Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues
Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the gene...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pinoli, J-C [verfasserIn] Debayle, J [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2006 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2007(2006), 1 vom: 01. Dez. |
|---|---|
| Übergeordnetes Werk: |
volume:2007 ; year:2006 ; number:1 ; day:01 ; month:12 |
| Links: |
|---|
| DOI / URN: |
10.1155/2007/36105 |
|---|
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SPR031983960 |
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| 520 | |a Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications. | ||
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10.1155/2007/36105 doi (DE-627)SPR031983960 (SPR)36105-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Pinoli, J-C verfasserin aut Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications. Image Segmentation (dpeaa)DE-He213 Image Enhancement (dpeaa)DE-He213 Image Representation (dpeaa)DE-He213 Image Restoration (dpeaa)DE-He213 Imaging Application (dpeaa)DE-He213 Debayle, J verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2007(2006), 1 vom: 01. Dez. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2007 year:2006 number:1 day:01 month:12 https://dx.doi.org/10.1155/2007/36105 lizenzpflichtig 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_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2007 2006 1 01 12 |
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10.1155/2007/36105 doi (DE-627)SPR031983960 (SPR)36105-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Pinoli, J-C verfasserin aut Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications. Image Segmentation (dpeaa)DE-He213 Image Enhancement (dpeaa)DE-He213 Image Representation (dpeaa)DE-He213 Image Restoration (dpeaa)DE-He213 Imaging Application (dpeaa)DE-He213 Debayle, J verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2007(2006), 1 vom: 01. Dez. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2007 year:2006 number:1 day:01 month:12 https://dx.doi.org/10.1155/2007/36105 lizenzpflichtig 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_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2007 2006 1 01 12 |
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10.1155/2007/36105 doi (DE-627)SPR031983960 (SPR)36105-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Pinoli, J-C verfasserin aut Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications. Image Segmentation (dpeaa)DE-He213 Image Enhancement (dpeaa)DE-He213 Image Representation (dpeaa)DE-He213 Image Restoration (dpeaa)DE-He213 Imaging Application (dpeaa)DE-He213 Debayle, J verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2007(2006), 1 vom: 01. Dez. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2007 year:2006 number:1 day:01 month:12 https://dx.doi.org/10.1155/2007/36105 lizenzpflichtig 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_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2007 2006 1 01 12 |
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10.1155/2007/36105 doi (DE-627)SPR031983960 (SPR)36105-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Pinoli, J-C verfasserin aut Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications. Image Segmentation (dpeaa)DE-He213 Image Enhancement (dpeaa)DE-He213 Image Representation (dpeaa)DE-He213 Image Restoration (dpeaa)DE-He213 Imaging Application (dpeaa)DE-He213 Debayle, J verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2007(2006), 1 vom: 01. Dez. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2007 year:2006 number:1 day:01 month:12 https://dx.doi.org/10.1155/2007/36105 lizenzpflichtig 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_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2007 2006 1 01 12 |
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10.1155/2007/36105 doi (DE-627)SPR031983960 (SPR)36105-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Pinoli, J-C verfasserin aut Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications. Image Segmentation (dpeaa)DE-He213 Image Enhancement (dpeaa)DE-He213 Image Representation (dpeaa)DE-He213 Image Restoration (dpeaa)DE-He213 Imaging Application (dpeaa)DE-He213 Debayle, J verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2007(2006), 1 vom: 01. Dez. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2007 year:2006 number:1 day:01 month:12 https://dx.doi.org/10.1155/2007/36105 lizenzpflichtig 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_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2007 2006 1 01 12 |
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620 ASE 53.73 bkl Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues Image Segmentation (dpeaa)DE-He213 Image Enhancement (dpeaa)DE-He213 Image Representation (dpeaa)DE-He213 Image Restoration (dpeaa)DE-He213 Imaging Application (dpeaa)DE-He213 |
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Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues |
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Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications. |
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Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications. |
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Abstract A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications. |
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