An efficient optimal multilevel image thresholding with electromagnetism-like mechanism

Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like me...
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Autor*in:	Bhandari, Ashish Kumar [verfasserIn] Singh, Neha [verfasserIn] Shubham, Swapnil [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Image segmentation Electromagnetism-like mechanism Renyi’s entropy Tsallis entropy Kapur’s entropy

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 78(2019), 24 vom: 05. Okt., Seite 35733-35788
Übergeordnetes Werk:	volume:78 ; year:2019 ; number:24 ; day:05 ; month:10 ; pages:35733-35788

Links:	Volltext

DOI / URN:	10.1007/s11042-019-08195-8

Katalog-ID:	SPR01603306X

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520			\|a Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.
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700	1		\|a Singh, Neha \|e verfasserin \|4 aut
700	1		\|a Shubham, Swapnil \|e verfasserin \|4 aut
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allfields	10.1007/s11042-019-08195-8 doi (DE-627)SPR01603306X (SPR)s11042-019-08195-8-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Bhandari, Ashish Kumar verfasserin aut An efficient optimal multilevel image thresholding with electromagnetism-like mechanism 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations. Image segmentation (dpeaa)DE-He213 Electromagnetism-like mechanism (dpeaa)DE-He213 Renyi’s entropy (dpeaa)DE-He213 Tsallis entropy (dpeaa)DE-He213 Kapur’s entropy (dpeaa)DE-He213 Singh, Neha verfasserin aut Shubham, Swapnil verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2019), 24 vom: 05. Okt., Seite 35733-35788 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2019 number:24 day:05 month:10 pages:35733-35788 https://dx.doi.org/10.1007/s11042-019-08195-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 78 2019 24 05 10 35733-35788
spelling	10.1007/s11042-019-08195-8 doi (DE-627)SPR01603306X (SPR)s11042-019-08195-8-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Bhandari, Ashish Kumar verfasserin aut An efficient optimal multilevel image thresholding with electromagnetism-like mechanism 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations. Image segmentation (dpeaa)DE-He213 Electromagnetism-like mechanism (dpeaa)DE-He213 Renyi’s entropy (dpeaa)DE-He213 Tsallis entropy (dpeaa)DE-He213 Kapur’s entropy (dpeaa)DE-He213 Singh, Neha verfasserin aut Shubham, Swapnil verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2019), 24 vom: 05. Okt., Seite 35733-35788 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2019 number:24 day:05 month:10 pages:35733-35788 https://dx.doi.org/10.1007/s11042-019-08195-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 78 2019 24 05 10 35733-35788
allfields_unstemmed	10.1007/s11042-019-08195-8 doi (DE-627)SPR01603306X (SPR)s11042-019-08195-8-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Bhandari, Ashish Kumar verfasserin aut An efficient optimal multilevel image thresholding with electromagnetism-like mechanism 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations. Image segmentation (dpeaa)DE-He213 Electromagnetism-like mechanism (dpeaa)DE-He213 Renyi’s entropy (dpeaa)DE-He213 Tsallis entropy (dpeaa)DE-He213 Kapur’s entropy (dpeaa)DE-He213 Singh, Neha verfasserin aut Shubham, Swapnil verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2019), 24 vom: 05. Okt., Seite 35733-35788 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2019 number:24 day:05 month:10 pages:35733-35788 https://dx.doi.org/10.1007/s11042-019-08195-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 78 2019 24 05 10 35733-35788
allfieldsGer	10.1007/s11042-019-08195-8 doi (DE-627)SPR01603306X (SPR)s11042-019-08195-8-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Bhandari, Ashish Kumar verfasserin aut An efficient optimal multilevel image thresholding with electromagnetism-like mechanism 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations. Image segmentation (dpeaa)DE-He213 Electromagnetism-like mechanism (dpeaa)DE-He213 Renyi’s entropy (dpeaa)DE-He213 Tsallis entropy (dpeaa)DE-He213 Kapur’s entropy (dpeaa)DE-He213 Singh, Neha verfasserin aut Shubham, Swapnil verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2019), 24 vom: 05. Okt., Seite 35733-35788 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2019 number:24 day:05 month:10 pages:35733-35788 https://dx.doi.org/10.1007/s11042-019-08195-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 78 2019 24 05 10 35733-35788
