The Experts below are selected from a list of 198 Experts worldwide ranked by ideXlab platform

Changxin Gao - One of the best experts on this subject based on the ideXlab platform.

  • ACCV (3) - Local Fractional Order Derivative Vector Quantization Pattern for Face Recognition
    Computer Vision – ACCV 2016, 2017
    Co-Authors: Nong Sang, Changxin Gao
    Abstract:

    Previous works have shown that fractional order Derivative can give a better image description compared with conventional integral one in applications of edge detection, image segmentation, image restoration, and so on. Motivated by this conclusion, in this paper, we propose a novel local image descriptor, local fractional order Derivative Vector quantization pattern (fVQP), based on image local directional fractional order Derivative feature Vector and Vector quantization method for face recognition. Compared with image integral order Derivative information based local binary pattern (LBP), local Derivative pattern (LDP) and local directional Derivative pattern (LDDP), our fVQP image descriptor has the advantages of better image recognition performance and robust to noise. Extensive experimental results conducted on four benchmark face databases demonstrate the superior performance of our fVQP compared with existing state-of-the-art descriptors for face recognition in terms of recognition rate.

  • local fractional order Derivative Vector quantization pattern for face recognition
    Asian Conference on Computer Vision, 2016
    Co-Authors: Nong Sang, Changxin Gao
    Abstract:

    Previous works have shown that fractional order Derivative can give a better image description compared with conventional integral one in applications of edge detection, image segmentation, image restoration, and so on. Motivated by this conclusion, in this paper, we propose a novel local image descriptor, local fractional order Derivative Vector quantization pattern (fVQP), based on image local directional fractional order Derivative feature Vector and Vector quantization method for face recognition. Compared with image integral order Derivative information based local binary pattern (LBP), local Derivative pattern (LDP) and local directional Derivative pattern (LDDP), our fVQP image descriptor has the advantages of better image recognition performance and robust to noise. Extensive experimental results conducted on four benchmark face databases demonstrate the superior performance of our fVQP compared with existing state-of-the-art descriptors for face recognition in terms of recognition rate.

Nong Sang - One of the best experts on this subject based on the ideXlab platform.

  • ACCV (3) - Local Fractional Order Derivative Vector Quantization Pattern for Face Recognition
    Computer Vision – ACCV 2016, 2017
    Co-Authors: Nong Sang, Changxin Gao
    Abstract:

    Previous works have shown that fractional order Derivative can give a better image description compared with conventional integral one in applications of edge detection, image segmentation, image restoration, and so on. Motivated by this conclusion, in this paper, we propose a novel local image descriptor, local fractional order Derivative Vector quantization pattern (fVQP), based on image local directional fractional order Derivative feature Vector and Vector quantization method for face recognition. Compared with image integral order Derivative information based local binary pattern (LBP), local Derivative pattern (LDP) and local directional Derivative pattern (LDDP), our fVQP image descriptor has the advantages of better image recognition performance and robust to noise. Extensive experimental results conducted on four benchmark face databases demonstrate the superior performance of our fVQP compared with existing state-of-the-art descriptors for face recognition in terms of recognition rate.

  • local fractional order Derivative Vector quantization pattern for face recognition
    Asian Conference on Computer Vision, 2016
    Co-Authors: Nong Sang, Changxin Gao
    Abstract:

    Previous works have shown that fractional order Derivative can give a better image description compared with conventional integral one in applications of edge detection, image segmentation, image restoration, and so on. Motivated by this conclusion, in this paper, we propose a novel local image descriptor, local fractional order Derivative Vector quantization pattern (fVQP), based on image local directional fractional order Derivative feature Vector and Vector quantization method for face recognition. Compared with image integral order Derivative information based local binary pattern (LBP), local Derivative pattern (LDP) and local directional Derivative pattern (LDDP), our fVQP image descriptor has the advantages of better image recognition performance and robust to noise. Extensive experimental results conducted on four benchmark face databases demonstrate the superior performance of our fVQP compared with existing state-of-the-art descriptors for face recognition in terms of recognition rate.

Robert A. Akins - One of the best experts on this subject based on the ideXlab platform.

