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DP-Pose: Multi-Person Pose Estimation in Video Sequence through Dynamic Programming

DP-Pose: Multi-Person Pose Estimation in Video Sequence thro...

引用

ieee International symposium on Parallel and Distributed processing with Applications and ieee International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Deyuan Zhang Junyuan Wang Xiangbin Shi Zhaokui Li Fang Liu Cuiwei Liu School of Computer Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Human pose estimation is an important research topic in the field of computer vision. Bottom-up methods such as OpenPose have become prevalent pose estimation methods because of its high efficiency and detection speed. But for poor quality video, the keypoints obtained by the bottom-up method have suffered from jitter and loss. In this paper, we propose DP-Pose, which constructs candidate pose sequence by selecting points in a region of the heatmap, improves constraint function by combining distance and confidence, and solves the optimal keypoints location by dynamic programming. The experimental results on class dataset show that DP-Pose can recover missing keypoint using heatmap data and obtain stable pose from candidate pose sequences.

关键词： computer vision dynamic programming image sequences pose estimation video signal processing pose estimation dynamic programming Video signal processing images computer vision image sequences Video sequences candidate species

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Graph Convolutional Networks with Objects for Skeleton-Based Action Recognition

Graph Convolutional Networks with Objects for Skeleton-Based...

引用

ieee International symposium on Parallel and Distributed processing with Applications and ieee International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xiangbin Shi Haowen Li Fang Liu Deyuan Zhang Jing Bi Zhaokui Li School of Computer Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Recently, graph convolutional neural networks has become a research hotspot for skeleton-based action recognition because of its excellent performance on graph structure data. Compared to traditional methods, it can explicitly exploit the natural connectivity among the joints and improve greater expressive power. In this paper, we propose a two-stream graph convolutional networks with objects for skeleton-based action recognition. An algorithm is designed for matching similar skeleton in adjacent frames, so that we can get the right skeletons which belong to the same person. It performs well when there are other irrelevant persons in the scene. In addition, other features are less employed except for the human joint in skeleton-based action recognition. We introduce limbs orientation information and related objects information. The related objects are treated as joint points which link with hands. The two-stream networks are built to model coordinate features and orientation features respectively, the results of two streams are fused to one. We get good results on the Kinetics dataset with our methods.

关键词： convolutional neural nets feature extraction graph theory image motion analysis image recognition image representation learning (artificial intelligence) object recognition video signal processing image motion analysis Video signal processing image representation image recognition Graph theory object recognition Feature extraction Line graph Recognition (Psychology) Joints

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An Improved Epsilon Constraint Handling Method Embedded in MOEA/D for Constrained Multi-objective Optimization Problems

An Improved Epsilon Constraint Handling Method Embedded in M...

引用

ieee symposium Series on computational intelligence

作者： Zhun Fan Wenji Li Xinye Cai Hui Li Han Huang Zhaoquan Cai Caimin Wei Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Department of Electronic Engineering Shantou University 515063 China School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi 710049 China Department of Mathematics Shantou University Guangdong 515063 China Department of Electronic Engineering Shantou University Guangdong 515063 China School of Software Engineering South China University of Technology Guangdong Guangzhou 510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu 210016 China Department of Computer Science Huizhou University Guangdong 516007 China

ISBN: (纸本)9781509042418

This paper proposes an improved epsilon constraint handling method embedded in the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). More specifically, it dynamically adjusts the epsilon level, which is a critical parameter in the epsilon constraint method, according to the feasible ratio of solutions in the current population. In order to verify the effect of the improved epsilon constraint handling method, three algorithms - MOEA/D-CDP, MOEA/D-Epsilon, and MOEA/D-IEpsilon (MOEA/D with the improved epsilon constraint handling mechanism) are tested on nine CMOPs (CMOP1-CMOP9). The comprehensive experimental results indicate that the proposed epsilon constraint handling method is very effective on the performance of both convergence and diversity.

