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| 1 | Multiple Feature Fusion in Convolutional Neural Networks for Action Recognition显示文摘Action recognition is important for understanding the human behaviors in the video,and the video representation is the basis for action recognition.This paper provides a new video representation based on convolution neural networks(CNN).For capturing human motion information in one CNN,we take both the optical flow maps and gray images as input,and combine multiple convolutional features by max pooling across frames.In another CNN,we input single color frame to capture context information.Finally,we take the top full connected layer vectors as video representation and train the classifiers by linear support vector machine.The experimental results show that the representation which integrates the optical flow maps and gray images obtains more discriminative properties than those which depend on only one element.On the most challenging data sets HMDB51 and UCF101,this video representation obtains competitive performance. | LI Hongyang CHEN Jun HU Ruimin | 2017 | Wuhan University Journal of Natural Sciences2017，22，1： | 4 |
| 2 | Speech wideband extension based on Gaussian mixture model显示文摘减少光谱 highband 信封的失真,功能光谱在特征向量和 highband 信封之间的失真和相互的信息被学习,并且一个扩大 Gaussian 混合模型(GMM ) 带宽延期算法基于研究被建议。与 highband 信封有更大的相互的信息的特征参数被选择组成特征向量,并且 GMM 被采用计算特征向量和 highband 信封的联合概率密度。然后, highband 信封经由从期望最大化(他们) 算法估计的模型参数计算的以后的可能性被估计。试验性的结果证明光谱失真比算法低,例如传统的算法基于 GMM,由 0.3 dB 和框架与的数字光谱在 10 dB 上的失真严厉地减少了超过 50% 。 | ZHANG Yong HU Ruimin | 2009 | Chinese Journal of Acoustics2009，28，4： | 4 |
| 3 | The “1-3-7” Approach to Malaria Surveillance and Response--Henan Province, China, 2012−2018显示文摘What is already known about this topic?The“1-3-7”approach to malaria surveillance and response was a key measure for malaria elimination in China and was first introduced into the World Health Organization(WHO)as an international guideline for malaria surveillance and response in 2018.What is added by this report?The“1-3-7”approach was well implemented in Henan Province from 2012−2018.Over this study period,a total of 1,294 malaria cases were detected and reported,and all cases were diagnosed and reported within 1 day with 99.23%(1,284/1,294)of cases were investigated within 3 days.In addition,93.7%(1,212/1,294)of foci were investigated and vector control was implemented within 7 days at all residual non-active foci to prevent further spread.What are the implications for public health practice?The“1-3-7”controlling pattern would be an effective and approachable method for implementation especially in malaria-eliminating countries and regions,but the interval from symptom onset to diagnosis cannot be ignored.Thus,the roles and responsibilities that all actors involved in the health sector must be specified too. | Qunqun Zhang Ying Liu Yabo Hu Yuling Zhao Chengyun Yang Dan Qian Ruimin Zhou Suhua Li Zhou Guan Deling Lu Hongwei Zhang Wanshen Guo | 2020 | China CDC weekly2020，2，17： | 3 |
| 4 | Nonlinear Prediction with Deep Recurrent Neural Networks for Non-Blind Audio Bandwidth Extension显示文摘Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods. | Lin Jiang Ruimin Hu Xiaochen Wang Weiping Tu Maosheng Zhang | 2018 | China Communications2018，15，1： | 2 |
| 5 | Arbitrary Scale Super Resolution Network for Satellite Imagery显示文摘Recently,satellite imagery has been widely applied in many areas.However,due to the limitations of hardware equipment and transmission bandwidth,the images received on the ground have low resolution and weak texture.In addition,since ground terminals have various resolutions and real-time playing requirements,it is essential to achieve arbitrary scale super-resolution(SR)of satellite images.In this paper,we propose an arbitrary scale SR network for satellite image reconstruction.First,we propose an arbitrary upscale module for satellite imagery that can map low-resolution satellite image features to arbitrary scale enlarged SR outputs.Second,we design an edge reinforcement module to enhance the highfrequency details in satellite images through a twobranch network.Finally,extensive upsample experiments on WHU-RS19 and NWPU-RESISC45 datasets and subsequent image segmentation experiments both show the superiority of our method over the counterparts. | Jing Fang Jing Xiao Xu Wang Dan Chen Ruimin Hu | 2022 | China Communications2022，19，8： | 2 |
