Enhancement of Textural Differences Based on Morphological Component Analysis
This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information usin...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jianning Chi [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
comparator enhancement methods texture-based image segmentation algorithms multidimensional feature space texture-based segmentation methods |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on image processing - New York, NY : Inst., 1992, 24(2015), 9, Seite 2671-2684 |
|---|---|
| Übergeordnetes Werk: |
volume:24 ; year:2015 ; number:9 ; pages:2671-2684 |
| Links: |
|---|
| DOI / URN: |
10.1109/TIP.2015.2427514 |
|---|
| Katalog-ID: |
OLC1959241443 |
|---|
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| 245 | 1 | 0 | |a Enhancement of Textural Differences Based on Morphological Component Analysis |
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| 520 | |a This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. | ||
| 650 | 4 | |a comparator enhancement methods | |
| 650 | 4 | |a segmentation | |
| 650 | 4 | |a texture-based image segmentation algorithms | |
| 650 | 4 | |a image texture | |
| 650 | 4 | |a image component | |
| 650 | 4 | |a texture features | |
| 650 | 4 | |a multidimensional feature space | |
| 650 | 4 | |a Image edge detection | |
| 650 | 4 | |a Dictionaries | |
| 650 | 4 | |a texture-enhanced image | |
| 650 | 4 | |a image decomposition | |
| 650 | 4 | |a image segmentation | |
| 650 | 4 | |a texture-based segmentation methods | |
| 650 | 4 | |a Optimized production technology | |
| 650 | 4 | |a texture | |
| 650 | 4 | |a texture enhancement method | |
| 650 | 4 | |a Visualization | |
| 650 | 4 | |a enhancement | |
| 650 | 4 | |a mathematical morphology | |
| 650 | 4 | |a textural differences | |
| 650 | 4 | |a texture information | |
| 650 | 4 | |a Noise | |
| 650 | 4 | |a non-linear transform | |
| 650 | 4 | |a Wavelet transforms | |
| 650 | 4 | |a texture component | |
| 650 | 4 | |a image enhancement | |
| 650 | 4 | |a morphological component analysis | |
| 700 | 1 | |a Eramian, Mark |4 oth | |
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10.1109/TIP.2015.2427514 doi PQ20160617 (DE-627)OLC1959241443 (DE-599)GBVOLC1959241443 (PRQ)c1533-b9f16eec4a9502001ff9959f679fd1feeeb95dfba1f2fee7691d060b1f24aecf0 (KEY)0213811520150000024000902671enhancementoftexturaldifferencesbasedonmorphologic DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Jianning Chi verfasserin aut Enhancement of Textural Differences Based on Morphological Component Analysis 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. comparator enhancement methods segmentation texture-based image segmentation algorithms image texture image component texture features multidimensional feature space Image edge detection Dictionaries texture-enhanced image image decomposition image segmentation texture-based segmentation methods Optimized production technology texture texture enhancement method Visualization enhancement mathematical morphology textural differences texture information Noise non-linear transform Wavelet transforms texture component image enhancement morphological component analysis Eramian, Mark oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 9, Seite 2671-2684 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:9 pages:2671-2684 http://dx.doi.org/10.1109/TIP.2015.2427514 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7097044 http://www.ncbi.nlm.nih.gov/pubmed/25935032 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 9 2671-2684 |
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10.1109/TIP.2015.2427514 doi PQ20160617 (DE-627)OLC1959241443 (DE-599)GBVOLC1959241443 (PRQ)c1533-b9f16eec4a9502001ff9959f679fd1feeeb95dfba1f2fee7691d060b1f24aecf0 (KEY)0213811520150000024000902671enhancementoftexturaldifferencesbasedonmorphologic DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Jianning Chi verfasserin aut Enhancement of Textural Differences Based on Morphological Component Analysis 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. comparator enhancement methods segmentation texture-based image segmentation algorithms image texture image component texture features multidimensional feature space Image edge detection Dictionaries texture-enhanced image image decomposition image segmentation texture-based segmentation methods Optimized production technology texture texture enhancement method Visualization enhancement mathematical morphology textural differences texture information Noise non-linear transform Wavelet transforms texture component image enhancement morphological component analysis Eramian, Mark oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 9, Seite 2671-2684 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:9 pages:2671-2684 http://dx.doi.org/10.1109/TIP.2015.2427514 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7097044 http://www.ncbi.nlm.nih.gov/pubmed/25935032 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 9 2671-2684 |
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10.1109/TIP.2015.2427514 doi PQ20160617 (DE-627)OLC1959241443 (DE-599)GBVOLC1959241443 (PRQ)c1533-b9f16eec4a9502001ff9959f679fd1feeeb95dfba1f2fee7691d060b1f24aecf0 (KEY)0213811520150000024000902671enhancementoftexturaldifferencesbasedonmorphologic DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Jianning Chi verfasserin aut Enhancement of Textural Differences Based on Morphological Component Analysis 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. comparator enhancement methods segmentation texture-based image segmentation algorithms image texture image component texture features multidimensional feature space Image edge detection Dictionaries texture-enhanced image image decomposition image segmentation texture-based segmentation methods Optimized production technology texture texture enhancement method Visualization enhancement mathematical morphology textural differences texture information Noise non-linear transform Wavelet transforms texture component image enhancement morphological component analysis Eramian, Mark oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 9, Seite 2671-2684 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:9 pages:2671-2684 http://dx.doi.org/10.1109/TIP.2015.2427514 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7097044 http://www.ncbi.nlm.nih.gov/pubmed/25935032 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 9 2671-2684 |
