The Experts below are selected from a list of 36534 Experts worldwide ranked by ideXlab platform
Zhengzhou Li - One of the best experts on this subject based on the ideXlab platform.
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infrared small target detection based on flux density and direction diversity in Gradient Vector field
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Zhengzhou LiAbstract:The existing small target detection methods may suffer serious false alarm rate and low probability of detection in the situation of intricate background clutter. To cope with this problem, a novel small target detection method is proposed in this paper. Initially, the infrared image is transformed to the infrared Gradient Vector field (IGVF), where some new distinctive characters of the target and background clutter can be exploited. The small targets show as sink points, while the heavy clutter illustrates high direction coherence in IGVF. Then, the multiscale flux density (MFD) is proposed to quantify the extent of sink point character. In the MFD map, the small targets can be well enhanced and background clutters can be suppressed simultaneously. After that, by analyzing the coherence of heavy clutter shown in the IGVF, the Gradient direction diversity (GDD) is presented. The residual noise caused by the heavy clutter in IGVF can be further suppressed by GDD. Finally, an adaptive threshold is adopted to separate the targets. Extensive experiments, including both real data and synthesized data, show that the proposed method outperforms other state-of-the-art methods, especially for infrared images with complex background clutter. Moreover, the experiments prove that the proposed method can work stably for different small target quantities, distances between adjacent targets, target shapes, and noise types with reasonable computational cost.
Peng Che - One of the best experts on this subject based on the ideXlab platform.
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infrared small target detection based on flux density and direction diversity in Gradient Vector field
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Depeng Liu, Lei Cao, Tianmei Liu, Peng CheAbstract:The existing small target detection methods may suffer serious false alarm rate and low probability of detection in the situation of intricate background clutter. To cope with this problem, a novel small target detection method is proposed in this paper. Initially, the infrared image is transformed to the infrared Gradient Vector field (IGVF), where some new distinctive characters of the target and background clutter can be exploited. The small targets show as sink points, while the heavy clutter illustrates high direction coherence in IGVF. Then, the multiscale flux density (MFD) is proposed to quantify the extent of sink point character. In the MFD map, the small targets can be well enhanced and background clutters can be suppressed simultaneously. After that, by analyzing the coherence of heavy clutter shown in the IGVF, the Gradient direction diversity (GDD) is presented. The residual noise caused by the heavy clutter in IGVF can be further suppressed by GDD. Finally, an adaptive threshold is adopted to separate the targets. Extensive experiments, including both real data and synthesized data, show that the proposed method outperforms other state-of-the-art methods, especially for infrared images with complex background clutter. Moreover, the experiments prove that the proposed method can work stably for different small target quantities, distances between adjacent targets, target shapes, and noise types with reasonable computational cost.
Visvanathan Ramesh - One of the best experts on this subject based on the ideXlab platform.
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Gradient Vector flow fast geometric active contours
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004Co-Authors: Nikos Paragios, O Mellinagottardo, Visvanathan RameshAbstract:In this paper, we propose an edge-driven bidirectional geometric flow for boundary extraction. To this end, we combine the geodesic active contour flow and the Gradient Vector flow external force for snakes. The resulting motion equation is considered within a level set formulation, can deal with topological changes and important shape deformations. An efficient numerical schema is used for the flow implementation that exhibits robust behavior and has fast convergence rate. Promising results on real and synthetic images demonstrate the potentials of the flow.
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Gradient Vector flow fast geodesic active contours
International Conference on Computer Vision, 2001Co-Authors: Nikos Paragios, O Mellinagottardo, Visvanathan RameshAbstract:This paper proposes a new front propagation flow for boundary extraction. The proposed framework is inspired by the geodesic active contour model and leads to a paradigm that is relatively free from the initial curve position. Towards this end, it makes use of a recently introduced external boundary force, the Gradient Vector field that refers to a spatial diffusion of the boundary information. According to the proposed flow, the traditional boundary attraction term is replaced with a new force that guides the propagation to the object boundaries from both sides. This new geometric flow is implemented using a level set approach, thereby allowing dealing naturally with topological changes and important shape deformations. Moreover the level set motion equations are implemented using a recently introduced numerical approximation scheme, the Additive Operator Splitting Schema (AOS) which has a fast convergence rate and stable behavior. Encouraging experimental results are provided using real images.
Jerry L Prince - One of the best experts on this subject based on the ideXlab platform.
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global optimality of Gradient Vector flow
2000Co-Authors: Jerry L PrinceAbstract:In [1, 2], Xu and Prince introduced Gradient Vector flow (GVF), a class of Vector fields derived from images, that can be used as external forces for deformable models [3]. Figure 1 illustrates the use of GVF in a deformable model to extract a two-dimensional U-shape object. GVF can be defined through either a variational formulation or a partial differential equation. In this paper, we are concerned with the variational formulation introduced in [2]. The solution to this variational formulation was obtained in [2] by first deriving the necessary condition, the Euler-Lagrange Equation (ELE), and then solving the ELE numerically. Here, we prove the convexity of the GVF variational formulation using the convexity analysis described in [4] and point out that the corresponding ELE is in fact a sufficient condition for globally minimizing the variational energy formulation.
