The Experts below are selected from a list of 44676 Experts worldwide ranked by ideXlab platform
Senem Velipasalar - One of the best experts on this subject based on the ideXlab platform.
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light weight salient foreground detection with adaptive Memory Requirement
International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Mauricio Casares, Senem VelipasalarAbstract:Designing algorithms, which require less Memory and consume less power, is very important for the portability to embedded smart cameras, which have limited resources. We present a light-weight and efficient algorithm for salient foreground detection that is highly robust against lighting variations and non-static backgrounds such as scenes with swaying trees. Contrary to traditional methods, Memory Requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the total Memory Requirement is adaptive, and is decreased even more depending on the amount of activity in the scene. As opposed to existing methods, we treat each pixel differently based on its history. Instead of requiring the same amount of Memory for every pixel, we allocate less Memory for stable background pixels. The plot of the required Memory at each frame also serves as a tool to find the video portions with high activity.
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ICASSP - Light-weight salient foreground detection with adaptive Memory Requirement
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Mauricio Casares, Senem VelipasalarAbstract:Designing algorithms, which require less Memory and consume less power, is very important for the portability to embedded smart cameras, which have limited resources. We present a light-weight and efficient algorithm for salient foreground detection that is highly robust against lighting variations and non-static backgrounds such as scenes with swaying trees. Contrary to traditional methods, Memory Requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the total Memory Requirement is adaptive, and is decreased even more depending on the amount of activity in the scene. As opposed to existing methods, we treat each pixel differently based on its history. Instead of requiring the same amount of Memory for every pixel, we allocate less Memory for stable background pixels. The plot of the required Memory at each frame also serves as a tool to find the video portions with high activity.
Mauricio Casares - One of the best experts on this subject based on the ideXlab platform.
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light weight salient foreground detection with adaptive Memory Requirement
International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Mauricio Casares, Senem VelipasalarAbstract:Designing algorithms, which require less Memory and consume less power, is very important for the portability to embedded smart cameras, which have limited resources. We present a light-weight and efficient algorithm for salient foreground detection that is highly robust against lighting variations and non-static backgrounds such as scenes with swaying trees. Contrary to traditional methods, Memory Requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the total Memory Requirement is adaptive, and is decreased even more depending on the amount of activity in the scene. As opposed to existing methods, we treat each pixel differently based on its history. Instead of requiring the same amount of Memory for every pixel, we allocate less Memory for stable background pixels. The plot of the required Memory at each frame also serves as a tool to find the video portions with high activity.
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ICASSP - Light-weight salient foreground detection with adaptive Memory Requirement
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Mauricio Casares, Senem VelipasalarAbstract:Designing algorithms, which require less Memory and consume less power, is very important for the portability to embedded smart cameras, which have limited resources. We present a light-weight and efficient algorithm for salient foreground detection that is highly robust against lighting variations and non-static backgrounds such as scenes with swaying trees. Contrary to traditional methods, Memory Requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the total Memory Requirement is adaptive, and is decreased even more depending on the amount of activity in the scene. As opposed to existing methods, we treat each pixel differently based on its history. Instead of requiring the same amount of Memory for every pixel, we allocate less Memory for stable background pixels. The plot of the required Memory at each frame also serves as a tool to find the video portions with high activity.
Cyril Gavoille - One of the best experts on this subject based on the ideXlab platform.
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local Memory Requirement of universal routing schemes
ACM Symposium on Parallel Algorithms and Architectures, 1996Co-Authors: Pierre Fraigniaud, Cyril GavoilleAbstract:In this paper we deal with the compact routing problem that is the problem of implementing routing schemes that use a minimum Memory size on each router A universal routing scheme is a scheme that applies to all networks In Peleg and Upfal showed that one can not implement a universal routing scheme with less than a total of n s Memory bits for any routing scheme satisfying that the maximum ratio between the lengths of the routing paths and the lengths of the shortest paths that is the stretch factor is bounded by s In Fraigniaud and Gavoille improve this bound by proving that universal routing schemes of stretch factors at most use a total of n Memory bits in the worst case Recently Gavoille and P erenn es showed that in fact in the worst case n routers of an n node network may require up to n logn Memory bits for shortest path routing In this paper we extend this result by showing that for any constant n routers of an n node network may require up to n logn Memory bits even if each routing path is of length up to twice the distance between its source and its destination
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Local Memory Requirement of universal routing schemes.
