The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Chandra Kambhamettu - One of the best experts on this subject based on the ideXlab platform.
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single image Vignetting correction from gradient distribution symmetries
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Yuanjie Zheng, Sing Bing Kang, Stephen Lin, Rui Xiao, James C Gee, Chandra KambhamettuAbstract:We present novel techniques for single-image Vignetting correction based on symmetries of two forms of image gradients: semicircular tangential gradients (SCTG) and radial gradients (RG). For a given image pixel, an SCTG is an image gradient along the tangential direction of a circle centered at the presumed optical center and passing through the pixel. An RG is an image gradient along the radial direction with respect to the optical center. We observe that the symmetry properties of SCTG and RG distributions are closely related to the Vignetting in the image. Based on these symmetry properties, we develop an automatic optical center estimation algorithm by minimizing the asymmetry of SCTG distributions, and also present two methods for Vignetting estimation based on minimizing the asymmetry of RG distributions. In comparison to prior approaches to single-image Vignetting correction, our methods do not rely on image segmentation and they produce more accurate results. Experiments show our techniques to work well for a wide range of images while achieving a speed-up of 3-5 times compared to a state-of-the-art method.
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single image Vignetting correction
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009Co-Authors: Yuanjie Zheng, Chandra Kambhamettu, Stephen Lin, Sing Bing KangAbstract:In this paper, we propose a method for robustly determining the Vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract Vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for Vignetting estimation. Within each image region, our method capitalizes on the frequency characteristics and physical properties of Vignetting to distinguish it from other sources of intensity variation. Rejection of outlier pixels is applied to improve the robustness of Vignetting estimation. Comprehensive experiments demonstrate the effectiveness of this technique on a broad range of images with both simulated and natural Vignetting effects. Causes of failures using the proposed algorithm are also analyzed.
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Single-image optical center estimation from Vignetting and tangential gradient symmetry
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009Co-Authors: Yuanjie Zheng, Chandra KambhamettuAbstract:In this paper, we propose a method for estimating the optical center of a camera given only a single image with Vignetting. This is accomplished by identifying the center of the Vignetting effect in the image through an analysis of semicircular tangential gradients (SCTGs). For a given image pixel, the SCTG is the image gradient along the tangential direction of the circle centered at the currently estimated optical center and passing through the pixel. We show that for natural images with Vignetting, the distribution of SCTGs is generally symmetric if the optical center is estimated accurately, but is skewed otherwise. By minimizing the asymmetry of the SCTG distribution with nonlinear optimization, our method is able to obtain reliable estimates of the optical center. Experiments on simulated and real Vignetting images demonstrate the effectiveness of this technique.
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Single-image Vignetting correction using radial gradient symmetry
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008Co-Authors: Yuanjie Zheng, Sing Bing Kang, Jingyi Yu, Chandra KambhamettuAbstract:In this paper, we present a novel single-image Vignetting method based on the symmetric distribution of the radial gradient (RG). The radial gradient is the image gradient along the radial direction with respect to the image center. We show that the RG distribution for natural images without Vignetting is generally symmetric. However, this distribution is skewed by Vignetting. We develop two variants of this technique, both of which remove Vignetting by minimizing asymmetry of the RG distribution. Compared with prior approaches to single-image Vignetting correction, our method does not require segmentation and the results are generally better. Experiments show our technique works for a wide range of images and it achieves a speed-up of 4-5 times compared with a state-of-the-art method.
Sing Bing Kang - One of the best experts on this subject based on the ideXlab platform.
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single image Vignetting correction from gradient distribution symmetries
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Yuanjie Zheng, Sing Bing Kang, Stephen Lin, Rui Xiao, James C Gee, Chandra KambhamettuAbstract:We present novel techniques for single-image Vignetting correction based on symmetries of two forms of image gradients: semicircular tangential gradients (SCTG) and radial gradients (RG). For a given image pixel, an SCTG is an image gradient along the tangential direction of a circle centered at the presumed optical center and passing through the pixel. An RG is an image gradient along the radial direction with respect to the optical center. We observe that the symmetry properties of SCTG and RG distributions are closely related to the Vignetting in the image. Based on these symmetry properties, we develop an automatic optical center estimation algorithm by minimizing the asymmetry of SCTG distributions, and also present two methods for Vignetting estimation based on minimizing the asymmetry of RG distributions. In comparison to prior approaches to single-image Vignetting correction, our methods do not rely on image segmentation and they produce more accurate results. Experiments show our techniques to work well for a wide range of images while achieving a speed-up of 3-5 times compared to a state-of-the-art method.