关键词： improved epsilon constraint

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Multidisciplinary approaches for signal processing education

Multidisciplinary approaches for signal processing education

引用

International symposium on image and signal processing and Analysis

作者： J.B. Destro J.P. Breda-Destro J.M. Travassos-Romano DECOM Campinas Univ. Brazil OasiSTEC Foundation São José do Rio Pardo SP BRAZIL DECOM University of Campinas Campinas SP BRAZIL

ISBN: (纸本)9539676940

This work reports the implementation of two multidisciplinary postgraduate courses on intelligent signal processing, which have been taught at FEEC (School of Electrical and Computer Engineering), University of Campinas (UNICAMP), Brazil. The first one is devoted to high-order statistics (March-June/2000) and the second one, to blind deconvolution (August-December/2000). The adjective "multidisciplinary" concerns the participation of students of different backgrounds and research fields. The main difficulties associated with establishing an effective communication among the students and the lecturer is discussed, and the motivation level of students is evaluated. This experimental evaluation points out good results as well as drawbacks, and the main challenges of multidisciplinary education are discussed.

关键词： signal processing Education Deconvolution Biomedical signal processing Biomedical engineering computational intelligence Statistics Communication effectiveness Adaptive signal processing Geophysics

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Pose-Based Multi-Target Tracking

Pose-Based Multi-Target Tracking

引用

ieee International symposium on Parallel and Distributed processing with Applications and ieee International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xiangbin Shi Xiaoyu Yang Deyuan Zhang Jing Bi Zhaokui Li Fang Liu School of Computer Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Human multi-target tracking in video is an important issue in the field of computer vision. It is necessary to detect the target on each frame, and to connect the targets of all frames into a target sequence. For target matching among different frames, we propose a tracking algorithm for constructing object pose sequence(COPS) based on Openpose. The position status and the ORB feature of the target pose are dynamically weighted and fused into new features. Target pose is searched in the corresponding target pose sequence by comparing the new features between the target pose in the sequence and every pose in current frame. When the target pose is matched, the influence of the position feature on the pose similarity could be enhanced when the target motion is continuously detected. When the target scale changes too much, the method can expand the contribution of the ORB feature to the pose similarity comparison. The experiments of human multitarget tracking algorithm are carried out on the PoseTrack and MOT datasets, and the results show that the proposed tracking algorithm overcomes the problem of target matching between frames.

关键词： computer vision feature extraction image fusion image matching image motion analysis image sequences object tracking pose estimation target tracking video signal processing Target tracking images computer vision Targets image motion analysis pose estimation Video signal processing image matching image sequences image fusion Feature extraction Frame FRAMES tracking objects

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Blind Video Quality Assessment Based on Human Visual Characteristic

Blind Video Quality Assessment Based on Human Visual Charact...

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ieee International symposium on Parallel and Distributed processing with Applications and ieee International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Shiyu Zhou Xiqing Liu Yuanbing Tian Bin Fu Xiuyan Xia Jin Ge China Unicom Network Technology Research Institute Beijing China Zhiyou Open Source Communication Research Institute (Beijing) Co. Ltd Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728152103

Based human visual speed perception characteristic, a video quanlity assessment(VQA) algorithm is introduced in the paper. Natural video statistics features extraction and weighting factors are incorporated in the scheme. In the VQA, considering the impact both video content itself and HVS's characteristic on human subjective perception, we propose weighting factors to scale the effect of those features, and it contains two parts: motion information and perception noise. And natural video statistics features relate to the spatial and temporal domain are extracted. The weighting factors would be used to combine both the temporal and spatial features, then generate the quality of each frame. Finally, the video quality score can be obtained by pooling scheme. LIVE database, EPFL-PoliMI database and some other generated test videos were used in our experiments, and the results indicate our model has outstanding performance.

关键词： feature extraction image denoising image motion analysis statistical analysis video signal processing visual databases image motion analysis visual databases Video signal processing image denoising Feature extraction Videotapes Quality assessment Statistical methods Statistical analysis WEIGHTING FACTORS

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Object Tracking Based on Deep CNN Features Together with Color Features and Sparse Representation

Object Tracking Based on Deep CNN Features Together with Col...

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ieee symposium Series on computational intelligence (SSCI)

作者： Yuchi Liu Yujuan Qi Yanjiang Wang College of Control Science and Engineering China University of Petroleum(East china) Qingdao China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

To acquire the better object tracking performance in this paper, the object of interest is modeled by its RGB-color histogram feature together with deep convolutional neural network feature (deep CNN feature) and sparse representation. Because the information of the objects of interest cannot be obtained ahead in practical use, the deep CNN features should be abstracted through the pre-trained VGG network. Then the deep feature and RGB-color histogram are combined to model the object in adaptive mode. And then sparse representation is used to express the model. Above all, an adaptive particle filter algorithm based on deep CNN feature together with RGB-color feature and sparse representation is proposed to track the object of interest. Extensive experiment results demonstrate the effectiveness of our proposed method under the serious object occlusions and appearance changes.