  • Shuttle Vectors for Candida albicans: control of plasmid copy number and elevated expression of cloned genes.
    Current genetics, 2004
    Co-Authors: Melisa Coaker, Jack D. Sobel, Robert A. Akins
    Abstract:

    Plasmids containing the inosine monophosphate dehydrogenase gene CaIMH3 from Candida albicans strain ATCC 32354 transform their host to resistance against mycophenolic acid (MPA). The transformants maintain the plasmids at a high copy number (20–40 per cell) and express the CaIMH3 gene at very high levels relative to untransformed controls. The plasmid copy number can be controlled by the concentration of MPA in the media. The transformation procedure is reproducible and the efficiency of transformation is high, up to 15,000 per microgram. Unrearranged plasmids are readily recovered by transforming total DNA from transformants back into Escherichia coli. C. albicans genes cloned into the plasmid are expressed at elevated levels relative to untransformed controls. A Derivative Vector containing the CaMAL2 promoter and termination sequences expresses the CaERG11 ORF at high levels and confers moderate resistance to fluconazole. These shuttle Vectors should facilitate global genomics approaches in C. albicans that have been hampered by its diploid genome.

S. Thiria - One of the best experts on this subject based on the ideXlab platform.

  • YAO: A Software for Variational Data Assimilation Using Numerical Models
    Computational Science and Its Applications – ICCSA 2009, 2009
    Co-Authors: Luigi Nardi, Charles Sorror, F. Badran, S. Thiria
    Abstract:

    Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and some actual observations. The gradient based minimization methods require the multiplication of the transpose jacobian matrix (adjoint model), which is of huge dimension, with the Derivative Vector of the cost function at the observation points. We present a method based on a modular graph concept and two algorithms to avoid these expensive multiplications. The first step of the method is a propagation algorithm on the graph that allows computing the output of the numerical model and its linear tangent, the second is a backpropagation on the graph that allows the computation of the adjoint model. The YAO software implements these two steps using appropriate algorithms. We present a brief description of YAO functionalities.

  • ICCSA (2) - YAO: A Software for Variational Data Assimilation Using Numerical Models
    Computational Science and Its Applications – ICCSA 2009, 2009
    Co-Authors: Luigi Nardi, Charles Sorror, F. Badran, S. Thiria
    Abstract:

    Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and some actual observations. The gradient based minimization methods require the multiplication of the transpose jacobian matrix (adjoint model), which is of huge dimension, with the Derivative Vector of the cost function at the observation points. We present a method based on a modular graph concept and two algorithms to avoid these expensive multiplications. The first step of the method is a propagation algorithm on the graph that allows computing the output of the numerical model and its linear tangent, the second is a backpropagation on the graph that allows the computation of the adjoint model. The YAO software implements these two steps using appropriate algorithms. We present a brief description of YAO functionalities.

Pan Jingchang - One of the best experts on this subject based on the ideXlab platform.

  • Determining knots by minimizing second Derivative Vector
    Journal of Computer Applications, 2008
    Co-Authors: Pan Jingchang
    Abstract:

    One of key problems of constructing parametric fitting curves is to compute a knot for each point.A new method for determining knots was presented.Corresponding to each data point,the new method constructed a quadratic curve that passed three consecutive points;the knots of the quadratic curve were determined by minimizing the second Derivative Vector of the curve.The knot interval between two adjacent points was determined by the two quadratic curves.The new method determined the knots with a local way,which made it useful in interactive design of curve and surface.The experimental results in this paper show that the new method is more effective than the other existing methods.

  • Construction of a parametric cubic curve with quadratic precision
    Journal of Shandong University, 2008
    Co-Authors: Pan Jingchang
    Abstract:

    A method for constructing a parametric cubic curve to interpolate a set of distinct data points was presented.Unlike existing methods,this method includes the determination of knots in the process of constructing a parametric curve, and the new method can construct an interpolation curve without the process of determining knots.Between each pair of data points,a cubic Hermite interpolation curve segment was constructed by the new method,and all the curve segments are put together to form the whole interpolation curve.Hence,the key of this new method is to compute the Derivative Vector at each data point.For each data point,this new method constructs a quadratic polynomial curve using five or four data points,and the Derivative Vector at each data point was computed by the quadratic polynomial curve.The constructed cubic polynomial curve has the precision of the quadratic polynomial.Experiments for testing the efficiency of the new method with the existing ones were included,and comparison results show that the curves by this new method have better precision.