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single image Vignetting correction
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009Co-Authors: Yuanjie Zheng, Chandra Kambhamettu, Stephen Lin, Sing Bing KangAbstract:In this paper, we propose a method for robustly determining the Vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract Vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for Vignetting estimation. Within each image region, our method capitalizes on the frequency characteristics and physical properties of Vignetting to distinguish it from other sources of intensity variation. Rejection of outlier pixels is applied to improve the robustness of Vignetting estimation. Comprehensive experiments demonstrate the effectiveness of this technique on a broad range of images with both simulated and natural Vignetting effects. Causes of failures using the proposed algorithm are also analyzed.
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Single-image Vignetting correction using radial gradient symmetry
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008Co-Authors: Yuanjie Zheng, Sing Bing Kang, Jingyi Yu, Chandra KambhamettuAbstract:In this paper, we present a novel single-image Vignetting method based on the symmetric distribution of the radial gradient (RG). The radial gradient is the image gradient along the radial direction with respect to the image center. We show that the RG distribution for natural images without Vignetting is generally symmetric. However, this distribution is skewed by Vignetting. We develop two variants of this technique, both of which remove Vignetting by minimizing asymmetry of the RG distribution. Compared with prior approaches to single-image Vignetting correction, our method does not require segmentation and the results are generally better. Experiments show our technique works for a wide range of images and it achieves a speed-up of 4-5 times compared with a state-of-the-art method.
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single image Vignetting correction
Computer Vision and Pattern Recognition, 2006Co-Authors: Yuanjie Zheng, Stephen Lin, Sing Bing KangAbstract:In this paper, we propose a method for determining the Vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract Vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for Vignetting estimation. Within each image region, our method capitalizes on frequency characteristics and physical properties of Vignetting to distinguish it from other sources of intensity variation. The Vignetting data acquired from regions are weighted according to a presented reliability measure to promote robustness in estimation. Comprehensive experiments demonstrate the effectiveness of this technique on a broad range of images.
Yuanjie Zheng - One of the best experts on this subject based on the ideXlab platform.
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single image Vignetting correction
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009Co-Authors: Yuanjie Zheng, Chandra Kambhamettu, Stephen Lin, Sing Bing KangAbstract:In this paper, we propose a method for robustly determining the Vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract Vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for Vignetting estimation. Within each image region, our method capitalizes on the frequency characteristics and physical properties of Vignetting to distinguish it from other sources of intensity variation. Rejection of outlier pixels is applied to improve the robustness of Vignetting estimation. Comprehensive experiments demonstrate the effectiveness of this technique on a broad range of images with both simulated and natural Vignetting effects. Causes of failures using the proposed algorithm are also analyzed.
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Single-image optical center estimation from Vignetting and tangential gradient symmetry
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009Co-Authors: Yuanjie Zheng, Chandra KambhamettuAbstract:In this paper, we propose a method for estimating the optical center of a camera given only a single image with Vignetting. This is accomplished by identifying the center of the Vignetting effect in the image through an analysis of semicircular tangential gradients (SCTGs). For a given image pixel, the SCTG is the image gradient along the tangential direction of the circle centered at the currently estimated optical center and passing through the pixel. We show that for natural images with Vignetting, the distribution of SCTGs is generally symmetric if the optical center is estimated accurately, but is skewed otherwise. By minimizing the asymmetry of the SCTG distribution with nonlinear optimization, our method is able to obtain reliable estimates of the optical center. Experiments on simulated and real Vignetting images demonstrate the effectiveness of this technique.
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Single-image Vignetting correction using radial gradient symmetry
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008Co-Authors: Yuanjie Zheng, Sing Bing Kang, Jingyi Yu, Chandra KambhamettuAbstract:In this paper, we present a novel single-image Vignetting method based on the symmetric distribution of the radial gradient (RG). The radial gradient is the image gradient along the radial direction with respect to the image center. We show that the RG distribution for natural images without Vignetting is generally symmetric. However, this distribution is skewed by Vignetting. We develop two variants of this technique, both of which remove Vignetting by minimizing asymmetry of the RG distribution. Compared with prior approaches to single-image Vignetting correction, our method does not require segmentation and the results are generally better. Experiments show our technique works for a wide range of images and it achieves a speed-up of 4-5 times compared with a state-of-the-art method.
