The Experts below are selected from a list of 62796 Experts worldwide ranked by ideXlab platform
Eric Marchand - One of the best experts on this subject based on the ideXlab platform.
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IROS - Using Image Gradient as a visual feature for visual servoing
2010 IEEE RSJ International Conference on Intelligent Robots and Systems, 2010Co-Authors: Eric Marchand, Christophe CollewetAbstract:Direct photometric visual servoing has proved to be an efficient approach for robot positioning. Instead of using classical geometric features such as points, straight lines, pose or an homography, as it is usually done, information provided by all pixels in the Image are considered. In the past mainly luminance information has been considered. In this paper, considering that most of the useful information in an Image is located in its high frequency areas (that are contours), we have consider various possible combinations of global visual feature based on luminance and Gradient. Experimental results are presented to show the behavior of such features.
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Using Image Gradient as a visual feature for visual servoing
2010Co-Authors: Eric Marchand, Christophe CollewetAbstract:Direct photometric visual servoing has proved to be an efficient approach for robot positioning. Instead of using classical geometric features such as points, straight lines, pose or an homography, as it is usually done, information provided by all pixels in the Image are considered. In the past mainly luminance information has been considered. In this paper, considering that most of the useful information in an Image is located in its high frequency areas (that are contours), we have consider various possible combinations of global visual feature based on luminance and Gradient. Experimental results are presented to show the behavior of such features.
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ICRA - Control Camera and Light Source Positions using Image Gradient Information
Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007Co-Authors: Eric MarchandAbstract:In this paper, we propose an original approach to control camera position and/or lighting conditions in an environment using Image Gradient information. Our goal is to ensure a good viewing condition and good illumination of an object to perform vision-based task (recognition, tracking, etc.). Within the visual servoing framework, we propose solutions to two different issues: maximizing the brightness of the scene and maximizing the contrast in the Image. Solutions are proposed to consider either a static light and a moving camera, either or a moving light and a static/moving camera. The proposed method is independent of the structure, color and aspect of the objects. Experimental results on both synthetic and real Images are finally presented.
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Control camera and light source positions using Image Gradient information
Proceedings - IEEE International Conference on Robotics and Automation, 2007Co-Authors: Eric MarchandAbstract:In this paper, we propose an original approach to control camera position and/or lighting conditions in an environment using Image Gradient information. Our goal is to ensure a good viewing condition and good illumination of an object to perform vision-based task (recognition, tracking, etc.). Within the visual servoing framework, we propose solutions to two different issues: maximizing the brightness of the scene and maximizing the contrast in the Image. Solutions are proposed to consider either a static light and a moving camera, either or a moving light and a static/moving camera. The proposed method is independent of the structure, color and aspect of the objects. Experimental results on both synthetic and real Images are finally presented.
Peijun Chen - One of the best experts on this subject based on the ideXlab platform.
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Image Gradient L0-norm based PICCS for swinging multi-source CT reconstruction
Optics express, 2019Co-Authors: Peijun Chen, Changcheng Gong, Junru Jiang, Shaoyu Wang, Fenglin LiuAbstract:Dynamic computed tomography (CT) is usually employed to Image motion objects, such as beating heart, coronary artery and cerebral perfusion, etc. Recently, to further improve the temporal resolution for aperiodic industrial process imaging, the swinging multi-source CT (SMCT) systems and the corresponding swinging multi-source prior Image constrained compressed sensing (SM-PICCS) method were developed. Since the SM-PICCS uses the L1-norm of Image Gradient, the edge structures in the reconstructed Images are blurred and motion artifacts are still present. Inspired by the advantages in terms of Image edge preservation and fine structure recovering, the L0-norm of Image Gradient is incorporated into the prior Image constrained compressed sensing, leading to an L0-PICCS Algorithm 1Table 1The parameters of L0-PICCS (δ1,δ2,λ1*,λ2*) for numerical simulation.Sourceswδ1(10-2)δ2(10-2)λ1*(10-2)λ2*(10-8)Noise-free510522.001.525522.001.55035002.00471014.33332.00500025522.00500050222.005000Noise51062002.505002554502.501.55054502.901.571027.385.91.5810000258.285.91.5850050522.001.5. The experimental results confirm that the L0-PICCS outperforms the SM-PICCS in both visual inspection and quantitative analysis.
