The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection
In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the doma...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hait, Swati Rani [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: Prediction of livestock manure and mixture higher heating value based on fundamental analysis - Choi, Hong L. ELSEVIER, 2013, an international journal on multi-sensor, multi-source information fusion, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:80 ; year:2022 ; pages:226-240 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.inffus.2021.11.002 |
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ELV05640476X |
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| 245 | 1 | 4 | |a The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection |
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| 520 | |a In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. | ||
| 520 | |a In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. | ||
| 650 | 7 | |a Feature image |2 Elsevier | |
| 650 | 7 | |a Bonferroni mean-type pre-aggregation operator |2 Elsevier | |
| 650 | 7 | |a Pre-aggregation operator |2 Elsevier | |
| 650 | 7 | |a Image processing |2 Elsevier | |
| 650 | 7 | |a Aggregation operator |2 Elsevier | |
| 650 | 7 | |a Directional monotonicity |2 Elsevier | |
| 650 | 7 | |a Edge detection |2 Elsevier | |
| 700 | 1 | |a Mesiar, Radko |4 oth | |
| 700 | 1 | |a Gupta, Pragya |4 oth | |
| 700 | 1 | |a Guha, Debashree |4 oth | |
| 700 | 1 | |a Chakraborty, Debjani |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Choi, Hong L. ELSEVIER |t Prediction of livestock manure and mixture higher heating value based on fundamental analysis |d 2013 |d an international journal on multi-sensor, multi-source information fusion |g Amsterdam [u.a.] |w (DE-627)ELV003322297 |
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10.1016/j.inffus.2021.11.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001634.pica (DE-627)ELV05640476X (ELSEVIER)S1566-2535(21)00226-8 DE-627 ger DE-627 rakwb eng 660 VZ 58.21 bkl Hait, Swati Rani verfasserin aut The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection 2022transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. Feature image Elsevier Bonferroni mean-type pre-aggregation operator Elsevier Pre-aggregation operator Elsevier Image processing Elsevier Aggregation operator Elsevier Directional monotonicity Elsevier Edge detection Elsevier Mesiar, Radko oth Gupta, Pragya oth Guha, Debashree oth Chakraborty, Debjani oth Enthalten in Elsevier Science Choi, Hong L. ELSEVIER Prediction of livestock manure and mixture higher heating value based on fundamental analysis 2013 an international journal on multi-sensor, multi-source information fusion Amsterdam [u.a.] (DE-627)ELV003322297 volume:80 year:2022 pages:226-240 extent:15 https://doi.org/10.1016/j.inffus.2021.11.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.21 Brennstoffe Kraftstoffe Explosivstoffe VZ AR 80 2022 226-240 15 |
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10.1016/j.inffus.2021.11.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001634.pica (DE-627)ELV05640476X (ELSEVIER)S1566-2535(21)00226-8 DE-627 ger DE-627 rakwb eng 660 VZ 58.21 bkl Hait, Swati Rani verfasserin aut The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection 2022transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. Feature image Elsevier Bonferroni mean-type pre-aggregation operator Elsevier Pre-aggregation operator Elsevier Image processing Elsevier Aggregation operator Elsevier Directional monotonicity Elsevier Edge detection Elsevier Mesiar, Radko oth Gupta, Pragya oth Guha, Debashree oth Chakraborty, Debjani oth Enthalten in Elsevier Science Choi, Hong L. ELSEVIER Prediction of livestock manure and mixture higher heating value based on fundamental analysis 2013 an international journal on multi-sensor, multi-source information fusion Amsterdam [u.a.] (DE-627)ELV003322297 volume:80 year:2022 pages:226-240 extent:15 https://doi.org/10.1016/j.inffus.2021.11.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.21 Brennstoffe Kraftstoffe Explosivstoffe VZ AR 80 2022 226-240 15 |
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10.1016/j.inffus.2021.11.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001634.pica (DE-627)ELV05640476X (ELSEVIER)S1566-2535(21)00226-8 DE-627 ger DE-627 rakwb eng 660 VZ 58.21 bkl Hait, Swati Rani verfasserin aut The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection 2022transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. Feature image Elsevier Bonferroni mean-type pre-aggregation operator Elsevier Pre-aggregation operator Elsevier Image processing Elsevier Aggregation operator Elsevier Directional monotonicity Elsevier Edge detection Elsevier Mesiar, Radko oth Gupta, Pragya oth Guha, Debashree oth Chakraborty, Debjani oth Enthalten in Elsevier Science Choi, Hong L. ELSEVIER Prediction of livestock manure and mixture higher heating value based on fundamental analysis 2013 an international journal on multi-sensor, multi-source information fusion Amsterdam [u.a.] (DE-627)ELV003322297 volume:80 year:2022 pages:226-240 extent:15 https://doi.org/10.1016/j.inffus.2021.11.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.21 Brennstoffe Kraftstoffe Explosivstoffe VZ AR 80 2022 226-240 15 |
