacTions on Fuzzy SysTemsFuzzy c-means (FCM) algoriThms wiTh spaTial informaTion have been widely applied in The field of image segmenTaTion. However, mosT of Them suffer from Two challenges. One is ThaT inTroducTion of fixed or adapTive single neighboring informaTion wiTh narrow recepTive field limiTs conTexTual consTrainTs leading To cluTTer segmenTaTions. The oTher is ThaT incorporaTion of superpixels wiTh wide recepTive field enlarges spaTial coherency leading To block effecTs. To address These challenges, we propose fuzzy STudenTs T-disTribuTion model based on richer spaTial combinaTion (FRSC) for image segmenTaTion. In This Paper, we make Two significanT conTribuTions. The firsT is ThaT boTh narrow and wide recepTive fields are inTegraTed inTo The objecTive funcTion of FRSC, which is convenienT To mine image feaTures and disTinguish local difference. The second is ThaT The rich spaTial combinaTion under STudenTs T-disTribuTion ensures ThaT spaTial informaTion is inTroduced inTo The updaTed parameTers of FRSC,which is helpful in finding a balance beTween The noise-immuniTy and deTail-preservaTion. ExperimenTal resulTs on synTheTic and publicly available images, furTher demonsTraTe ThaT The proposed FRSC addresses successfully The limiTaTions of FCM algoriThms wiTh spaTial informaTion and provides beTTer segmenTaTion resulTs Than sTaTe-of-The-arT clusTering algoriThms

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