The Experts below are selected from a list of 1202127 Experts worldwide ranked by ideXlab platform
Frédéric Dufaux - One of the best experts on this subject based on the ideXlab platform.
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An image smoothing operator for fast and accurate scale space approximation
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Maxim Karpushin, Giuseppe Valenzise, Frédéric DufauxAbstract:Gussian image smoothing is a fundamental operation in the extraction of scale-invariant feature points. Its computation, however, can be too expensive in some resource-constrained scenarios. Alternative solutions such as the box filter can be computed more efficiently, at the cost of a loss in feature repeatibility under some conditions. In this paper we propose a fast and accurate image smoothing operator based on integral images. It has the same order of computational complexity as the box filter, but provides much more accurate visual results and improved keypoint Repeatability, which is confirmed in a feature detection scenario using SIFT features.
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ICASSP - An image smoothing operator for fast and accurate scale space approximation
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Maxim Karpushin, Giuseppe Valenzise, Frédéric DufauxAbstract:Gussian image smoothing is a fundamental operation in the extraction of scale-invariant feature points. Its computation, however, can be too expensive in some resource-constrained scenarios. Alternative solutions such as the box filter can be computed more efficiently, at the cost of a loss in feature repeatibility under some conditions. In this paper we propose a fast and accurate image smoothing operator based on integral images. It has the same order of computational complexity as the box filter, but provides much more accurate visual results and improved keypoint Repeatability, which is confirmed in a feature detection scenario using SIFT features.
Mark A. Bullimore - One of the best experts on this subject based on the ideXlab platform.
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the Repeatability of corneal thickness measures
Cornea, 2000Co-Authors: Matthew M Marsich, Mark A. BullimoreAbstract:Purpose.To compare the Repeatability of three measures of corneal thickness: Orbscan Slitscan pachymetry, ultrasound pachymetry, and optical pachymetry.Methods.Twenty normal subjects were tested on three occasions. Two occurred on the same day and the third was on a different day at approximately th
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Repeatability of visual acuity measurement
Optometry and Vision Science, 1998Co-Authors: Thomas W Raasch, Ian Bailey, Mark A. BullimoreAbstract:ABSTRACT:Purpose.This study investigates features of visual acuity chart design and acuity testing scoring methods which affect the validity and Repeatability of visual acuity measurements.Methods.Visual acuity was measured using the Sloan and British Standard letter series, and Landolt rings. Ident
Maxim Karpushin - One of the best experts on this subject based on the ideXlab platform.
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An image smoothing operator for fast and accurate scale space approximation
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Maxim Karpushin, Giuseppe Valenzise, Frédéric DufauxAbstract:Gussian image smoothing is a fundamental operation in the extraction of scale-invariant feature points. Its computation, however, can be too expensive in some resource-constrained scenarios. Alternative solutions such as the box filter can be computed more efficiently, at the cost of a loss in feature repeatibility under some conditions. In this paper we propose a fast and accurate image smoothing operator based on integral images. It has the same order of computational complexity as the box filter, but provides much more accurate visual results and improved keypoint Repeatability, which is confirmed in a feature detection scenario using SIFT features.
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ICASSP - An image smoothing operator for fast and accurate scale space approximation
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Maxim Karpushin, Giuseppe Valenzise, Frédéric DufauxAbstract:Gussian image smoothing is a fundamental operation in the extraction of scale-invariant feature points. Its computation, however, can be too expensive in some resource-constrained scenarios. Alternative solutions such as the box filter can be computed more efficiently, at the cost of a loss in feature repeatibility under some conditions. In this paper we propose a fast and accurate image smoothing operator based on integral images. It has the same order of computational complexity as the box filter, but provides much more accurate visual results and improved keypoint Repeatability, which is confirmed in a feature detection scenario using SIFT features.
Andrey Fedorov - One of the best experts on this subject based on the ideXlab platform.
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Repeatability of multiparametric prostate mri radiomics features
Scientific Reports, 2019Co-Authors: Michael Schwier, Joost J M Van Griethuysen, Mark Vangel, Steve Pieper, Sharon Peled, Clare M Tempany, Hugo J W L Aerts, Ron Kikinis, Fiona M Fennessy, Andrey FedorovAbstract:In this study we assessed the Repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, Repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated Repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high Repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the Repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in Repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.
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Repeatability of multiparametric prostate mri radiomics features
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Michael Schwier, Joost J M Van Griethuysen, Mark Vangel, Steve Pieper, Sharon Peled, Clare M Tempany, Hugo J W L Aerts, Ron Kikinis, Fiona M Fennessy, Andrey FedorovAbstract:In this study we assessed the Repeatability of the values of radiomics features for small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI) images. The premise of radiomics is that quantitative image features can serve as biomarkers characterizing disease. For such biomarkers to be useful, Repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated Repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, 2D vs 3D texture computation, and different bin widths for image discretization. Image registration as means to re-identify regions of interest across time points was evaluated against human-expert segmented regions in both time points. Even though we found many radiomics features and preprocessing combinations with a high Repeatability (Intraclass Correlation Coefficient (ICC) > 0.85), our results indicate that overall the Repeatability is highly sensitive to the processing parameters (under certain configurations, it can be below 0.0). Image normalization, using a variety of approaches considered, did not result in consistent improvements in Repeatability. There was also no consistent improvement of Repeatability through the use of pre-filtering options, or by using image registration between timepoints to improve consistency of the region of interest localization. Based on these results we urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend making the implementation available.
Giuseppe Valenzise - One of the best experts on this subject based on the ideXlab platform.
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An image smoothing operator for fast and accurate scale space approximation
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Maxim Karpushin, Giuseppe Valenzise, Frédéric DufauxAbstract:Gussian image smoothing is a fundamental operation in the extraction of scale-invariant feature points. Its computation, however, can be too expensive in some resource-constrained scenarios. Alternative solutions such as the box filter can be computed more efficiently, at the cost of a loss in feature repeatibility under some conditions. In this paper we propose a fast and accurate image smoothing operator based on integral images. It has the same order of computational complexity as the box filter, but provides much more accurate visual results and improved keypoint Repeatability, which is confirmed in a feature detection scenario using SIFT features.
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ICASSP - An image smoothing operator for fast and accurate scale space approximation
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Maxim Karpushin, Giuseppe Valenzise, Frédéric DufauxAbstract:Gussian image smoothing is a fundamental operation in the extraction of scale-invariant feature points. Its computation, however, can be too expensive in some resource-constrained scenarios. Alternative solutions such as the box filter can be computed more efficiently, at the cost of a loss in feature repeatibility under some conditions. In this paper we propose a fast and accurate image smoothing operator based on integral images. It has the same order of computational complexity as the box filter, but provides much more accurate visual results and improved keypoint Repeatability, which is confirmed in a feature detection scenario using SIFT features.