1996Co-Authors: Pierre Fraigniaud, Cyril GavoilleAbstract:In this paper, we deal with the compact routing problem, that is the problem of implementing routing schemes that use a minimum Memory size on each router. A universal routing scheme is a scheme that applies to all networks. In [13], Peleg and Upfal showed that one can not implement a universal routing scheme with less than a total of Omega(n^{1+1/(2s+4)}) Memory bits for any routing scheme satisfying that the maximum ratio between the lengths of the routing paths and the lengths of the shortest paths, that is the stretch factor, is bounded by s. In [6], Fraigniaud and Gavoille improve this bound by proving that universal routing schemes of stretch factors at most 2 use a total of Omega(n^2) Memory bits in the worst-case. Recently, Gavoille and Pérennès [9] showed that, in fact, in the worst-case, Theta(n) routers of an n-node network may require up to Omega(n log n) Memory bits for shortest path routing. In this paper, we extend this result by showing that, for any constant e, 0 < e < 1, Omega(n^e) routers of an n-node network may require up to Omega(n log n) Memory bits even if each routing path is of length up to twice the distance between its source and its destination.
Pierre Fraigniaud - One of the best experts on this subject based on the ideXlab platform.
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local Memory Requirement of universal routing schemes
ACM Symposium on Parallel Algorithms and Architectures, 1996Co-Authors: Pierre Fraigniaud, Cyril GavoilleAbstract:In this paper we deal with the compact routing problem that is the problem of implementing routing schemes that use a minimum Memory size on each router A universal routing scheme is a scheme that applies to all networks In Peleg and Upfal showed that one can not implement a universal routing scheme with less than a total of n s Memory bits for any routing scheme satisfying that the maximum ratio between the lengths of the routing paths and the lengths of the shortest paths that is the stretch factor is bounded by s In Fraigniaud and Gavoille improve this bound by proving that universal routing schemes of stretch factors at most use a total of n Memory bits in the worst case Recently Gavoille and P erenn es showed that in fact in the worst case n routers of an n node network may require up to n logn Memory bits for shortest path routing In this paper we extend this result by showing that for any constant n routers of an n node network may require up to n logn Memory bits even if each routing path is of length up to twice the distance between its source and its destination
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Local Memory Requirement of universal routing schemes.
1996Co-Authors: Pierre Fraigniaud, Cyril GavoilleAbstract:In this paper, we deal with the compact routing problem, that is the problem of implementing routing schemes that use a minimum Memory size on each router. A universal routing scheme is a scheme that applies to all networks. In [13], Peleg and Upfal showed that one can not implement a universal routing scheme with less than a total of Omega(n^{1+1/(2s+4)}) Memory bits for any routing scheme satisfying that the maximum ratio between the lengths of the routing paths and the lengths of the shortest paths, that is the stretch factor, is bounded by s. In [6], Fraigniaud and Gavoille improve this bound by proving that universal routing schemes of stretch factors at most 2 use a total of Omega(n^2) Memory bits in the worst-case. Recently, Gavoille and Pérennès [9] showed that, in fact, in the worst-case, Theta(n) routers of an n-node network may require up to Omega(n log n) Memory bits for shortest path routing. In this paper, we extend this result by showing that, for any constant e, 0 < e < 1, Omega(n^e) routers of an n-node network may require up to Omega(n log n) Memory bits even if each routing path is of length up to twice the distance between its source and its destination.
Jiming Song - One of the best experts on this subject based on the ideXlab platform.
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an efficient multilevel fast multipole algorithm to solve volume integral equation for arbitrary inhomogeneous bi anisotropic objects
IEEE Access, 2019Co-Authors: Zengrui Li, Jiming SongAbstract:A volume integral equation (VIE) based on the mixed-potential representation is presented to analyze the electromagnetic scattering from objects involving inhomogeneous bi-anisotropic materials. By discretizing the objects using tetrahedrons on which the commonly used Schaubert-Wilton-Glisson (SWG) basis functions are defined, the matrix equation is derived using the method of moments (MoM) combined with the Galerkin's testing. Further, adopting an integral strategy of tetrahedron-to-tetrahedron scheme, the multilevel fast multipole algorithm (MLFMA) is proposed to accelerate the iterative solution, which is further improved by using the spherical harmonics expansion with a faster implementation and low Memory Requirement. The Memory Requirement of the radiation patterns of basis functions in the proposed MLFMA is several times less than that in the conventional MLFMA.
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the spherical harmonics expansion based multilevel fast multipole algorithm for inhomogeneous bi anisotropic objects
2019 International Applied Computational Electromagnetics Society Symposium - China (ACES), 2019Co-Authors: Limei Luo, Jinbo Liu, Jiming SongAbstract:A volume integral equation (VIE) based on the mixed-potential representation is presented to analyze the electromagnetic scattering from objects involving inhomogeneous bi-anisotropic materials. Adopting an integral strategy of tetrahedron-to-tetrahedron scheme, the multilevel fast multipole algorithm (MLFMA) is proposed to accelerate the iterative solution, which is further improved by using the spherical harmonics expansion with low Memory Requirement as well as without compromising accuracy. The Memory Requirement of the radiation patterns of basis functions in the proposed MLFMA is several times less than that in the conventional MLFMA.