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generalized Gradient Vector flow external forces for active contours
Signal Processing, 1998Co-Authors: Jerry L PrinceAbstract:Active contours, or snakes, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. A new type of external force for active contours, called Gradient Vector flow (GVF) was introduced recently to address problems associated with initialization and poor convergence to boundary concavities. GVF is computed as a di⁄usion of the Gradient Vectors of a gray-level or binary edge map derived from the image. In this paper, we generalize the GVF formulation to include two spatially varying weighting functions. This improves active contour convergence to long, thin boundary indentations, while maintaining other desirable properties of GVF, such as an extended capture range. The original GVF is a special case of this new generalized GVF (GGVF) model. An error analysis for active contour results on simulated test images is also presented. ( 1998 Elsevier Science B.V. All rights reserved.
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snakes shapes and Gradient Vector flow
IEEE Transactions on Image Processing, 1998Co-Authors: Jerry L PrinceAbstract:Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call Gradient Vector flow (GVF), is computed as a diffusion of the Gradient Vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative Gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.
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Gradient Vector flow a new external force for snakes
Computer Vision and Pattern Recognition, 1997Co-Authors: Jerry L PrinceAbstract:Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to concave boundaries, however, have limited their utility. This paper develops a new external force for active contours, largely solving both problems. This external force, which we call Gradient Vector flow (GVF) is computed as a diffusion of the Gradient Vectors of a gray-level or binary edge map derived from the image. The resultant field has a large capture range and forces active contours into concave regions. Examples on simulated images and one real image are presented.
David Zhang - One of the best experts on this subject based on the ideXlab platform.
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fast Gradient Vector flow computation based on augmented lagrangian method
Pattern Recognition Letters, 2013Co-Authors: Dongwei Ren, Wangmeng Zuo, Xiaofei Zhao, Zhouchen Lin, David ZhangAbstract:Gradient Vector flow (GVF) and generalized GVF (GGVF) have been widely applied in many image processing applications. The high cost of GVF/GGVF computation, however, has restricted their potential applications on images with large size. Motivated by progress in fast image restoration algorithms, we reformulate the GVF/GGVF computation problem using the convex optimization model with equality constraint, and solve it using the inexact augmented Lagrangian method (IALM). With fast Fourier transform (FFT), we provide two novel simple and efficient algorithms for GVF/GGVF computation, respectively. To further improve the computational efficiency, the multiresolution approach is adopted to perform the GVF/GGVF computation in a coarse-to-fine manner. Experimental results show that the proposed methods can improve the computational speed of the original GVF/GGVF by one or two order of magnitude, and are more efficient than the state-of-the-art methods for GVF/GGVF computation.
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automatic tongue image segmentation based on Gradient Vector flow and region merging
Neural Computing and Applications, 2012Co-Authors: David Zhang, Jifeng Ning, Feng YueAbstract:This paper presents a region merging-based automatic tongue segmentation method. First, Gradient Vector flow is modified as a scalar diffusion equation to diffuse the tongue image while preserving the edge structures of tongue body. Then the diffused tongue image is segmented into many small regions by using the watershed algorithm. Third, the maximal similarity-based region merging is used to extract the tongue body area under the control of tongue marker. Finally, the snake algorithm is used to refine the region merging result by setting the extracted tongue contour as the initial curve. The proposed method is qualitatively tested on 200 images by traditional Chinese medicine practitioners and quantitatively tested on 50 tongue images using the receiver operating characteristic analysis. Compared with the previous active contour model-based bi-elliptical deformable contour algorithm, the proposed method greatly enhances the segmentation performance, and it could reliably extract the tongue body from different types of tongue images.
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an algorithm based on augmented lagrangian method for generalized Gradient Vector flow computation
Chinese Conference on Pattern Recognition, 2012Co-Authors: Dongwei Ren, Wangmeng Zuo, Xiaofei Zhao, David Zhang, Hongzhi ZhangAbstract:We propose a novel algorithm for the fast computation of generalized Gradient Vector flow (GGVF) whose high cost of computation has restricted its potential applications on images with large size. We reformulate the GGVF problem as a convex optimization model with equality constraint. Our approach is based on a variable splitting method to obtain an equivalent constrained optimization formulation, which is then addressed with the inexact augmented Lagrangian method (IALM). To further enhance the computational efficiency, IALM is incorporated in a multiresolution approach. Experiments on a set of images with a variety of sizes show that the proposed method can improve the computational speed of the original GGVF by one or two order of magnitude, and is comparable with the multigrid GGVF (MGGVF) method in terms of the computational efficiency.
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an augmented lagrangian method for fast Gradient Vector flow computation
International Conference on Image Processing, 2011Co-Authors: Wangmeng Zuo, Xiaofei Zhao, David ZhangAbstract:Gradient Vector flow (GVF) and its generalization have been widely applied in many image processing applications. The high cost of GVF computation, however, has restricted their potential applications to images with large size. In this paper, motivated by progress in fast image restoration algorithms, we reformulate the GVF computation problem as a convex optimization model with an equality constraint, and solve it using a fast algorithm, inexact augmented Lagrangian method (ALM). With fast Fourier transform (FFT), we provide a novel simple and efficient algorithm for GVF computation. Experimental results show that the proposed method can improve the computational speed by an order of magnitude, and is even more efficient for images with large sizes.