| 6 | Structural iMoSIFT for Human Action Recognition显示文摘Classic local space-time features are successful representations for action recognition in videos. However, these features always confuse object motions with camera motions, which seriously affect the accuracy of action recognition. In this paper, we propose improved motion scale-inviriant feature transform(iMoSIFT) algorithm to eliminate the negative effects caused by camera motions. Based on iMoSIFT, we consider the spatial-temporal structure relationship among iMoSIFT interest points, and adopt locally weighted word context descriptors to code this relationship. Then, we use two-layer BOW representation for every video clip. The proposed approach is evaluated on available datasets, namely Weizemann, KTH and UCF sports. The experimental results clearly demonstrate the effectiveness of the proposed approach. | CHEN Huafeng CHEN Jun HU Ruimin | 2016 | Wuhan University Journal of Natural Sciences2016，21，3： | 2 |
| 7 | Pedestrian Attributes Recognition in Surveillance Scenarios with Hierarchical Multi-Task CNN Models显示文摘Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively. | Wenhua Fang Jun Chen Ruimin Hu | 2018 | China Communications2018，15，12： | 2 |
| 8 | Object tracking algorithm based on meanshift algorithm combining with motion vector analysis显示文摘 | TIAN Gang HU RuiMin WANG ZhongYuan ZHU Li | 2009 | IEEE First In- ternational Workshop on Education Technology and Com- puter Science ( $7695 - 3557)2009，10，： | 1 |
| 9 | Fast sy- nopsis for moving objects using compressed video 显示文摘 | Zhong Rui Hu Ruimin Wang Zhongyuan | 2014 | IEEE Signal Processing Letters2014，21，7： | 1 |
| 10 | Structural Sparse Representation for Object Detection显示文摘Classic sparse representation,as one of prevalent feature learning methods,is successfully applied for different computer vision tasks.However it has some intrinsic defects in object detection.Firstly,how to learn a discriminative dictionary for object detection is a hard problem.Secondly,it is usually very time-consuming to learn dictionary based features in a traditional exhaustive search manner like sliding window.In this paper,we propose a novel feature learning framework for object detection with the structure sparsity constraint and classification error minimization constraint to learn a discriminative dictionary.For improving the efficiency,we just learn sparse representation coefficients from object candidate regions and feed them to a kernelized SVM classifier.Experiments on INRIA Person Dataset and Pascal VOC 2007 challenge dataset clearly demonstrate the effectiveness of the proposed approach compared with two state-of-the-art baselines. | FANG Wenhua CHEN Jun HU Ruimin | 2017 | Wuhan University Journal of Natural Sciences2017，22，4： | 1 |
| 11 | An Enhanced SYN Cookie Defence Method for TCP DDoS Attack显示文摘 | Bo Hang Ruimin Hu Wei Shi | 2011 | Journal of Networks2011，，8： | 1 |
| 12 | Super-Resolution for Face Image with an Improved K-NN Search Strategy显示文摘Recently, neighbor embedding based face super-resolution(SR) methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution(LR) training set and high-resolution(HR) training set. However, due to the 'one-to-many' mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified K-nearest neighbor(RIKNN) method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches K-NN of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improved performance. A statistical analysis of the influence of the training set size and nearest neighbor number is given, experimental results on some public face databases show the superiority of our proposed scheme over state-of-the-art face hallucination approaches in terms of subjective and objective results as well as computational complexity. | QU Shenming HU Ruimin CHEN Shihong JIANG Junjun WANG Zhongyuan ZHANG Maosheng | 2016 | China Communications2016，13，4： | 1 |
