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51.
A Synthetic Transcriptional Activator of Genes Associated with the Retina in Human Dermal Fibroblasts 下载免费PDF全文
52.
In this paper, we propose a novel approach for key frames extraction on human action recognition from 3D video sequences. To represent human actions, an Energy Feature (EF), combining kinetic energy and potential energy, is extracted from 3D video sequences. A Self-adaptive Weighted Affinity Propagation (SWAP) algorithm is then proposed to extract the key frames. Finally, we employ SVM to recognize human actions on the EFs of selected key frames. The experiments show the information including whole action course can be effectively extracted by our method, and we obtain good recognition performance without losing classification accuracy. Moreover, the recognition speed is greatly improved. 相似文献
53.
In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods. 相似文献
54.
Masoud Muhammed Hassan Haval Ismael Hussein Adel Sabry Eesa Ramadhan J. Mstafa 《计算机、材料和连续体(英文)》2021,68(2):1637-1659
Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system. 相似文献
55.
Automatic affect recognition in real-world environments is an important task towards a natural interaction between humans and machines. The recent years, several advancements have been accomplished in determining the emotional states with the use of Deep Neural Networks (DNNs). In this paper, we propose an emotion recognition system that utilizes the raw text, audio and visual information in an end-to-end manner. To capture the emotional states of a person, robust features need to be extracted from the various modalities. To this end, we utilize Convolutional Neural Networks (CNNs) and propose a novel transformer-based architecture for the text modality that can robustly capture the semantics of sentences. We develop an audio model to process the audio channel, and adopt a variation of a high resolution network (HRNet) to process the visual modality. To fuse the modality-specific features, we propose novel attention-based methods. To capture the temporal dynamics in the signal, we utilize Long Short-Term Memory (LSTM) networks. Our model is trained on the SEWA dataset of the AVEC 2017 research sub-challenge on emotion recognition, and produces state-of-the-art results in the text, visual and multimodal domains, and comparable performance in the audio case when compared with the winning papers of the challenge that use several hand-crafted and DNN features. Code is available at: https://github.com/glam-imperial/multimodal-affect-recognition. 相似文献
56.
This paper proposes a new deep learning architecture for context-based multi-label multi-task emotion recognition. The architecture is built from three main modules: (1) a body features extraction module, which is a pre-trained Xception network, (2) a scene features extraction module, based on a modified VGG16 network, and (3) a fusion-decision module. Moreover, three categorical and three continuous loss functions are compared in order to point out the importance of the synergy between loss functions when it comes to multi-task learning. Then, we propose a new loss function, the multi-label focal loss (MFL), based on the focal loss to deal with imbalanced data. Experimental results on EMOTIC dataset show that MFL with the Huber loss gave better results than any other combination and outperformed the current state of art on the less frequent labels. 相似文献
57.
传统的图像识别方法需要大量有标签样本进行训练,且模型训练难以达到稳定。针对这些问题,结合条件生成网络和信息最大化生成网络的结构优势建立了条件信息卷积生成网络(C-Info-DCGAN)。模型增加图像的类别信息和潜在信息作为输入数据,然后利用Q网络去更好地发挥类别信息和潜在信息对训练的引导作用,并且利用深度卷积网络来加强对图像特征的提取能力。实验结果表明,该方法能够加快模型训练收敛速度,并有效提高图像识别的准确率。 相似文献
58.
Zhiyu Sun Ethan Rooke Jerome Charton Yusen He Jia Lu Stephen Baek 《Computer Graphics Forum》2020,39(6):204-216
In this paper, we propose a novel formulation extending convolutional neural networks (CNN) to arbitrary two-dimensional manifolds using orthogonal basis functions called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific trends and phenomena, where accurate numerical quantification of geometric features is critical. Recently, CNNs have demonstrated a substantial improvement in extracting and codifying geometric features. However, the progress is mostly centred around computer vision and its applications where an inherent grid-like data representation is naturally present. In contrast, many geometry processing problems deal with curved surfaces and the application of CNNs is not trivial due to the lack of canonical grid-like representation, the absence of globally consistent orientation and the incompatible local discretizations. In this paper, we show that the Zernike polynomials allow rigourous yet practical mathematical generalization of CNNs to arbitrary surfaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike coefficients and the rotation of a convolution kernel is essentially a set of 2 × 2 rotation matrices applied to the coefficients. The key contribution of this work is in such a computationally efficient but rigorous generalization of the major CNN building blocks. 相似文献
59.
无人机信号的探测识别技术是应对无人机黑飞滥用的关键技术之一。在实际信号监测环境中,经常会接收到多个信号的混合信号,它们在时域和频域上混叠且各信号分量调制样式相同。为解决在同频段混合信号中检测识别出无人机信号的问题,提出了一种通过谱特征分析判断无人机信号存在性的方法。分别采用基于二次方谱特征的无人机图传和WiFi混合信号检测识别算法以及基于频谱带宽特征的多无人机混合信号检测识别算法,通过对射频电路采集的信号进行仿真验证,实现了从同频段混合信号中检测识别出无人机信号分量。理论分析和实验测试结果证实了所提检测识别算法的有效性。 相似文献
60.
For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection,the spatial characteristics of adjacent between CFCC were studied,and the spatial gradient structure of CFCC based on points was established.On this basis,the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally,a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%. 相似文献