The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
J P Burrows - One of the best experts on this subject based on the ideXlab platform.
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total ozone retrieval from gome uv spectral data using the Weighting Function doas approach
Atmospheric Chemistry and Physics, 2004Co-Authors: M Coldeweyegbers, M Weber, L N Lamsal, R De Beek, M Buchwitz, J P BurrowsAbstract:A new algorithm approach called Weighting Function Differential Optical Absorption Spectroscopy (WF- DOAS) is presented which has been developed to retrieve total ozone columns from nadir observations of the Global Ozone Monitoring Experiment. By fitting the vertically in- tegrated ozone Weighting Function rather than ozone cross- section to the sun-normalized radiances, a direct retrieval of vertical column amounts is possible. The new WFDOAS ap- proach takes into account the slant path wavelength modu- lation that is usually neglected in the standard DOAS ap- proach using single airmass factors. This paper focuses on the algorithm description and error analysis, while in a com- panion paper by Weber et al. (2004) a detailed validation with groundbased measurements is presented. For the first time several auxiliary quantities directly derived from the GOME spectral range such as cloud-top-height and cloud fraction (O2-A band) and effective albedo using the Lam- bertian Equivalent Reflectivity (LER) near 377 nm are used in combination as input to the ozone retrieval. In addition the varying ozone dependent contribution to the Raman cor- rection in scattered light known as Ring effect has been in- cluded. The molecular ozone filling-in that is accounted for in the new algorithm has the largest contribution to the im- proved total ozone results from WFDOAS compared to the operational product. The precision of the total ozone retrieval is estimated to be better than 3% for solar zenith angles be- low 80 .
J. S. Sahambi - One of the best experts on this subject based on the ideXlab platform.
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ICIP - Weighting Function in Random Walk based left ventricle segmentation
2011 18th IEEE International Conference on Image Processing, 2011Co-Authors: S. P. Dakua, J. S. SahambiAbstract:Cardiac Magnetic Resonance (CMR) image segmentation is a crucial step before physicians go for patient diagnoses, related image guided surgery or medical data visualization. Most of the existing algorithms are effective under certain circumstances. On the other hand, Random Walk approach is robust for image segmentation in every condition. Weighting Function plays an important role for a successful segmentation in the approach. In this work, an attempt has been made to study the behavior of the Weighting Function with respect to the intensity distribution in the object to be segmented. In this work, we present a Weighting Function viz. derivative of Gaussian, that is proved to yield better segmentation results while applying on ischemic CMR images, where objects are obscure. Virtuous results on CMR images describes the potential of the Weighting Function.
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Weighting Function in Random Walk based left ventricle segmentation
2011 18th IEEE International Conference on Image Processing, 2011Co-Authors: S. P. Dakua, J. S. SahambiAbstract:Cardiac Magnetic Resonance (CMR) image segmentation is a crucial step before physicians go for patient diagnoses, related image guided surgery or medical data visualization. Most of the existing algorithms are effective under certain circumstances. On the other hand, Random Walk approach is robust for image segmentation in every condition. Weighting Function plays an important role for a successful segmentation in the approach. In this work, an attempt has been made to study the behavior of the Weighting Function with respect to the intensity distribution in the object to be segmented. In this work, we present a Weighting Function viz. derivative of Gaussian, that is proved to yield better segmentation results while applying on ischemic CMR images, where objects are obscure. Virtuous results on CMR images describes the potential of the Weighting Function.
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LV Contour Extraction Using Difference of Gaussian Weighting Function and Random Walk Approach
2009 Annual IEEE India Conference, 2009Co-Authors: S. P. Dakua, J. S. SahambiAbstract:Image segmentation is the first step prior to any medical analysis. With the increase in modern disease variety, the images (specially cardiac magnetic resonance (CMR) images) to be segmented are found complex in nature. That might be due to noise, color geometry etc. Random walk method is proved to be good enough to this type of images. Simultaneously, it is robust noise and it does not require any pre-condition to perform. In the present paper we show the importance of Weighting Function, that is used in the method, on the algorithm output. This paper presents a new approach using difference of Gaussian (DoG) Weighting Function in the random walk method. We compare the frequently used Gaussian Weighting Function with DoG and show DoG to be the better one. Finally using DoG Weighting Function, the random walk method is performed on CMR data for left ventricle contour extraction. The result using DoG Weighting Function is found to be encouraging than that of Gaussian Weighting Function.
M Coldeweyegbers - One of the best experts on this subject based on the ideXlab platform.