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low dose spectral ct reconstruction using Image Gradient l0 norm and tensor dictionary
Applied Mathematical Modelling, 2018Co-Authors: Yanbo Zhang, Fenglin Liu, Qian Wang, Peijun ChenAbstract:Abstract Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of Image Gradient l0-norm, which is named as l0TDL. The l0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT Images. On the other hand, by introducing the l0-norm constraint in Gradient Image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The split-bregman method is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed l0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
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Low-dose spectral CT reconstruction using Image Gradient ℓ0-norm and tensor dictionary.
Applied mathematical modelling, 2018Co-Authors: Yanbo Zhang, Fenglin Liu, Qian Wang, Peijun ChenAbstract:Abstract Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of Image Gradient l0-norm, which is named as l0TDL. The l0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT Images. On the other hand, by introducing the l0-norm constraint in Gradient Image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The split-bregman method is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed l0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
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Low-dose spectral CT reconstruction using L0 Image Gradient and tensor dictionary.
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Yanbo Zhang, Fenglin Liu, Qian Wang, Peijun ChenAbstract:Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of Image Gradient L0-norm, which is named as L0TDL. The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT Images. On the other hand, by introducing the L0-norm constraint in Gradient Image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The alternative direction minimization method (ADMM) is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed L0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
Fenglin Liu - One of the best experts on this subject based on the ideXlab platform.
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Limited-Angle X-Ray CT Reconstruction Using Image Gradient ℓ ₀-Norm With Dictionary Learning
IEEE Transactions on Radiation and Plasma Medical Sciences, 2021Co-Authors: Fulin Luo, Shaoyu Wang, Fenglin Liu, Wu WeiwenAbstract:Limited-angle X-ray computed tomography (CT) reconstruction is a typical ill-posed problem. To recover satisfied reconstructed Images with limited-angle CT projections, prior information is usually introduced into Image reconstruction, such as the piece-wise constant, nonlocal Image similarity, and so on. To further improve the Image quality for limited-angle CT reconstruction, the dictionary learning (DL) and Image Gradient ${\ell }_{0}$ -norm are combined into Image reconstruction model, it can be called as ${\ell }_{0}$ DL reconstruction technique. The advantages of ${\ell }_{0}$ DL can be divided into two aspects. On one hand, the proposed ${\ell }_{0}$ DL method can inherit the advantages of DL in Image details preservation and features recovery by exploring an over-complete dictionary. On the other hand, the Image Gradient ${\ell }_{0}$ -norm minimization can further protect Image edges and reduce shadow artifact. Both numerical simulation and preclinical mouse experiments are performed to validate and evaluate the outperformances of proposed ${\ell }_{0}$ DL method by comparing with other state-of-the-art methods, such as total variation (TV) minimization and TV with low rank (TV + LR).
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Low-dose spectral CT reconstruction based on Image-Gradient L0-norm and adaptive spectral PICCS.
Physics in medicine and biology, 2020Co-Authors: Shaoyu Wang, Jian Feng, Fenglin LiuAbstract:The photon-counting detector based spectral computed tomography (CT) is promising for lesion detection, tissue characterization, and material decomposition. However, the lower signal-to-noise ratio within multi-energy projection dataset can result in poorly reconstructed Image quality. Recently, as prior information, a high-quality spectral mean Image was introduced into the prior Image constrained compressed sensing (PICCS) framework to suppress noise, leading to spectral PICCS (SPICCS). In the original SPICCS model, the Image Gradient L1-norm is employed, and it can cause blurred edge structures in the reconstructed Images. Encouraged by the advantages in edge preservation and finer structure recovering, the Image Gradient L0-norm was incorporated into the PICCS model. Furthermore, due to the difference of energy spectrum in different channels, a weighting factor is introduced and adaptively adjusted for different channel-wise Images, leading to an L0-norm based adaptive SPICCS (L0-ASPICCS) algorithm for low-dose spectral CT reconstruction. The split-Bregman method is employed to minimize the objective function. Extensive numerical simulations and physical phantom experiments are performed to evaluate the proposed method. By comparing with the state-of-the-art algorithms, such as the simultaneous algebraic reconstruction technique, total variation minimization, and SPICCS, the advantages of our proposed method are demonstrated in terms of both qualitative and quantitative evaluation results.
