Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image

Abstract A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimiz...
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Autor*in:	Qi, Dawei [verfasserIn] Zhang, Peng [verfasserIn] Zhang, Xuefei [verfasserIn] Jin, Xuejing [verfasserIn] Wu, Haijun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Cost Function Boundary Detection Active Contour Model Solve Optimization Problem Hopfield Neural Network

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2010(2010), 1 vom: 10. Juni
Übergeordnetes Werk:	volume:2010 ; year:2010 ; number:1 ; day:10 ; month:06

Links:	Volltext

DOI / URN:	10.1155/2010/427878

Katalog-ID:	SPR031994105

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allfields	10.1155/2010/427878 doi (DE-627)SPR031994105 (SPR)427878-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Qi, Dawei verfasserin aut Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods. Cost Function (dpeaa)DE-He213 Boundary Detection (dpeaa)DE-He213 Active Contour Model (dpeaa)DE-He213 Solve Optimization Problem (dpeaa)DE-He213 Hopfield Neural Network (dpeaa)DE-He213 Zhang, Peng verfasserin aut Zhang, Xuefei verfasserin aut Jin, Xuejing verfasserin aut Wu, Haijun verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 10. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:10 month:06 https://dx.doi.org/10.1155/2010/427878 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 10 06
spelling	10.1155/2010/427878 doi (DE-627)SPR031994105 (SPR)427878-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Qi, Dawei verfasserin aut Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods. Cost Function (dpeaa)DE-He213 Boundary Detection (dpeaa)DE-He213 Active Contour Model (dpeaa)DE-He213 Solve Optimization Problem (dpeaa)DE-He213 Hopfield Neural Network (dpeaa)DE-He213 Zhang, Peng verfasserin aut Zhang, Xuefei verfasserin aut Jin, Xuejing verfasserin aut Wu, Haijun verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 10. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:10 month:06 https://dx.doi.org/10.1155/2010/427878 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 10 06
allfields_unstemmed	10.1155/2010/427878 doi (DE-627)SPR031994105 (SPR)427878-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Qi, Dawei verfasserin aut Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods. Cost Function (dpeaa)DE-He213 Boundary Detection (dpeaa)DE-He213 Active Contour Model (dpeaa)DE-He213 Solve Optimization Problem (dpeaa)DE-He213 Hopfield Neural Network (dpeaa)DE-He213 Zhang, Peng verfasserin aut Zhang, Xuefei verfasserin aut Jin, Xuejing verfasserin aut Wu, Haijun verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 10. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:10 month:06 https://dx.doi.org/10.1155/2010/427878 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 10 06
allfieldsGer	10.1155/2010/427878 doi (DE-627)SPR031994105 (SPR)427878-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Qi, Dawei verfasserin aut Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods. Cost Function (dpeaa)DE-He213 Boundary Detection (dpeaa)DE-He213 Active Contour Model (dpeaa)DE-He213 Solve Optimization Problem (dpeaa)DE-He213 Hopfield Neural Network (dpeaa)DE-He213 Zhang, Peng verfasserin aut Zhang, Xuefei verfasserin aut Jin, Xuejing verfasserin aut Wu, Haijun verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 10. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:10 month:06 https://dx.doi.org/10.1155/2010/427878 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 10 06
allfieldsSound	10.1155/2010/427878 doi (DE-627)SPR031994105 (SPR)427878-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Qi, Dawei verfasserin aut Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods. Cost Function (dpeaa)DE-He213 Boundary Detection (dpeaa)DE-He213 Active Contour Model (dpeaa)DE-He213 Solve Optimization Problem (dpeaa)DE-He213 Hopfield Neural Network (dpeaa)DE-He213 Zhang, Peng verfasserin aut Zhang, Xuefei verfasserin aut Jin, Xuejing verfasserin aut Wu, Haijun verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 10. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:10 month:06 https://dx.doi.org/10.1155/2010/427878 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 10 06
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2010(2010), 1 vom: 10. Juni volume:2010 year:2010 number:1 day:10 month:06
sourceStr	Enthalten in EURASIP journal on advances in signal processing 2010(2010), 1 vom: 10. Juni volume:2010 year:2010 number:1 day:10 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cost Function Boundary Detection Active Contour Model Solve Optimization Problem Hopfield Neural Network
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container_title	EURASIP journal on advances in signal processing
authorswithroles_txt_mv	Qi, Dawei @@aut@@ Zhang, Peng @@aut@@ Zhang, Xuefei @@aut@@ Jin, Xuejing @@aut@@ Wu, Haijun @@aut@@
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author	Qi, Dawei
spellingShingle	Qi, Dawei ddc 620 bkl 53.73 misc Cost Function misc Boundary Detection misc Active Contour Model misc Solve Optimization Problem misc Hopfield Neural Network Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image
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topic_title	620 ASE 53.73 bkl Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image Cost Function (dpeaa)DE-He213 Boundary Detection (dpeaa)DE-He213 Active Contour Model (dpeaa)DE-He213 Solve Optimization Problem (dpeaa)DE-He213 Hopfield Neural Network (dpeaa)DE-He213
topic	ddc 620 bkl 53.73 misc Cost Function misc Boundary Detection misc Active Contour Model misc Solve Optimization Problem misc Hopfield Neural Network
topic_unstemmed	ddc 620 bkl 53.73 misc Cost Function misc Boundary Detection misc Active Contour Model misc Solve Optimization Problem misc Hopfield Neural Network
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title	Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image
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title_full	Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image
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title_sort	appling a novel cost function to hopfield neural network for defects boundaries detection of wood image
title_auth	Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image
abstract	Abstract A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods.
abstractGer	Abstract A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods.
abstract_unstemmed	Abstract A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods.
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title_short	Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image
url	https://dx.doi.org/10.1155/2010/427878
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