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10.1016/j.inffus.2021.11.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001634.pica (DE-627)ELV05640476X (ELSEVIER)S1566-2535(21)00226-8 DE-627 ger DE-627 rakwb eng 660 VZ 58.21 bkl Hait, Swati Rani verfasserin aut The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection 2022transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. Feature image Elsevier Bonferroni mean-type pre-aggregation operator Elsevier Pre-aggregation operator Elsevier Image processing Elsevier Aggregation operator Elsevier Directional monotonicity Elsevier Edge detection Elsevier Mesiar, Radko oth Gupta, Pragya oth Guha, Debashree oth Chakraborty, Debjani oth Enthalten in Elsevier Science Choi, Hong L. ELSEVIER Prediction of livestock manure and mixture higher heating value based on fundamental analysis 2013 an international journal on multi-sensor, multi-source information fusion Amsterdam [u.a.] (DE-627)ELV003322297 volume:80 year:2022 pages:226-240 extent:15 https://doi.org/10.1016/j.inffus.2021.11.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.21 Brennstoffe Kraftstoffe Explosivstoffe VZ AR 80 2022 226-240 15 |
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10.1016/j.inffus.2021.11.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001634.pica (DE-627)ELV05640476X (ELSEVIER)S1566-2535(21)00226-8 DE-627 ger DE-627 rakwb eng 660 VZ 58.21 bkl Hait, Swati Rani verfasserin aut The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection 2022transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. Feature image Elsevier Bonferroni mean-type pre-aggregation operator Elsevier Pre-aggregation operator Elsevier Image processing Elsevier Aggregation operator Elsevier Directional monotonicity Elsevier Edge detection Elsevier Mesiar, Radko oth Gupta, Pragya oth Guha, Debashree oth Chakraborty, Debjani oth Enthalten in Elsevier Science Choi, Hong L. ELSEVIER Prediction of livestock manure and mixture higher heating value based on fundamental analysis 2013 an international journal on multi-sensor, multi-source information fusion Amsterdam [u.a.] (DE-627)ELV003322297 volume:80 year:2022 pages:226-240 extent:15 https://doi.org/10.1016/j.inffus.2021.11.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.21 Brennstoffe Kraftstoffe Explosivstoffe VZ AR 80 2022 226-240 15 |
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The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection |
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In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. |
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In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. |
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In recent years, immense interest in the exploration of the generalized version of the monotonicity condition has been significantly accomplished by the researchers. The intention behind generalizing the monotonicity condition is to envelop many prime functions which are of huge interest in the domain of mathematical applications such as classification problems, image processing, decision-making systems, etc. In this regard, the framework of the pre-aggregation operators was introduced to generalize the notion of monotonicity in the traditionally defined concept of aggregation operators. Such functions have extended the group of operators utilized for information accumulation by considering directional monotonicity with respect to a specified vector. This study emphasizes the systematized exploration of the theoretical framework of the Bonferroni mean-type (BM-type) pre-aggregation operators. We propose the construction methodology of the BM-type pre-aggregation operators by suitably befitting preferable functions to provide a descriptive arrangement, which is quite adaptable, understandable, and interpretable. First, a construction mechanism is proposed by utilizing a bivariate function M . To enhance the potentiality of the proposed operator, a generalized variation of it has been proposed by suitably using two functions M and M ∗ , respectively. The primary step for an object recognition problem is edge detection and is considered as an important tool in image processing systems. For the applicatory purpose, an edge detection algorithm based on the proposed BM-type pre-aggregation operator has been presented with more emphasis given to the feature image extraction. A comprehensive comparative study has been made to assess the results obtained through the proposed edge detection algorithm with some other well-known edge detectors extensively utilized in the literature. |
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