| 13 | Individualization of Head Related Impulse Responses Using Division Analysis显示文摘For Virtual Reality(VR) to be truly immersive, it needs convincing sound to match. Due to the diversity of individual's anthropometric measurements, the individualized customization technology is needed to get convincing sound. In this paper, we proposed a simple and effective method for modeling relationships between anthropometric measurements and Head-related Impulse Response(HRIR). Considering the relationship between anthropometric measurements and different HRIR parts is complicated, we divided the HRIRs into small segments and carried out regression analysis between anthropometric measurements and each segment to establish relationship model. The results of objective simulation and subjective test indicate that the model can generate individualize HRIRs from a series of anthropometric measurements. With the individualized HRIRs, we can get more accurate acoustic localization sense than using non-individualized HRIRs. | Wei Chen Ruimin Hu Xiaochen Wang Cheng Yang Lian Meng | 2018 | China Communications2018，15，5： | 1 |
| 14 | Action Recognition with Temporal Scale-Invariant Deep Learning Framework显示文摘Recognizing actions according to video features is an important problem in a wide scope of applications. In this paper, we propose a temporal scale.invariant deep learning framework for action recognition, which is robust to the change of action speed. Specifically, a video is firstly split into several sub.action clips and a keyframe is selected from each sub.action clip. The spatial and motion features of the keyframe are extracted separately by two Convolutional Neural Networks(CNN) and combined in the convolutional fusion layer for learning the relationship between the features. Then, Long Short Term Memory(LSTM) networks are applied to the fused features to formulate long.term temporal clues. Finally, the action prediction scores of the LSTM network are combined by linear weighted summation. Extensive experiments are conducted on two popular and challenging benchmarks, namely, the UCF.101 and the HMDB51 Human Actions. On both benchmarks, our framework achieves superior results over the state.of.the.art methods by 93.7% on UCF.101 and 69.5% on HMDB51, respectively. | Huafeng Chen Jun Chen Ruimin Hu Chen Chen Zhongyuan Wang | 2017 | China Communications2017，14，2： | 1 |
| 15 | Ni/Al layered double hydroxide nanosheet film grown directly on Ti substrate and its application for a nonenzymatic glucose sensor显示文摘 | Xin Li Jinping Liu Xiaoxu Ji Jian Jiang Ruimin Ding Yingying Hu Anzheng Hu Xintang Huang | 2010 | Sensors & Actuators: B. Chemical2010，，： | 1 |
| 16 | Intra coding and refresh with video compression-oriented epitomic priors显示文摘 | Wang Qijun Hu Ruimin Wang Zhongyuan | 2012 | IEEE Transactions on Circuits and Systems for Video Tech-nology2012，22，5： | 1 |
| 17 | Robust Background Subtraction Method via Low-Rank and Structured Sparse Decomposition显示文摘Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios. | Minsheng Ma Ruimin Hu Shihong Chen Jing Xiao Zhongyuan Wang | 2018 | China Communications2018，15，7： | 1 |
| 18 | Introduction to AVS audio显示文摘 | Ai Haojun Chen Shuixian Hu Ruimin | 2006 | J Conmpute Sci and Technol2006，21，3： | 1 |
| 19 | Intra coding and refresh with video compression-oriented epitomic priors显示文摘 | Wang Qijun Hu Ruimin Wang Zhongyuan | 2012 | IEEE Transactions on Circuits and Systems for Video Technology2012，22，5： | 1 |
| 20 | Depth Similarity Enhanced Image Summarization Algorithm for Hole-Filling in Depth Image-Based Rendering显示文摘In free viewpoint video(FVV)and 3DTV,the depth image-based rendering method has been put forward for rendering virtual view video based on multi-view video plus depth(MVD) format.However,the projection with slightly different perspective turns the covered background regions into hole regions in the rendered video.This paper presents a depth enhanced image summarization generation model for the hole-filling via exploiting the texture fidelity and the geometry consistency between the hole and the remaining nearby regions.The texture fidelity and the geometry consistency are enhanced by drawing texture details and pixel-wise depth information into the energy cost of similarity measure correspondingly.The proposed approach offers significant improvement in terms of 0.2dB PSNR gain,0.06 SSIM gain and subjective quality enhancement for the hole-filling images in virtual viewpoint video. | SONG Lin HU Ruimin ZHONG Rui | 2014 | China Communications2014，11，11： | 1 |
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