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10.1109/TIP.2015.2427514 doi PQ20160617 (DE-627)OLC1959241443 (DE-599)GBVOLC1959241443 (PRQ)c1533-b9f16eec4a9502001ff9959f679fd1feeeb95dfba1f2fee7691d060b1f24aecf0 (KEY)0213811520150000024000902671enhancementoftexturaldifferencesbasedonmorphologic DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Jianning Chi verfasserin aut Enhancement of Textural Differences Based on Morphological Component Analysis 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. comparator enhancement methods segmentation texture-based image segmentation algorithms image texture image component texture features multidimensional feature space Image edge detection Dictionaries texture-enhanced image image decomposition image segmentation texture-based segmentation methods Optimized production technology texture texture enhancement method Visualization enhancement mathematical morphology textural differences texture information Noise non-linear transform Wavelet transforms texture component image enhancement morphological component analysis Eramian, Mark oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 9, Seite 2671-2684 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:9 pages:2671-2684 http://dx.doi.org/10.1109/TIP.2015.2427514 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7097044 http://www.ncbi.nlm.nih.gov/pubmed/25935032 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 9 2671-2684 |
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10.1109/TIP.2015.2427514 doi PQ20160617 (DE-627)OLC1959241443 (DE-599)GBVOLC1959241443 (PRQ)c1533-b9f16eec4a9502001ff9959f679fd1feeeb95dfba1f2fee7691d060b1f24aecf0 (KEY)0213811520150000024000902671enhancementoftexturaldifferencesbasedonmorphologic DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Jianning Chi verfasserin aut Enhancement of Textural Differences Based on Morphological Component Analysis 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. comparator enhancement methods segmentation texture-based image segmentation algorithms image texture image component texture features multidimensional feature space Image edge detection Dictionaries texture-enhanced image image decomposition image segmentation texture-based segmentation methods Optimized production technology texture texture enhancement method Visualization enhancement mathematical morphology textural differences texture information Noise non-linear transform Wavelet transforms texture component image enhancement morphological component analysis Eramian, Mark oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 9, Seite 2671-2684 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:9 pages:2671-2684 http://dx.doi.org/10.1109/TIP.2015.2427514 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7097044 http://www.ncbi.nlm.nih.gov/pubmed/25935032 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 9 2671-2684 |
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Enthalten in IEEE transactions on image processing 24(2015), 9, Seite 2671-2684 volume:24 year:2015 number:9 pages:2671-2684 |
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comparator enhancement methods segmentation texture-based image segmentation algorithms image texture image component texture features multidimensional feature space Image edge detection Dictionaries texture-enhanced image image decomposition image segmentation texture-based segmentation methods Optimized production technology texture texture enhancement method Visualization enhancement mathematical morphology textural differences texture information Noise non-linear transform Wavelet transforms texture component image enhancement morphological component analysis |
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Jianning Chi @@aut@@ Eramian, Mark @@oth@@ |
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Jianning Chi ddc 004 bkl 54.00 misc comparator enhancement methods misc segmentation misc texture-based image segmentation algorithms misc image texture misc image component misc texture features misc multidimensional feature space misc Image edge detection misc Dictionaries misc texture-enhanced image misc image decomposition misc image segmentation misc texture-based segmentation methods misc Optimized production technology misc texture misc texture enhancement method misc Visualization misc enhancement misc mathematical morphology misc textural differences misc texture information misc Noise misc non-linear transform misc Wavelet transforms misc texture component misc image enhancement misc morphological component analysis Enhancement of Textural Differences Based on Morphological Component Analysis |
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004 620 DNB 54.00 bkl Enhancement of Textural Differences Based on Morphological Component Analysis comparator enhancement methods segmentation texture-based image segmentation algorithms image texture image component texture features multidimensional feature space Image edge detection Dictionaries texture-enhanced image image decomposition image segmentation texture-based segmentation methods Optimized production technology texture texture enhancement method Visualization enhancement mathematical morphology textural differences texture information Noise non-linear transform Wavelet transforms texture component image enhancement morphological component analysis |
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ddc 004 bkl 54.00 misc comparator enhancement methods misc segmentation misc texture-based image segmentation algorithms misc image texture misc image component misc texture features misc multidimensional feature space misc Image edge detection misc Dictionaries misc texture-enhanced image misc image decomposition misc image segmentation misc texture-based segmentation methods misc Optimized production technology misc texture misc texture enhancement method misc Visualization misc enhancement misc mathematical morphology misc textural differences misc texture information misc Noise misc non-linear transform misc Wavelet transforms misc texture component misc image enhancement misc morphological component analysis |
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Enhancement of Textural Differences Based on Morphological Component Analysis |
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This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. |
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This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. |
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This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. |
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Enhancement of Textural Differences Based on Morphological Component Analysis |
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