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total ozone retrieval from gome uv spectral data using the Weighting Function doas approach
Atmospheric Chemistry and Physics, 2004Co-Authors: M Coldeweyegbers, M Weber, L N Lamsal, R De Beek, M Buchwitz, J P BurrowsAbstract:A new algorithm approach called Weighting Function Differential Optical Absorption Spectroscopy (WF- DOAS) is presented which has been developed to retrieve total ozone columns from nadir observations of the Global Ozone Monitoring Experiment. By fitting the vertically in- tegrated ozone Weighting Function rather than ozone cross- section to the sun-normalized radiances, a direct retrieval of vertical column amounts is possible. The new WFDOAS ap- proach takes into account the slant path wavelength modu- lation that is usually neglected in the standard DOAS ap- proach using single airmass factors. This paper focuses on the algorithm description and error analysis, while in a com- panion paper by Weber et al. (2004) a detailed validation with groundbased measurements is presented. For the first time several auxiliary quantities directly derived from the GOME spectral range such as cloud-top-height and cloud fraction (O2-A band) and effective albedo using the Lam- bertian Equivalent Reflectivity (LER) near 377 nm are used in combination as input to the ozone retrieval. In addition the varying ozone dependent contribution to the Raman cor- rection in scattered light known as Ring effect has been in- cluded. The molecular ozone filling-in that is accounted for in the new algorithm has the largest contribution to the im- proved total ozone results from WFDOAS compared to the operational product. The precision of the total ozone retrieval is estimated to be better than 3% for solar zenith angles be- low 80 .
Gregory J Tripoli - One of the best experts on this subject based on the ideXlab platform.
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foundations for statistical physical precipitation retrieval from passive microwave satellite measurements ii emission source and generalized Weighting Function properties of a time dependent cloud radiation model
Journal of Applied Meteorology, 1993Co-Authors: Alberto Mugnai, Eric A Smith, Gregory J TripoliAbstract:Abstract We present the second part of a study on the development of a framework for precipitation retrieval from space-based passive microwave measurements using a three-dimensional time-dependent cloud model to establish the microphysical setting. We first develop the theory needed to interpret the vertically distributed radiative sources and the emission-absorption-scattering processes responsible for the behavior of frequency-dependent top-of-atmosphere brightness temperatures TB's. This involves two distinct types of vertical Weighting Functions for the TB's: an emission-source weighing Function describing the origin of emitted radiation that eventually reaches a satellite radiometer, and a generalized Weighting Function describing emitted-scattered radiation undergoing no further interactions prior to interception by the radiometer. The Weighting-Function framework is used for an analysis of land-based precipitation processes within a hail-storm simulation originally described in Part I. The individ...
S. P. Dakua - One of the best experts on this subject based on the ideXlab platform.
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ICIP - Weighting Function in Random Walk based left ventricle segmentation
2011 18th IEEE International Conference on Image Processing, 2011Co-Authors: S. P. Dakua, J. S. SahambiAbstract:Cardiac Magnetic Resonance (CMR) image segmentation is a crucial step before physicians go for patient diagnoses, related image guided surgery or medical data visualization. Most of the existing algorithms are effective under certain circumstances. On the other hand, Random Walk approach is robust for image segmentation in every condition. Weighting Function plays an important role for a successful segmentation in the approach. In this work, an attempt has been made to study the behavior of the Weighting Function with respect to the intensity distribution in the object to be segmented. In this work, we present a Weighting Function viz. derivative of Gaussian, that is proved to yield better segmentation results while applying on ischemic CMR images, where objects are obscure. Virtuous results on CMR images describes the potential of the Weighting Function.
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Weighting Function in Random Walk based left ventricle segmentation
2011 18th IEEE International Conference on Image Processing, 2011Co-Authors: S. P. Dakua, J. S. SahambiAbstract:Cardiac Magnetic Resonance (CMR) image segmentation is a crucial step before physicians go for patient diagnoses, related image guided surgery or medical data visualization. Most of the existing algorithms are effective under certain circumstances. On the other hand, Random Walk approach is robust for image segmentation in every condition. Weighting Function plays an important role for a successful segmentation in the approach. In this work, an attempt has been made to study the behavior of the Weighting Function with respect to the intensity distribution in the object to be segmented. In this work, we present a Weighting Function viz. derivative of Gaussian, that is proved to yield better segmentation results while applying on ischemic CMR images, where objects are obscure. Virtuous results on CMR images describes the potential of the Weighting Function.
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LV Contour Extraction Using Difference of Gaussian Weighting Function and Random Walk Approach
2009 Annual IEEE India Conference, 2009Co-Authors: S. P. Dakua, J. S. SahambiAbstract:Image segmentation is the first step prior to any medical analysis. With the increase in modern disease variety, the images (specially cardiac magnetic resonance (CMR) images) to be segmented are found complex in nature. That might be due to noise, color geometry etc. Random walk method is proved to be good enough to this type of images. Simultaneously, it is robust noise and it does not require any pre-condition to perform. In the present paper we show the importance of Weighting Function, that is used in the method, on the algorithm output. This paper presents a new approach using difference of Gaussian (DoG) Weighting Function in the random walk method. We compare the frequently used Gaussian Weighting Function with DoG and show DoG to be the better one. Finally using DoG Weighting Function, the random walk method is performed on CMR data for left ventricle contour extraction. The result using DoG Weighting Function is found to be encouraging than that of Gaussian Weighting Function.
