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19th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Zeng Keqin China Acad Engn Phys Inst Comp Applicat Mianyang 621900 Sichuan Peoples R China

ISBN: (纸本)9781665493895

With the continuous development of intelligent computing power, neural networks are widely used in all walks of life. Traditional neural networks, such as convolutional neural networks and fully connected networks, as the carrier of method models, have played an important role in solving many structural data problems. However, for unstructured graph data, it has its own diversity and complexity. For large-scale graphs, it is still challenging to quickly calculate graph neural networks for processing graphs due to the limitations of software and hardware tools and resources. For this reason, this paper proposes a method of multi-GPU block parallel computing based on graph segmentation, so as to improve the computing efficiency of message passing graph neural network in dealing with large-scale graph problems.

关键词： graph neural network Parallel computing graph segmentation Large-scale graph

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Small protein complex prediction algorithm based on protein-protein interaction network segmentation

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BMC BIOINFORMATICS 2022年第1期23卷 405-405页

作者： Lyu, Jiaqing Yao, Zhen Liang, Bing Liu, Yiwei Zhang, Yijia Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China Dalian Univ Technol Sch Chem Engn Dalian Peoples R China Dalian Univ Technol Sch Innovat & Entrepreneurship Dalian Peoples R China Dalian Maritime Univ Sch Informat Sci & Technol Dalian Peoples R China

Background Identifying protein complexes from protein-protein interaction network is one of significant tasks in the postgenome era. Protein complexes, none of which exceeds 10 in size play an irreplaceable role in life activities and are also a hotspot of scientific research, such as PSD-95, CD44, PKM2 and BRD4. And in MIPS, CYC2008, SGD, Aloy and TAP06 datasets, the proportion of small protein complexes is over 75%. But up to now, protein complex identification methods do not perform well in the field of small protein complexes. Results In this paper, we propose a novel method, called BOPS. It is a three-step procedure. Firstly, it calculates the balanced weights to replace the original weights. Secondly, it divides the graphs larger than MAXP until the original PPIN is divided into small PPINs. Thirdly, it enumerates the connected subset of each small PPINs, identifies potential protein complexes based on cohesion and removes those that are similar. Conclusions In four yeast PPINs, experimental results have shown that BOPS has an improvement of about 5% compared with the SOTA model. In addition, we constructed a weighted Homo sapiens PPIN based on STRINGdb and BioGRID, and BOPS gets the best result in it. These results give new insights into the identification of small protein complexes, and the weighted Homo sapiens PPIN provides more data for related research.

关键词： Protein complex identification Small protein complex Protein-protein interaction graph segmentation

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Video segmentation scheme based on AMC

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IET IMAGE PROCESSING 2020年第3期14卷 407-416页

作者： Cao, Ying Sun, Lijuan Han, Chong Guo, Jian Nanjing Univ Posts & Telecommun Internet Things Acad Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensors Nanjing 210003 Peoples R China

Video segmentation has become a fundamental of various multimedia applications. Spatiotemporal coherence is important for video segmentation. In this study, to balance the spatiotemporal coherence in scenes with deformation or large motion, the authors propose a novel segmentation scheme based on the absorbing Markov chain (AMC) model named directed graph segmentation based on AMC. In their study, they first generate primary proposals per frame. Then, they train weight models by using a part of primary proposals with their features and feature scores. Next, they construct a directed AMC graph, in which states are the generated primary proposals and edge weights are decided by trained weight models. They subsequently perform the first proposal selection per frame by thresholding the modified absorbed time. Afterwards, they design a reselection algorithm to filter the selected proposals and ensure the proposals, which are the most likely to be the motion object in each frame, to be selected as candidates. Finally, they employ the graph-cuts based optimisation algorithm to generate refined per pixel segmentation by using object and background models built by candidate proposals under the concept of Gaussian mixture models. Experimental results demonstrate that the proposed scheme shows competitive performance compared with advanced algorithms.

关键词： Markov processes directed graphs video signal processing image segmentation Gaussian processes optimisation mixture models video segmentation scheme multimedia applications spatiotemporal coherence deformation graph segmentation feature scores directed AMC graph edge weights trained weight models proposal selection modified absorbed time motion object graph-cuts based optimisation algorithm pixel segmentation background models candidate proposals Gaussian mixture models Markov chain model

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Application of agricultural insect pest detection and control map based on image processing analysis

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期38卷 379-389页

作者： Deng, Leilei Wang, Zhenghao Wang, Chuang He, Yifan Huang, Tao Dong, Yue Zhang, Xian Jilin Agr Univ Coll Informat Technol Changchun Jilin Peoples R China Changchun Univ Sch Off Changchun 130022 Jilin Peoples R China

