The Experts below are selected from a list of 12225 Experts worldwide ranked by ideXlab platform
Kun Zhang - One of the best experts on this subject based on the ideXlab platform.
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Improved method for SAR image registration based on scale invariant Feature Transform
Iet Radar Sonar and Navigation, 2017Co-Authors: Deyun Zhou, Lina Zeng, Junli Liang, Kun ZhangAbstract:Scale invariant Feature Transform (SIFT) is one of the most common registration algorithms for synthetic aperture radar (SAR) images. However, the occurrence of speckle noise and geometric distortion within SAR images usually leads to limited effectiveness, challenging the stability of SIFT and its variants in real actual applications. In this study, significant improvements for SAR image registration with SIFT are made, which lie mainly in two aspects. First, a scheme is developed to enhance the description of keypoints with improved dominant orientation assignment and support region. Second, an optimised matching method for further enhancing the matching performance is developed to reduce the mutual interference among the keypoints with similar location and dominant orientations. Extensive experiments confirm the effectiveness of the proposed algorithm for SAR images.
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Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Lina Zeng, Deyun Zhou, Junli Liang, Kun ZhangAbstract:Obtaining high accuracy in orientation assignment for Synthetic Aperture Radar (SAR) image registration is a great challenge because of the serious speckle noise and geometrical distortion. In this letter, a polar Scale-Invariant Feature Transform (PSIFT) descriptor is proposed for SAR image registration. The novel descriptor is invariant to rotation, skipping the dominant orientation assignment. In PSIFT, a polar-Transformed support region is adopted to calculate the gradient magnitudes and orientations and further sampled in the radial and angular directions with different scales. The final descriptor is then built with the orientation bins covering the omnidirectional space. Furthermore, an improved dual-matching method is proposed to achieve sufficiently correct matches. Extensive experiments confirm that the PSIFT descriptor is suitable for SAR image registration because of its excellent performance.
Lina Zeng - One of the best experts on this subject based on the ideXlab platform.
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Improved method for SAR image registration based on scale invariant Feature Transform
Iet Radar Sonar and Navigation, 2017Co-Authors: Deyun Zhou, Lina Zeng, Junli Liang, Kun ZhangAbstract:Scale invariant Feature Transform (SIFT) is one of the most common registration algorithms for synthetic aperture radar (SAR) images. However, the occurrence of speckle noise and geometric distortion within SAR images usually leads to limited effectiveness, challenging the stability of SIFT and its variants in real actual applications. In this study, significant improvements for SAR image registration with SIFT are made, which lie mainly in two aspects. First, a scheme is developed to enhance the description of keypoints with improved dominant orientation assignment and support region. Second, an optimised matching method for further enhancing the matching performance is developed to reduce the mutual interference among the keypoints with similar location and dominant orientations. Extensive experiments confirm the effectiveness of the proposed algorithm for SAR images.
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Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Lina Zeng, Deyun Zhou, Junli Liang, Kun ZhangAbstract:Obtaining high accuracy in orientation assignment for Synthetic Aperture Radar (SAR) image registration is a great challenge because of the serious speckle noise and geometrical distortion. In this letter, a polar Scale-Invariant Feature Transform (PSIFT) descriptor is proposed for SAR image registration. The novel descriptor is invariant to rotation, skipping the dominant orientation assignment. In PSIFT, a polar-Transformed support region is adopted to calculate the gradient magnitudes and orientations and further sampled in the radial and angular directions with different scales. The final descriptor is then built with the orientation bins covering the omnidirectional space. Furthermore, an improved dual-matching method is proposed to achieve sufficiently correct matches. Extensive experiments confirm that the PSIFT descriptor is suitable for SAR image registration because of its excellent performance.
Deyun Zhou - One of the best experts on this subject based on the ideXlab platform.
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Improved method for SAR image registration based on scale invariant Feature Transform
Iet Radar Sonar and Navigation, 2017Co-Authors: Deyun Zhou, Lina Zeng, Junli Liang, Kun ZhangAbstract:Scale invariant Feature Transform (SIFT) is one of the most common registration algorithms for synthetic aperture radar (SAR) images. However, the occurrence of speckle noise and geometric distortion within SAR images usually leads to limited effectiveness, challenging the stability of SIFT and its variants in real actual applications. In this study, significant improvements for SAR image registration with SIFT are made, which lie mainly in two aspects. First, a scheme is developed to enhance the description of keypoints with improved dominant orientation assignment and support region. Second, an optimised matching method for further enhancing the matching performance is developed to reduce the mutual interference among the keypoints with similar location and dominant orientations. Extensive experiments confirm the effectiveness of the proposed algorithm for SAR images.