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single image Vignetting correction
Computer Vision and Pattern Recognition, 2006Co-Authors: Yuanjie Zheng, Stephen Lin, Sing Bing KangAbstract:In this paper, we propose a method for determining the Vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract Vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for Vignetting estimation. Within each image region, our method capitalizes on frequency characteristics and physical properties of Vignetting to distinguish it from other sources of intensity variation. The Vignetting data acquired from regions are weighted according to a presented reliability measure to promote robustness in estimation. Comprehensive experiments demonstrate the effectiveness of this technique on a broad range of images.
J. Shin - One of the best experts on this subject based on the ideXlab platform.
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The Vignetting Effect of the Soft X-Ray Telescope Onboard Yohkoh: II. Pre-Launch Data Analysis
Solar Physics, 2016Co-Authors: J. Shin, T. SakuraiAbstract:The Vignetting effect in the Soft X-ray Telescope (SXT) onboard Yohkoh is studied using a two-dimensional distribution of the effective area in the field of view taken from a pre-launch experiment. Our initial estimation of the Vignetting in SXT was carried out by fitting the data with a second-order polynomial function. The results show that a two-dimensional Vignetting effect in SXT is not rotationally symmetric, particularly in the y $y$ - (north–south) direction, which is probably due to the offset of SXT’s optical center from the location of its CCD center. We adopted a combined functional form consisting of a second-order polynomial function and a Gaussian function to take this asymmetric distribution of the effective area into account. We also considered the steep gradient of the effective area variation at the region near the Vignetting center for the case of higher photon energy with this form. We completed a two-dimensional description of the Vignetting effect in SXT by a spline surface fit using the “cleaned” effective area data whose noise was reduced satisfactorily by the fitting of our combined function.
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Vignetting effect in the soft x ray telescope onboard yohkoh i numerical simulation
Solar Physics, 2015Co-Authors: J. Shin, Takashi SakuraiAbstract:Using a numerical simulation method, we examine the Vignetting effect in the Soft X-ray Telescope (SXT) onboard Yohkoh. The off-axis variation of the effective area in the field-of-view shows that the Vignetting in SXT cannot be described properly with a one-dimensional axisymmetric model. Our model assumes a response function of the X-ray mirror that is symmetric about the optical center, and an effect of occultation due to other telescope structures that is symmetric about the geometrical center; the Vignetting is the result of these two contributions. We found that a rotationally non-symmetric distribution of the SXT effective area is mostly due to the offset of the optical center from the geometrical center of the telescope. The deviation from rotational symmetry due to the offset is most noticeable at the outskirts of the field-of-view, which results in the dispersion of effective area when considered as a one-dimensional distribution. The model cannot completely describe the Vignetting in SXT because the fitting errors are larger than the measurement errors. We ultimately need a fully two-dimensional model for the Vignetting in SXT.
Takashi Sakurai - One of the best experts on this subject based on the ideXlab platform.
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Vignetting effect in the soft x ray telescope onboard yohkoh i numerical simulation
Solar Physics, 2015Co-Authors: J. Shin, Takashi SakuraiAbstract:Using a numerical simulation method, we examine the Vignetting effect in the Soft X-ray Telescope (SXT) onboard Yohkoh. The off-axis variation of the effective area in the field-of-view shows that the Vignetting in SXT cannot be described properly with a one-dimensional axisymmetric model. Our model assumes a response function of the X-ray mirror that is symmetric about the optical center, and an effect of occultation due to other telescope structures that is symmetric about the geometrical center; the Vignetting is the result of these two contributions. We found that a rotationally non-symmetric distribution of the SXT effective area is mostly due to the offset of the optical center from the geometrical center of the telescope. The deviation from rotational symmetry due to the offset is most noticeable at the outskirts of the field-of-view, which results in the dispersion of effective area when considered as a one-dimensional distribution. The model cannot completely describe the Vignetting in SXT because the fitting errors are larger than the measurement errors. We ultimately need a fully two-dimensional model for the Vignetting in SXT.
