The estimation of image resampling factors is an important problem in image *** all the resampling factor estimation methods,spectrumbased methods are one of the most widely used methods and have attracted a lot of re...
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The estimation of image resampling factors is an important problem in image *** all the resampling factor estimation methods,spectrumbased methods are one of the most widely used methods and have attracted a lot of research ***,because of inherent ambiguity,spectrum-based methods fail to discriminate upscale and downscale operations without any prior *** general,the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled ***,the resampling process will introduce correlations between neighboring *** this case,a set of periodic pixels that are correlated to their neighbors can be found in a resampled ***,the resampled image has distinct and strong peaks on spectrum while the spectrum of original image has no clear ***,in this paper,we propose a dual-stream convolutional neural network for image resampling factors *** of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled *** other is frequency stream that discovers the differences of spectrum between rescaled and original *** features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and frequency domain,which is later fed into softmax layer for resampling factor *** results show that the proposed method is effective on resampling factor estimation and outperforms some CNN-based methods.
We present a new perspective of achieving image synthesis by viewing this task as a visual token generation problem. Different from existing paradigms that directly synthesize a full image from a single input (e.g., a...
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Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static netwo...
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Emotion recognition in conversations (ERC) has received significant attention in recent years due to its widespread applications in diverse areas, such as social media, health care, and artificial intelligence interac...
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Emotion recognition based on text modality has been one of the major topics in the field of emotion recognition in conversation. How to extract efficient emotional features is still a challenge. Previous studies utili...
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In this paper,a GZ-type complex neuronet（generated by combining gradient dynamic system and Zhang dynamic system） is illustrated and discretized into an Euler-discretized（i.e.,discretized by Euler forward rule） GZ...
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ISBN:
(纸本)9781538629185
In this paper,a GZ-type complex neuronet（generated by combining gradient dynamic system and Zhang dynamic system） is illustrated and discretized into an Euler-discretized（i.e.,discretized by Euler forward rule） GZ-type complex neuronet for computing real-time varying complex matrix *** results not only show the efficacy of the proposed Eulerdiscretized GZ-type neuronet but also verify that the error pattern is square.
Emotion recognition from speech is an important field of research in human computer interaction. In this letter the framework of Support Vector machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduce...
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Emotion recognition from speech is an important field of research in human computer interaction. In this letter the framework of Support Vector machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduced for emotional speech recognition. Because of the importance of variance in reflecting the distribution of speech, the normalized mean vectors potential to exploit the information from the variance are adopted to form the GMM supervector. Comparative experiments from five aspects are conducted to study their corresponding effect to system performance. The experiment results, which indicate that the influence of number of mixtures is strong as well as influence of duration is weak, provide basis for the train set selection of Universal Background Model (UBM).
Mapping of IP(Intellectual Property) cores onto NoC(Network-on-Chip) architectures is a key step in NoCbased designs. Energy is the key parameter to measure the designs. Therefore, we propose an Improved Simulated Ann...
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Mapping of IP(Intellectual Property) cores onto NoC(Network-on-Chip) architectures is a key step in NoCbased designs. Energy is the key parameter to measure the designs. Therefore, we propose an Improved Simulated Annealing Genetic Alogrithm, abbreviated as ISAGA. The algorithm combines the parallelism of Genetic Algorithm(GA) and the local search ability of Simulated Annealing(SA). We improve the initial population selection of GA to get the lower power consumption mapping scheme. The experimental results show that compared with the GA, ISAGA has good convergence and can search the optimal solution quickly, which can effectively reduce the power consumption of the system. In the case of 124 IP cores, the average power consumption of the ISAGA is reduced by 32.0% compared with the GA.
Traditional 3D NoC router may encounter the situations of congestion and hot spot. And it also doesn't have the function of multicast. In this paper, we present a new routing arithmetic called Multicast Rotational...
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The motion information contained in surface electromyography (sEMG) signals contributes significantly to the prosthetic hand control. However, the accuracy and speed of gesture recognition from sEMG signals are still ...
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