The Experts below are selected from a list of 27753 Experts worldwide ranked by ideXlab platform
Zaiping Lin - One of the best experts on this subject based on the ideXlab platform.
-
infrared small target detection using a Temporal variance and spatial patch contrast Filter
IEEE Access, 2019Co-Authors: Jinyan Gao, Zaiping LinAbstract:Infrared small target detection is challenging due to the various background and low signal-to-clutter ratios. Considering the information deficiency faced by single spatial or Temporal information, we construct a low false alarm spatial and Temporal Filter for infrared small target detection. A multiscale patch-based contrast measure is first used to suppress background and remove cloud edges at a coarse level. Then, a Temporal variance Filter is used to remove small broken cloud regions and suppress noise at a fine level. By integrating these two methods, infrared small targets can be extracted accurately and robustly using an adaptive threshold segmentation. The experimental results indicate that our proposed method can robustly detect small infrared targets with a low false alarm rate.
-
DICTA - TVPCF: A Spatial and Temporal Filter for Small Target Detection in IR Images
2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2017Co-Authors: Jinyan Gao, Zaiping Lin, Yulan GuoAbstract:Small infrared target detection in complex backgrounds is a challenging task. Due to dynamic background clutter and low signal-to-clutter ratio, most conventional methods fail to produce satisfactory results. In this paper, an effective spatial and Temporal Filter is proposed. The spatial Filter is used to remove cloud edge, and the Temporal Filter is used to remove point-like background clutter. Experimental results demonstrate that the proposed method can effectively detect dim small targets with a very low false alarm rate and an acceptable detection rate.
Jinyan Gao - One of the best experts on this subject based on the ideXlab platform.
-
infrared small target detection using a Temporal variance and spatial patch contrast Filter
IEEE Access, 2019Co-Authors: Jinyan Gao, Zaiping LinAbstract:Infrared small target detection is challenging due to the various background and low signal-to-clutter ratios. Considering the information deficiency faced by single spatial or Temporal information, we construct a low false alarm spatial and Temporal Filter for infrared small target detection. A multiscale patch-based contrast measure is first used to suppress background and remove cloud edges at a coarse level. Then, a Temporal variance Filter is used to remove small broken cloud regions and suppress noise at a fine level. By integrating these two methods, infrared small targets can be extracted accurately and robustly using an adaptive threshold segmentation. The experimental results indicate that our proposed method can robustly detect small infrared targets with a low false alarm rate.
-
DICTA - TVPCF: A Spatial and Temporal Filter for Small Target Detection in IR Images
2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2017Co-Authors: Jinyan Gao, Zaiping Lin, Yulan GuoAbstract:Small infrared target detection in complex backgrounds is a challenging task. Due to dynamic background clutter and low signal-to-clutter ratio, most conventional methods fail to produce satisfactory results. In this paper, an effective spatial and Temporal Filter is proposed. The spatial Filter is used to remove cloud edge, and the Temporal Filter is used to remove point-like background clutter. Experimental results demonstrate that the proposed method can effectively detect dim small targets with a very low false alarm rate and an acceptable detection rate.
John Zelek - One of the best experts on this subject based on the ideXlab platform.
-
improved spatio Temporal salient feature detection for action recognition
British Machine Vision Conference, 2011Co-Authors: Amir H Shabani, David A Clausi, John ZelekAbstract:Spatio-Temporal salient features can localize the local motion events and are used to represent video sequences for many computer vision tasks such as action recognition. The robust detection of these features under geometric variations such as affine transformation and view/scale changes is however an open problem. Existing methods use the same Filter for both time and space and hence, perform an isotropic Temporal Filtering. A novel anisotropic Temporal Filter for better spatio-Temporal feature detection is developed. The effect of symmetry and causality of the video Filtering is investigated. Based on the positive results of precision and reproducibility tests, we propose the use of Temporally asymmetric Filtering for robust motion feature detection and action recognition.
-
BMVC - Improved Spatio-Temporal Salient Feature Detection for Action Recognition
Procedings of the British Machine Vision Conference 2011, 2011Co-Authors: Amir H Shabani, David A Clausi, John ZelekAbstract:Spatio-Temporal salient features can localize the local motion events and are used to represent video sequences for many computer vision tasks such as action recognition. The robust detection of these features under geometric variations such as affine transformation and view/scale changes is however an open problem. Existing methods use the same Filter for both time and space and hence, perform an isotropic Temporal Filtering. A novel anisotropic Temporal Filter for better spatio-Temporal feature detection is developed. The effect of symmetry and causality of the video Filtering is investigated. Based on the positive results of precision and reproducibility tests, we propose the use of Temporally asymmetric Filtering for robust motion feature detection and action recognition.
Amir H Shabani - One of the best experts on this subject based on the ideXlab platform.
-
improved spatio Temporal salient feature detection for action recognition
British Machine Vision Conference, 2011Co-Authors: Amir H Shabani, David A Clausi, John ZelekAbstract:Spatio-Temporal salient features can localize the local motion events and are used to represent video sequences for many computer vision tasks such as action recognition. The robust detection of these features under geometric variations such as affine transformation and view/scale changes is however an open problem. Existing methods use the same Filter for both time and space and hence, perform an isotropic Temporal Filtering. A novel anisotropic Temporal Filter for better spatio-Temporal feature detection is developed. The effect of symmetry and causality of the video Filtering is investigated. Based on the positive results of precision and reproducibility tests, we propose the use of Temporally asymmetric Filtering for robust motion feature detection and action recognition.
-
BMVC - Improved Spatio-Temporal Salient Feature Detection for Action Recognition
Procedings of the British Machine Vision Conference 2011, 2011Co-Authors: Amir H Shabani, David A Clausi, John ZelekAbstract:Spatio-Temporal salient features can localize the local motion events and are used to represent video sequences for many computer vision tasks such as action recognition. The robust detection of these features under geometric variations such as affine transformation and view/scale changes is however an open problem. Existing methods use the same Filter for both time and space and hence, perform an isotropic Temporal Filtering. A novel anisotropic Temporal Filter for better spatio-Temporal feature detection is developed. The effect of symmetry and causality of the video Filtering is investigated. Based on the positive results of precision and reproducibility tests, we propose the use of Temporally asymmetric Filtering for robust motion feature detection and action recognition.
David L Wilson - One of the best experts on this subject based on the ideXlab platform.
-
quantitative image quality analysis of a nonlinear spatio Temporal Filter
IEEE Transactions on Image Processing, 2001Co-Authors: F J Sanchezmarin, Yogesh Srinivas, Kadri N Jabri, David L WilsonAbstract:Digital Temporal and spatial Filtering of fluoroscopic image sequences can be used to improve the quality of images acquired at low X-ray exposure. In this study, we characterized a nonlinear edge preserving, spatio-Temporal noise reduction Filter, the bidirectional multistage (BMS) median Filter of Arce (1991). To assess image quality, signal detection and discrimination experiments were performed on stationary targets using a four-alternative forced-choice paradigm. A measure of detectability, d', was obtained for Filtered and unFiltered noisy image sequences at different signal amplitudes. Filtering gave statistically significant, average d' improvements of 20% (detection) and 31% (discrimination). A nonprewhitening detection model modified to include the human spatio-Temporal visual system contrast-sensitivity underestimated enhancement, predicting an improvement of 6%. Pixel noise standard deviation, a commonly applied image quality measure, greatly overestimated effectiveness giving 67% improvement in d'. We conclude that human testing is required to evaluate the Filter effectiveness and that human perception models must be improved to account for the spatio-Temporal Filtering of image sequences.