关键词： Particle filters Feature extraction Object tracking Target tracking image color analysis signal processing algorithms Robustness

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CNN-based Super-resolution Reconstruction for Traffic Sign Detection

CNN-based Super-resolution Reconstruction for Traffic Sign D...

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ieee symposium Series on computational intelligence (SSCI)

作者： Fan Wang Jianqi Shi Xuan Tang Jielong Guo Peidong Liang Yuanzhi Feng College of Electrical Engineering and Automation Fuzhou university Fujian China Shanghai Key Lab for Trustworthy Computing School of Software Engineering East China Normal University Shanghai Putuo District Quanzhou Institute of Equipment Manufacture Haixi Institutes Chinese Academy of Sciences Quanzhou China Quanzhou HIT Research Institute of Engineering and Technology Quanzhou Fengze District China Henan University North Section of Jinming Avenue Kaifeng Longting District China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Automatic identification for traffic signs is an important part of intelligent driving and traffic safety. Deep learning has already made a great achievement in traffic sign detection. However, the camera on a car may capture a low resolution and blurry image in certain environments, which make it inaccurate for traffic sign detection. Therefore, we propose a method based on image super-resolution reconstruction for improving the detection rate of traffic signs. Firstly, a low-resolution image is transformed by CNN-based super-resolution network into a high-resolution one. Then, to meet the requirements of on-line processing, we use the generated super-resolution image as input for the detection network with 16 filters in this layer. At last, we separately trained two CNNs for super-resolution reconstruction and traffic sign detection, which reduce the processing time. Experimental results demonstrate that our model can achieve better performance than the existing methods for traffic sign detection.

关键词： image resolution Convolution Feature extraction signal resolution image reconstruction Object detection Machine learning

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Designing a Fuzzy Rule Based System to Estimate Depth of Anesthesia

Designing a Fuzzy Rule Based System to Estimate Depth of Ane...

引用

ieee symposium on computational intelligence and Data Mining (CIDM)

作者： V. Esmaeili A. Assareh M. B. Shamsollahi M. H. Moradi N. M. Arefian Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Iran Amirkabir University of Technology슠 Iran Department of Anesthesia Shahid Beheshti University Iran

Estimating the depth of anesthesia (DOA) is still a challenging area in anesthesia research. The objective of this study was to design a fuzzy rule based system which integrates electroencephalogram (EEG) features to quantitatively estimate the DOA. The proposed method is based on the analysis of single-channel EEG using frequency and time domain features as well as Shannon entropy measure. The fuzzy classifier is trained with features obtained from four subsets of data comprising well-defined anesthesia states: awake, moderate, general anesthesia, and isoelectric. The classifier extracts efficient fuzzy if-then rules and the DOA index is derived between 100 (full awake) to 0 (isoelectric) using fuzzy inference engine. To validate the proposed method, a clinical study has conducted on 22 patients to construct 4 subsets of reference states and also to compare the results with CSM monitor (Danmeter, Denmark), which has revealed satisfactory correlation with clinical assessments

关键词： Fuzzy systems Knowledge based systems Anesthesia Direction of arrival estimation Electroencephalography Frequency domain analysis Time domain analysis Entropy Frequency measurement Time measurement

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signal Detection in Three-Dimensional Confocal Microscopy images Through Deep Learning

Signal Detection in Three-Dimensional Confocal Microscopy Im...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Róbert Paulik Miklós Kozlovszky Béla Molnár 3DHISTECH Ltd. Obuda University Budapest Hungary BioTech Research Center Obuda University Budapest Hungary Image Analsysis Department 3DHISTECH Ltd. Budapest Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

As routine pathology moves into the digital age; the spread of high-efficiency and high-resolution tissue scanners opens up the possibility of routine analysis of three-dimensional samples containing fluorescent genetic signals. One of these cornerstones is confocal microscopy, with the help of which cell nuclei and their signals can be imaged in three dimensions. This article presents a novel deep learning-based algorithm for detecting signals within three-dimensional confocal microscopy images. By leveraging the power of convolutional neural networks, our approach significantly improves the accuracy and efficiency of signal detection compared to traditional image processing methods, especially in the case of thick sections. We demonstrate the algorithmic effectiveness through validation on various samples, highlighting its potential to advance research in biology and medicine.

关键词： Deep learning Pathology Accuracy Microscopy Fluorescence Fish Information age

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