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Image Gradient L0-norm based PICCS for swinging multi-source CT reconstruction
Optics express, 2019Co-Authors: Peijun Chen, Changcheng Gong, Junru Jiang, Shaoyu Wang, Fenglin LiuAbstract:Dynamic computed tomography (CT) is usually employed to Image motion objects, such as beating heart, coronary artery and cerebral perfusion, etc. Recently, to further improve the temporal resolution for aperiodic industrial process imaging, the swinging multi-source CT (SMCT) systems and the corresponding swinging multi-source prior Image constrained compressed sensing (SM-PICCS) method were developed. Since the SM-PICCS uses the L1-norm of Image Gradient, the edge structures in the reconstructed Images are blurred and motion artifacts are still present. Inspired by the advantages in terms of Image edge preservation and fine structure recovering, the L0-norm of Image Gradient is incorporated into the prior Image constrained compressed sensing, leading to an L0-PICCS Algorithm 1Table 1The parameters of L0-PICCS (δ1,δ2,λ1*,λ2*) for numerical simulation.Sourceswδ1(10-2)δ2(10-2)λ1*(10-2)λ2*(10-8)Noise-free510522.001.525522.001.55035002.00471014.33332.00500025522.00500050222.005000Noise51062002.505002554502.501.55054502.901.571027.385.91.5810000258.285.91.5850050522.001.5. The experimental results confirm that the L0-PICCS outperforms the SM-PICCS in both visual inspection and quantitative analysis.
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low dose spectral ct reconstruction using Image Gradient l0 norm and tensor dictionary
Applied Mathematical Modelling, 2018Co-Authors: Yanbo Zhang, Fenglin Liu, Qian Wang, Peijun ChenAbstract:Abstract Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of Image Gradient l0-norm, which is named as l0TDL. The l0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT Images. On the other hand, by introducing the l0-norm constraint in Gradient Image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The split-bregman method is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed l0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
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Low-dose spectral CT reconstruction using Image Gradient ℓ0-norm and tensor dictionary.
Applied mathematical modelling, 2018Co-Authors: Yanbo Zhang, Fenglin Liu, Qian Wang, Peijun ChenAbstract:Abstract Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of Image Gradient l0-norm, which is named as l0TDL. The l0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT Images. On the other hand, by introducing the l0-norm constraint in Gradient Image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The split-bregman method is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed l0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
Yanbo Zhang - One of the best experts on this subject based on the ideXlab platform.
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low dose spectral ct reconstruction using Image Gradient l0 norm and tensor dictionary
Applied Mathematical Modelling, 2018Co-Authors: Yanbo Zhang, Fenglin Liu, Qian Wang, Peijun ChenAbstract:Abstract Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of Image Gradient l0-norm, which is named as l0TDL. The l0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT Images. On the other hand, by introducing the l0-norm constraint in Gradient Image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The split-bregman method is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed l0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
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Low-dose spectral CT reconstruction using Image Gradient ℓ0-norm and tensor dictionary.
Applied mathematical modelling, 2018Co-Authors: Yanbo Zhang, Fenglin Liu, Qian Wang, Peijun ChenAbstract:Abstract Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of Image Gradient l0-norm, which is named as l0TDL. The l0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT Images. On the other hand, by introducing the l0-norm constraint in Gradient Image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The split-bregman method is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed l0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
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Low-dose spectral CT reconstruction using L0 Image Gradient and tensor dictionary.
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Yanbo Zhang, Fenglin Liu, Qian Wang, Peijun ChenAbstract:Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of Image Gradient L0-norm, which is named as L0TDL. The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT Images. On the other hand, by introducing the L0-norm constraint in Gradient Image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The alternative direction minimization method (ADMM) is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed L0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
Christophe Collewet - One of the best experts on this subject based on the ideXlab platform.
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IROS - Using Image Gradient as a visual feature for visual servoing
2010 IEEE RSJ International Conference on Intelligent Robots and Systems, 2010Co-Authors: Eric Marchand, Christophe CollewetAbstract:Direct photometric visual servoing has proved to be an efficient approach for robot positioning. Instead of using classical geometric features such as points, straight lines, pose or an homography, as it is usually done, information provided by all pixels in the Image are considered. In the past mainly luminance information has been considered. In this paper, considering that most of the useful information in an Image is located in its high frequency areas (that are contours), we have consider various possible combinations of global visual feature based on luminance and Gradient. Experimental results are presented to show the behavior of such features.
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Using Image Gradient as a visual feature for visual servoing
2010Co-Authors: Eric Marchand, Christophe CollewetAbstract:Direct photometric visual servoing has proved to be an efficient approach for robot positioning. Instead of using classical geometric features such as points, straight lines, pose or an homography, as it is usually done, information provided by all pixels in the Image are considered. In the past mainly luminance information has been considered. In this paper, considering that most of the useful information in an Image is located in its high frequency areas (that are contours), we have consider various possible combinations of global visual feature based on luminance and Gradient. Experimental results are presented to show the behavior of such features.
