Sterile insect technique has been successfully applied in the control of agricultural pests, however, it has a limited ability to control mosquitoes. A promising alternative approach is Trojan Y Chromosome strategy, w...
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One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape i...
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ISBN:
(纸本)9781467369657
One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.
In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can...
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An efficient generic architecture for two-dimensional discrete wavelet transform (2-D DWT) with line-based method is proposed with using lifting scheme, in which the parallelism of four subbands transform in lifting-b...
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ISBN:
(纸本)0780390059
An efficient generic architecture for two-dimensional discrete wavelet transform (2-D DWT) with line-based method is proposed with using lifting scheme, in which the parallelism of four subbands transform in lifting-based 2-D DWT is exploited. The proposed architecture is designed to generate 4 subbands coefficients concurrently per clock cycle that :an perform a 1-level decomposition of a N/spl times/N image in approximately N/sup 2//4 working clock cycles, which has faster throughput rate but requires less hardware cost compared 10 the designs reported in previous literature.
We present the design and fabrication of metamaterials, and investigated the power transmission properties of the metamaterials in the frequency ranging from 1.04THz to 4.25THz. The measured results reveal a global ma...
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A sequence {ai |1 ≤ i ≤ k} of integers is a weak Sidon sequence if the sums ai + aj are all different for any i i |1 ≤ i ≤ k} such that 1 ≤ a1k ≤ n. Let the weak Sidon number G(k) = min{n | g(n) = k}. In this no...
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A sequence {ai |1 ≤ i ≤ k} of integers is a weak Sidon sequence if the sums ai + aj are all different for any i i |1 ≤ i ≤ k} such that 1 ≤ a1k ≤ n. Let the weak Sidon number G(k) = min{n | g(n) = k}. In this note, g(n) and G(k) are studied, and g(n) is computed for n ≤ 172, based on which the weak Sidon number G(k) is determined for up to k = 17.
In the paper, we propose a novel ordinal regression method called minimum class variance support vector ordinal regression(MCVSVOR). MCVSVOR is derived from minimum class variance support vector machine(MCVSVM) which ...
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ISBN:
(纸本)9781510812055
In the paper, we propose a novel ordinal regression method called minimum class variance support vector ordinal regression(MCVSVOR). MCVSVOR is derived from minimum class variance support vector machine(MCVSVM) which is a variant of SVM, and so inherits the latter's characteristics such as taking the distribution of the categories into consideration and good generalization performance. Finally, the experimental results validate the effectiveness of MCVSVOR and indicate its superior generalization performance over SVOR.
We present the design and fabrication of metamaterials, and investigated the normalized power transmission properties of four metamaterials at different frequency in terahertz regime. We compared the power transmissio...
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Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative mod...
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Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative model to eliminate cast shadow on Discriminative Random Fields (DRFs). The method combines different features for Boosting to discriminate cast shadow from moving objects, then temporal and spatial coherence of shadow and foreground are incorporated on Discriminative Random Fields and the problem can be solved by graph cut. Firstly, moving objects are obtained by background subtraction;secondly, shadow candidates can be derived through pre-processing moving objects, in terms of the shadow physical property;thirdly, color information and texture information is derived by comparing shadow and foreground points in current image with corresponding points in background image, which are selected as features for Boosting;finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields and discriminate shadow and foreground by graph cut accurately.
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