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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

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Research on Monocular Depth Estimation Method based on Multi-Level Attention and Feature Fusion 7

Research on Monocular Depth Estimation Method based on Multi...

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7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Wu, Zhongyu Huang, Hua Li, Qishen Chen, Penghui Nanchang Hangkong University School of Information Engineering Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Nanchang Hangkong University School of Software Jiangxi Nanchang China

ISBN: (纸本)9798350339161

Monocular depth estimation is a fundamental task in computer vision and has drawn increasing attention. Recently, attention-based models and encoder-decoder architectures have led to great improvements in monocular depth estimation. Typically, most of the previous methods used repeated simple up-sampling operations during decoding, which may not make full use of the potential properties of the features extracted by the encoder, and there are problems of inaccurate prediction of the edge and depth maximum region. We propose an attention-based feature fusion module for encoder and decoder. We treat the monocular depth estimation as a pixel-level optimization problem, where the coarsest encoder feature is used to initialize the pixel-level optimization, which is then refined to higher resolution by the proposed attentional feature fusion (AFF). We formulate the prediction problem as ordinal regression over the bin centers that discretize the continuous depth range. It predicts a correspondingly different distribution of bins based on different pictures and we predict bins at the coarsest level using global pooling and MLP layers. In the NYUV2 dataset, the proposed architecture improving original model by 2.5.% and 1.1%, in terms of Log10 and Absolute relative error, respectively. © 2024 IEEE.

关键词： Decoding

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Research on attention-based multiscale information fusion with the real-time instance segmentation method 2

Research on attention-based multiscale information fusion wi...

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： He, Yifan Huang, Hua Li, Qishen Zhang, Guiwen School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Instance segmentation is a comprehensive computer vision task that involves a wide range of other tasks. Recently, the study of real-time instance segmentation methods has received more attention for the development of autonomous driving. Although existing real-time instance segmentation methods are fast, their accuracy does not meet practical needs. Most methods go for segmentation based on object detection, and their effectiveness is overly dependent on the effectiveness of detection. This paper proposes a new attention-based multiscale information fusion method based on Cheng, T. et al. [1]. Firstly, the PPM module of the baseline network is replaced with the module Multiscale Context Attention (MSCA) designed in this paper based on the baseline network, which uses atrous convolution with different ratios to obtain information of four scales, and then uses non-local attention to enhance the information of features. It can effectively suppress the interference of redundant information on the instance segmentation results. Secondly, a new feature fusion approach is designed, which no longer uses bilinear interpolation, but sub-pixel up sampling combined with attention. We did experiments related to this module on the coco dataset and demonstrated its effectiveness, with a 0.5% improvement over the baseline network. © 2022 IEEE.

关键词： Computer vision

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The simulation models for rayleigh fading channels

The simulation models for rayleigh fading channels

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2007 2nd International Conference on communications and Networking in China, ChinaCom 2007

作者： Zhinian, Luo Wenjun, Zhang Institute of Image Communication and Information Processing Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer and Communication Hunan University ChangSha 410082 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424410095

Rayleigh fading channel is the foundation of mobile radio channel modeling. This paper summarizes four classes of the simulation models for Rayleigh fading channels based on sum-of-sinusoids by a uniform expression, according to the different definition of the assumed parameters in the expression. Among these parameters, the initial phases should be set as random variables and we conclude three cases of phases' definition. With the help of these discussions, it can be seen that the ergodicity and the stationary can not exist simultaneously when the number of multipath wave is finite. Based on this conclusion, some conditions on an effective channel model are proposed, and these conclusions are useful to model the new effective mobile radio channels.

关键词： Fading channels

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Research on Multi-level Attention-based Human Pose Estimation 2

Research on Multi-level Attention-based Human Pose Estimatio...

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Gao, Jun Huang, Hua Li, Qishen Xia, Shaoqi School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network. © 2022 IEEE.

关键词： attention mechanism Human Pose Estimation keypoint detection

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Imaging Detection and Classification for Near-space Dim Targets 7

Imaging Detection and Classification for Near-space Dim Targ...

