30 research outputs found

    Classification results between positive and negative samples by SVM method.

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    <p>The solid lines represent the optimal classification hyperplanes constructed for (A) start samples and (B) end samples.</p

    Hydrophobicity and length characteristics analyzed on RNH domains.

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    <p>RNHs in lentivirus are hydrophilic whose lengths range from 105 AA to 127 AA, RNHs in alpha-retrovirus, beta-retrovirus and delta-retrovirus are hydrophilic or hydrophobic whose lengths range from 130 AA to 136 AA, RNHs in epsilon-retrovirus and gamma-retrovirus are hydrophilic whose lengths range from 146 AA to 148 AA, while RNHs in spumavirus are hydrophilic whose lengths range from 159 AA to 160 AA.</p

    Comparison results between RNH tool and three other classical database search tools.

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    <p>Comparison results between RNH tool and three other classical database search tools.</p

    Change of average <i>Mcc</i> values versus different sample lengths.

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    <p>(A) Performance changes along with sample lengths from 10 AA to 100 AA under jack-knife test. (B)-(C) Performance varies along with optimal sample lengths selected in Fig 1(A) under 10-fold CV test. SD denotes the standard deviation of <i>Mcc</i> values.</p

    RNH motif plotted by weblogo software.

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    <p>The overall height of the stack indicates the sequence conservation at each position, the height of symbols within the stack represents the relative frequency of amino acid at that position, while the four underlined residues (D10, E58, D81 and D153) display the DEDD motif in RNH domain.</p

    Multispectral image fusion for illumination-invariant palmprint recognition

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    <div><p>Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.</p></div

    Demonstration of the effects of different settings in FABEMD: (a) experiments without illumination compensation, and (b) experiments with illumination compensation.

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    <p>Demonstration of the effects of different settings in FABEMD: (a) experiments without illumination compensation, and (b) experiments with illumination compensation.</p

    Time cost of the proposed method.

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    <p>Time cost of the proposed method.</p

    Performance of tensor-based extreme learning machine with different number of hidden nodes.

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    <p>Performance of tensor-based extreme learning machine with different number of hidden nodes.</p

    Demonstration of how to generate a noised palmprint image.

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    <p>Demonstration of how to generate a noised palmprint image.</p
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