Semantic-based image retrieval bridges the gap between visual features and human understanding of image in the field of image retrieval. image annotation is one important technology of image retrieval based on the sem...
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Text-To-image person search is challenging due to the cross-scale correspondences and information inequality between modalities. Specifically, images and text are complexly linked at different scales and images are us...
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A new prediction algorithm of tourists flow distribution based on transition probability matrix (TPM) is proposed in this paper. In order to analyze the visitor transition-behavior and the tourists distribution model,...
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ISBN:
(纸本)9781467312882
A new prediction algorithm of tourists flow distribution based on transition probability matrix (TPM) is proposed in this paper. In order to analyze the visitor transition-behavior and the tourists distribution model, the tourists flow distribution of 5 zones at Shanghai Expo site is predicted based on the TPM, which is estimated by use of multivariate linear regression in optimization. The extensive experimental results verify the efficiency and the correctness of the proposed algorithm over the wavelet neural network prediction method.
PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,f...
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PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.
Since real imaging systems are imperfect, the acquired images are always distorted by blur, scale and rotation transformation. Then the registration of these degraded images has become an important task in many applic...
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Since real imaging systems are imperfect, the acquired images are always distorted by blur, scale and rotation transformation. Then the registration of these degraded images has become an important task in many applications in which the moment invariants are usually efficient tools. However, the existing methods can only deal with the slightly distorted images and have the problems of information redundancy. Besides, some methods have overlapping constraint that the images to be aligned should be fully included into the reference images. In this paper, we proposed a novel method in which a new set of combined invariants based on Legendre moment holding for blur, rotation and scale degradation simultaneously were constructed as feature descriptors, and scale-invariant Harris-Laplace detector was applied to exact feature points. The experimental results show that our method can work well without overlapping constraint, especially when the distortion is great.
Facial expressions are considered a reliable indicator in neonatal pain *** paper proposes a new neonatal pain expression recognition method,which utilizes the feature descriptors based on weighted Local Binary Patter...
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Facial expressions are considered a reliable indicator in neonatal pain *** paper proposes a new neonatal pain expression recognition method,which utilizes the feature descriptors based on weighted Local Binary Pattern(LBP)and the classifier based on sparse ***,the normalized facial image is described using a feature vector,which is histogram sequence obtained by concatenating the weighted histograms of the LBP maps of all the local ***,the Principal Component Analysis(PCA)method is used to reduce the dimension of the feature ***,the classifier based on sparse representation is applied to classify test sample into four classes of facial expressions:calm,crying,moderate pain,severe *** objective of this study is to assist the clinicians in assessing neonatal pain by utilizing computer-based image analysis *** experimental results on neonate facial image database show the effectiveness of the proposed *** classification accuracy is up to 85.50%.
Modern Single Instruction Multiple Data (SIMD) microprocessor architectures allow parallel floating point operations over four contiguous elements in memory. The radix-2 FFT algorithm is well suited for modern SIMD ar...
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Modern Single Instruction Multiple Data (SIMD) microprocessor architectures allow parallel floating point operations over four contiguous elements in memory. The radix-2 FFT algorithm is well suited for modern SIMD architectures after the second stage (decimation-in-time case). In this paper, a general radix-2 FFT algorithm is developed for the modern SIMD architectures. This algorithm (SIMD-FFT) is implemented on the Intel Pentium and Motorola PowerPC architecture for 1D and 2D. The results are compared against Intel's implementation of the split-radix FFT for the SIMD architecture [2] and the FFTW [3]. Overall, the SIMDFFT was found to be faster than the other two implementations for complex 1D input data (ranging from 95.9% up to 372%), and for complex 2D input data (ranging from 68.8% up to 343%) as well.
A compressive sensing (CS) based mobile video communication system is proposed to meet the requirement that video service needs low-complexity video encoder in the mobile internet. In this system, the mobile client us...
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Previous single-channel speech enhancement algorithms often employ noisy phase while reconstructing the enhanced signal. In this paper, we propose novel phase estimation methods by employing several temporal and spect...
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Previous single-channel speech enhancement algorithms often employ noisy phase while reconstructing the enhanced signal. In this paper, we propose novel phase estimation methods by employing several temporal and spectral constraints imposed on the phase spectrum of speech signal. We pose the phase estimation problem as estimating the unknown clean speech phase at sinusoids observed in additive noise. To resolve the ambiguity in phase estimation problem, we introduce individual time-frequency constraints: group delay deviation, instantaneous frequency deviation, and relative phase shift. Through extensive simulations, the effectiveness of the proposed phase estimation methods in single-channel speech enhancement is demonstrated. Employing the estimated phase for signal reconstruction in medium-to-high SNRs leads to consistent improvement in perceived quality compared to when noisy phase is used.
Mutual information (MI) is an important information theoretic concept which has many applications in telecommunications, in blind source separation, and in machine learning. More recently, it has been also employed fo...
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ISBN:
(纸本)9783800734559
Mutual information (MI) is an important information theoretic concept which has many applications in telecommunications, in blind source separation, and in machine learning. More recently, it has been also employed for the instrumental assessment of speech intelligibility where traditionally correlation based measures are used. In this paper, we address the difference between MI and correlation from the viewpoint of discovering dependencies between variables in the context of speech signals. We perform our investigation by considering the linear predictive approximation and the extrapolation of speech signals as examples. We compare a parametric MI estimation approach based on a Gaussian mixture model (GMM) with the knearest neighbor (KNN) approach which is a well-known non-parametric method available to estimate the MI. We show that the GMM-based MI estimator leads to more consistent results.
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