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7th International Conference on Vision, image and Signal processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

The motion mode of near-space targets is complex due to their high threat level. The target imaging faces low SNR and susceptibility to background noise. The existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. The algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. The improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. The outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © The Institution of Engineering & Technology 2023.

关键词： Extraction

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Neonatal Pain Expression Recognition Based on LBP Features and Sparse Representation

Neonatal Pain Expression Recognition Based on LBP Features a...

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2015 World Conference on Control, Electronics and Electrical Engineering(WCEE 2015)

作者： Guanming Lu Mengying Chen Xiaonan Li Jiangsu Province Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing Children's Hospital Affiliated to Nanjing Medical University

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%.

关键词： expression recognition,local binary pattern,sparse representation,neonate,pain expression.

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A joint strength based genetic algorithm for network clustering

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Journal of Computational Information Systems 2014年第14期10卷 5915-5922页

作者： Zhang, Xingyi Ding, Zhuanlian Tang, Jin Luo, Bin Key Laboratory of Industrial Image Processing and Analysis of Anhui Province School of Computer Science and Technology Anhui University Hefei China

The quality of network clustering is partially determined by its evaluation criterion. In this paper, a joint strength based genetic algorithm (JSGA) for network clustering is proposed, where the joint strength which can effectively distinguish density and network topology is introduced as the evaluation criterion. An efficient joint strength based local search strategy is presented in JSGA, which can improve the local search ability of existing genetic algorithms for network clustering. A joint strength based population initialization method is also proposed, by which an initial population with a good clustering precision and diversity can be generated. Experimental results on both computer-generated networks and realworld network data sets illustrate that JSGA outperforms traditional methods in terms of detecting communities. © 2014 by Binary Information Press

关键词： Genetic algorithms

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Bayesian Network based Abnormality Detection with Genetic Algorithm optimization

Bayesian Network based Abnormality Detection with Genetic Al...

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International Conference on Computational Problem-Solving (ICCP)

作者： Jingbang Qiu Chongyang Zhang Shibao Zheng Institute of Image Communication and Information Processing Shanghai Jiaotong University Shanghai China Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiaotong University Shanghai China

Abnormality Detection (AD), being the core part of intelligent surveillance systems, is calling for growing research interest due to its importance in providing higher efficiency and labor saving. In this paper, we propose a novel Bayesian Network (BN) based AD method for smart surveillance in scenes containing large scale viewpoint changes without model-relearning. In the proposed AD scheme, Reasoning Layer is introduced into BN to strengthen logical inferences, and a localized Genetic Algorithm (GA) is developed to optimize BN parameters and structure. With the expert knowledge aided BN structure modeling and GA based optimization, the proposed method can provide more robust detection experience with retained accuracy. Experiments on unlearned surveillance test sequences are shown to exhibit the validity of this method.

关键词： Gallium Optimization Hidden Markov models Surveillance Genetic algorithms Humans Robustness

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ArtDiff: Artwork Generation via Conditional Diffusion Models 23

ArtDiff: Artwork Generation via Conditional Diffusion Models

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15th International Conference on Digital image processing, ICDIP 2023

作者： Liu, Wenhui Liu, Bin Lin, Xiao Li, Bo He, Zhifen Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang China Department of Supercomputing Management Jiangxi Science and Technology Infrastructure Center Jiangxi Nanchang China

ISBN: (纸本)9798400708237

Artwork Generation is an important research area of computer vision. Recently, kinds of generative models have achieved great success in natural image generation. However, artwork generation has rarely been studied due to the unfixed structure of artworks. Combined with prevailing diffusion models, we propose a simple yet effective framework, named as ArtDiff, for artwork generation. Given a name of an artist, we can generate diverse and novel artworks which reflecting the style of the artist. To the best of our knowledge, ArtDiff is the first diffusion model that generate artworks with the guidance of artist's name. Experimental results demonstrate that ArtDiff is able to recognize the artist's preference and generate artworks with reasonable structures and fine-grained details. © 2023 ACM.

关键词： Diffusion

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