The Experts below are selected from a list of 178401 Experts worldwide ranked by ideXlab platform

Tsungyung Hung - One of the best experts on this subject based on the ideXlab platform.

  • local vector pattern in high order Derivative space for face recognition
    International Conference on Image Processing, 2014
    Co-Authors: Tsungyung Hung, Kuochin Fan
    Abstract:

    In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in High-Order Derivative space is presented for face recognition. The proposed vector representation of the referenced pixel is generated to provide the one-dimensional structure of micropatterns. To effectively extract more detailed discriminative information in a given sub-region, the vector of LVP is refined by varying local Derivative directions from the nth-order LVP in (n−1)th-order Derivative space. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local Derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. Extensive experiments conducting on benchmark face image databases, FERET and Extended Yale B, demonstrate that the proposed LVP in High-Order Derivative space indeed performs much better than LBP, LDP and LTrP for face recognition.

  • a novel local pattern descriptor local vector pattern in high order Derivative space for face recognition
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Kuochin Fan, Tsungyung Hung
    Abstract:

    In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in High-Order Derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micropatterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given subregion, the vector of LVP is refined by varying local Derivative directions from the \(n\) th-order LVP in \((n-1)\) th-order Derivative space, which is a much more resilient structure of micropatterns than standard local pattern descriptors. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local Derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in High-Order Derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition.

Kuochin Fan - One of the best experts on this subject based on the ideXlab platform.

  • local vector pattern in high order Derivative space for face recognition
    International Conference on Image Processing, 2014
    Co-Authors: Tsungyung Hung, Kuochin Fan
    Abstract:

    In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in High-Order Derivative space is presented for face recognition. The proposed vector representation of the referenced pixel is generated to provide the one-dimensional structure of micropatterns. To effectively extract more detailed discriminative information in a given sub-region, the vector of LVP is refined by varying local Derivative directions from the nth-order LVP in (n−1)th-order Derivative space. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local Derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. Extensive experiments conducting on benchmark face image databases, FERET and Extended Yale B, demonstrate that the proposed LVP in High-Order Derivative space indeed performs much better than LBP, LDP and LTrP for face recognition.

  • a novel local pattern descriptor local vector pattern in high order Derivative space for face recognition
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Kuochin Fan, Tsungyung Hung
    Abstract:

    In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in High-Order Derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micropatterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given subregion, the vector of LVP is refined by varying local Derivative directions from the \(n\) th-order LVP in \((n-1)\) th-order Derivative space, which is a much more resilient structure of micropatterns than standard local pattern descriptors. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local Derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in High-Order Derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition.

Omar Ghattas - One of the best experts on this subject based on the ideXlab platform.

  • tensor train construction from tensor actions with application to compression of large high order Derivative tensors
    SIAM Journal on Scientific Computing, 2020
    Co-Authors: Nick Alger, Peng Chen, Omar Ghattas
    Abstract:

    We present a method for converting tensors into the tensor train format based on actions of the tensor as a vector-valued multilinear function. Existing methods for constructing tensor trains requi...

  • Tensor train construction from tensor actions, with application to compression of large high order Derivative tensors.
    arXiv: Numerical Analysis, 2020
    Co-Authors: Nick Alger, Peng Chen, Omar Ghattas
    Abstract:

    We present a method for converting tensors into tensor train format based on actions of the tensor as a vector-valued multilinear function. Existing methods for constructing tensor trains require access to "array entries" of the tensor and are therefore inefficient or computationally prohibitive if the tensor is accessible only through its action, especially for high order tensors. Our method permits efficient tensor train compression of large high order Derivative tensors for nonlinear mappings that are implicitly defined through the solution of a system of equations. Array entries of these Derivative tensors are not directly accessible, but actions of these tensors can be computed efficiently via a procedure that we discuss. Such tensors are often amenable to tensor train compression in theory, but until now no efficient algorithm existed to convert them into tensor train format. We demonstrate our method by compressing a Hilbert tensor of size $41 \times 42 \times 43 \times 44 \times 45$, and by forming high order (up to $5^\text{th}$ order Derivatives/$6^\text{th}$ order tensors) Taylor series surrogates of the noise-whitened parameter-to-output map for a stochastic partial differential equation with boundary output.

Muhammad Usman Rafique - One of the best experts on this subject based on the ideXlab platform.

  • a novel recurrent neural network for manipulator control with improved noise tolerance
    IEEE Transactions on Neural Networks, 2018
    Co-Authors: Huanqing Wang, Muhammad Usman Rafique
    Abstract:

    In this paper, we propose a novel recurrent neural network to resolve the redundancy of manipulators for efficient kinematic control in the presence of noises in a polynomial type. Leveraging the High-Order Derivative properties of polynomial noises, a deliberately devised neural network is proposed to eliminate the impact of noises and recover the accurate tracking of desired trajectories in workspace. Rigorous analysis shows that the proposed neural law stabilizes the system dynamics and the position tracking error converges to zero in the presence of noises. Extensive simulations verify the theoretical results. Numerical comparisons show that existing dual neural solutions lose stability when exposed to large constant noises or time-varying noises. In contrast, the proposed approach works well and has a low tracking error comparable to noise-free situations.

Nick Alger - One of the best experts on this subject based on the ideXlab platform.

  • tensor train construction from tensor actions with application to compression of large high order Derivative tensors
    SIAM Journal on Scientific Computing, 2020
    Co-Authors: Nick Alger, Peng Chen, Omar Ghattas
    Abstract:

    We present a method for converting tensors into the tensor train format based on actions of the tensor as a vector-valued multilinear function. Existing methods for constructing tensor trains requi...

  • Tensor train construction from tensor actions, with application to compression of large high order Derivative tensors.
    arXiv: Numerical Analysis, 2020
    Co-Authors: Nick Alger, Peng Chen, Omar Ghattas
    Abstract:

    We present a method for converting tensors into tensor train format based on actions of the tensor as a vector-valued multilinear function. Existing methods for constructing tensor trains require access to "array entries" of the tensor and are therefore inefficient or computationally prohibitive if the tensor is accessible only through its action, especially for high order tensors. Our method permits efficient tensor train compression of large high order Derivative tensors for nonlinear mappings that are implicitly defined through the solution of a system of equations. Array entries of these Derivative tensors are not directly accessible, but actions of these tensors can be computed efficiently via a procedure that we discuss. Such tensors are often amenable to tensor train compression in theory, but until now no efficient algorithm existed to convert them into tensor train format. We demonstrate our method by compressing a Hilbert tensor of size $41 \times 42 \times 43 \times 44 \times 45$, and by forming high order (up to $5^\text{th}$ order Derivatives/$6^\text{th}$ order tensors) Taylor series surrogates of the noise-whitened parameter-to-output map for a stochastic partial differential equation with boundary output.