allfieldsSound	10.1007/s11042-019-08195-8 doi (DE-627)SPR01603306X (SPR)s11042-019-08195-8-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Bhandari, Ashish Kumar verfasserin aut An efficient optimal multilevel image thresholding with electromagnetism-like mechanism 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations. Image segmentation (dpeaa)DE-He213 Electromagnetism-like mechanism (dpeaa)DE-He213 Renyi’s entropy (dpeaa)DE-He213 Tsallis entropy (dpeaa)DE-He213 Kapur’s entropy (dpeaa)DE-He213 Singh, Neha verfasserin aut Shubham, Swapnil verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2019), 24 vom: 05. Okt., Seite 35733-35788 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2019 number:24 day:05 month:10 pages:35733-35788 https://dx.doi.org/10.1007/s11042-019-08195-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 78 2019 24 05 10 35733-35788
language	English
source	Enthalten in Multimedia tools and applications 78(2019), 24 vom: 05. Okt., Seite 35733-35788 volume:78 year:2019 number:24 day:05 month:10 pages:35733-35788
sourceStr	Enthalten in Multimedia tools and applications 78(2019), 24 vom: 05. Okt., Seite 35733-35788 volume:78 year:2019 number:24 day:05 month:10 pages:35733-35788
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image segmentation Electromagnetism-like mechanism Renyi’s entropy Tsallis entropy Kapur’s entropy
dewey-raw	070
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Bhandari, Ashish Kumar @@aut@@ Singh, Neha @@aut@@ Shubham, Swapnil @@aut@@
publishDateDaySort_date	2019-10-05T00:00:00Z
hierarchy_top_id	27135030X
dewey-sort	270
id	SPR01603306X
language_de	englisch
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author	Bhandari, Ashish Kumar
spellingShingle	Bhandari, Ashish Kumar ddc 070 bkl 54.87 misc Image segmentation misc Electromagnetism-like mechanism misc Renyi’s entropy misc Tsallis entropy misc Kapur’s entropy An efficient optimal multilevel image thresholding with electromagnetism-like mechanism
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topic_title	070 004 ASE 54.87 bkl An efficient optimal multilevel image thresholding with electromagnetism-like mechanism Image segmentation (dpeaa)DE-He213 Electromagnetism-like mechanism (dpeaa)DE-He213 Renyi’s entropy (dpeaa)DE-He213 Tsallis entropy (dpeaa)DE-He213 Kapur’s entropy (dpeaa)DE-He213
topic	ddc 070 bkl 54.87 misc Image segmentation misc Electromagnetism-like mechanism misc Renyi’s entropy misc Tsallis entropy misc Kapur’s entropy
topic_unstemmed	ddc 070 bkl 54.87 misc Image segmentation misc Electromagnetism-like mechanism misc Renyi’s entropy misc Tsallis entropy misc Kapur’s entropy
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title	An efficient optimal multilevel image thresholding with electromagnetism-like mechanism
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title_full	An efficient optimal multilevel image thresholding with electromagnetism-like mechanism
author_sort	Bhandari, Ashish Kumar
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author_browse	Bhandari, Ashish Kumar Singh, Neha Shubham, Swapnil
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title_sort	efficient optimal multilevel image thresholding with electromagnetism-like mechanism
title_auth	An efficient optimal multilevel image thresholding with electromagnetism-like mechanism
abstract	Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.
abstractGer	Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.
abstract_unstemmed	Abstract Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.
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title_short	An efficient optimal multilevel image thresholding with electromagnetism-like mechanism
url	https://dx.doi.org/10.1007/s11042-019-08195-8
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author2	Singh, Neha Shubham, Swapnil
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up_date	2024-07-03T20:19:04.722Z
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Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. 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An efficient optimal multilevel image thresholding with electromagnetism-like mechanism

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