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Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Lina Zeng, Deyun Zhou, Junli Liang, Kun ZhangAbstract:Obtaining high accuracy in orientation assignment for Synthetic Aperture Radar (SAR) image registration is a great challenge because of the serious speckle noise and geometrical distortion. In this letter, a polar Scale-Invariant Feature Transform (PSIFT) descriptor is proposed for SAR image registration. The novel descriptor is invariant to rotation, skipping the dominant orientation assignment. In PSIFT, a polar-Transformed support region is adopted to calculate the gradient magnitudes and orientations and further sampled in the radial and angular directions with different scales. The final descriptor is then built with the orientation bins covering the omnidirectional space. Furthermore, an improved dual-matching method is proposed to achieve sufficiently correct matches. Extensive experiments confirm that the PSIFT descriptor is suitable for SAR image registration because of its excellent performance.
Junli Liang - One of the best experts on this subject based on the ideXlab platform.
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Improved method for SAR image registration based on scale invariant Feature Transform
Iet Radar Sonar and Navigation, 2017Co-Authors: Deyun Zhou, Lina Zeng, Junli Liang, Kun ZhangAbstract:Scale invariant Feature Transform (SIFT) is one of the most common registration algorithms for synthetic aperture radar (SAR) images. However, the occurrence of speckle noise and geometric distortion within SAR images usually leads to limited effectiveness, challenging the stability of SIFT and its variants in real actual applications. In this study, significant improvements for SAR image registration with SIFT are made, which lie mainly in two aspects. First, a scheme is developed to enhance the description of keypoints with improved dominant orientation assignment and support region. Second, an optimised matching method for further enhancing the matching performance is developed to reduce the mutual interference among the keypoints with similar location and dominant orientations. Extensive experiments confirm the effectiveness of the proposed algorithm for SAR images.
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Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Lina Zeng, Deyun Zhou, Junli Liang, Kun ZhangAbstract:Obtaining high accuracy in orientation assignment for Synthetic Aperture Radar (SAR) image registration is a great challenge because of the serious speckle noise and geometrical distortion. In this letter, a polar Scale-Invariant Feature Transform (PSIFT) descriptor is proposed for SAR image registration. The novel descriptor is invariant to rotation, skipping the dominant orientation assignment. In PSIFT, a polar-Transformed support region is adopted to calculate the gradient magnitudes and orientations and further sampled in the radial and angular directions with different scales. The final descriptor is then built with the orientation bins covering the omnidirectional space. Furthermore, an improved dual-matching method is proposed to achieve sufficiently correct matches. Extensive experiments confirm that the PSIFT descriptor is suitable for SAR image registration because of its excellent performance.
Peizhou He - One of the best experts on this subject based on the ideXlab platform.
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Shot Boundary Detection and Keyframe Extraction Based on Scale Invariant Feature Transform
2009 Eighth IEEE ACIS International Conference on Computer and Information Science, 2009Co-Authors: Wei Zheng, Peizhou HeAbstract:In shot boundary detection, the key technology is to compute the visual content discontinuity values between consecutive video frames. In this paper, a unified framework is proposed to detect the shot boundaries and extract the keyframes of a shot. Firstly, the scale invariant Feature Transform (SIFT) is adopted to compute the visual content discontinuity values. Then a new method, which is called the local double threshold shot boundary detection (LDT-SBD), is used to detect shot boundaries. Lastly, two mechanisms are proposed to extract keyframe. Experimental results show the framework is effective and has a good performance.
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ACIS-ICIS - Shot Boundary Detection and Keyframe Extraction Based on Scale Invariant Feature Transform
2009 Eighth IEEE ACIS International Conference on Computer and Information Science, 2009Co-Authors: Wei Zheng, Peizhou HeAbstract:In shot boundary detection, the key technology is to compute the visual content discontinuity values between consecutive video frames. In this paper, a unified framework is proposed to detect the shot boundaries and extract the keyframes of a shot. Firstly, the Scale invariant Feature Transform (SIFT) is adopted to compute the visual content discontinuity values. Then a new method, which is called the Local Double Threshold Shot Boundary Detection (LDT-SBD), is used to detect shot boundaries. Lastly, two mechanisms are proposed to extract keyframe. Experimental results show the framework is effective and has a good performance.