Image segmentation technology is a basic technology for image processing and analysis. As a typical interactive color image segmentation algorithm, grabbing segmentation has high precision, interactive operation and better segmentation effect in processing complex background segmentation, and has broad prospects in the field of agriculture. In this paper, the image segmentation algorithm of maize smut, Maize Head Smut and maize rust, which are three main diseases and insect pests, is studied by taking the high-yield crop Maize in Northeast China as an example. The image background in the static image editing is replaced by an improved one-time cutting algorithm. Through the adaptive combination of weights, the depth information and saliency information are combined into the grabbing color model. The improved image segmentation algorithm greatly improves the efficiency and accuracy of image segmentation, and achieves a good spot segmentation effect in the static image of corn pests and diseases, and has a high recognition. Do not rate. And it plays a predictive research effect in practical verification.

关键词： Threshold segmentation graph segmentation grab cut algorithm saliency maize diseases and pests

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Parallel Structure from Motion for Sparse Point Cloud Generation in Large-Scale Scenes

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SENSORS 2021年第11期21卷 3939页

作者： Bao, Yongtang Lin, Pengfei Li, Yao Qi, Yue Wang, Zhihui Du, Wenxiang Fan, Qing Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Beihang Univ State Key Lab Virtual Real & Technol Beijing 100191 Peoples R China Beihang Univ Virtual Real Res Inst Qingdao Res Inst Qingdao 266100 Peoples R China MiningLamp Technol Beijing 100102 Peoples R China

Scene reconstruction uses images or videos as input to reconstruct a 3D model of a real scene and has important applications in smart cities, surveying and mapping, military, and other fields. Structure from motion (SFM) is a key step in scene reconstruction, which recovers sparse point clouds from image sequences. However, large-scale scenes cannot be reconstructed using a single compute node. Image matching and geometric filtering take up a lot of time in the traditional SFM problem. In this paper, we propose a novel divide-and-conquer framework to solve the distributed SFM problem. First, we use the global navigation satellite system (GNSS) information from images to calculate the GNSS neighborhood. The number of images matched is greatly reduced by matching each image to only valid GNSS neighbors. This way, a robust matching relationship can be obtained. Second, the calculated matching relationship is used as the initial camera graph, which is divided into multiple subgraphs by the clustering algorithm. The local SFM is executed on several computing nodes to register the local cameras. Finally, all of the local camera poses are integrated and optimized to complete the global camera registration. Experiments show that our system can accurately and efficiently solve the structure from motion problem in large-scale scenes.

关键词： structure from motion graph segmentation sparse point cloud large-scale scene camera clustering UAV image

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Anomaly analysis and visualization for dynamic networks through spatiotemporal graph segmentations

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2018年 124卷 63-79页

作者： Liao, Qi Li, Ting Blakely, Benjamin A. Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA Argonne Natl Lab Strateg Secur Sci Div Argonne IL 60439 USA

With recent technology advance in Internet of Things (IoT) that involves human, sensors, and mobile devices, networks are not only growing much larger but more complex and dynamic in nature. The spatiotemporal dynamics of networks are represented by both topological changes and temporal shifts of attribute information associated with network components. Understanding the pattern and trend of dynamic networks is increasingly important. While data mining approaches are generally useful in analyzing the statistical properties of networks, there has been recent trend to consider bringing human into the loop, and to examine how feedback from visualizations of large-scale dynamic networks can further improve data mining and machine learning. Traditional visualization methods based on animation and sequences of snapshot graphs are also limited by human cognitive capability. We present a dynamic network analysis and visualization (DNAV) tool which explores the spatiotemporal dimensions of graph components. In particular, nodes and edges are augmented with spatiotemporal segmentation based on both topological and attribute dynamics (e.g., time and locations of connectivity). To further facilitate analysis of large dynamic networks, DNAV includes statistical dynamic overviews alongside graph views, as well as data filtering modules for scalable analysis. Using case studies on public datasets, we demonstrate the effectiveness of DNAV in understanding and analyzing anomalies in dynamic networks such as computer communication networks.

关键词： Dynamic networks Anomaly detection Visualization graph segmentation Spatiotemporal analysis Analytic tool and applications

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Automated Phase segmentation for Large-Scale X-ray Diffraction Data Using a graph-Based Phase segmentation (GPhase) Algorithm

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ACS COMBINATORIAL SCIENCE 2017年第3期19卷 137-144页

作者： Xiong, Zheng He, Yinyan Hattrick-Simpers, Jason R. Hu, Jianjun Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Univ South Carolina Dept Chem Engn Columbia SC 29208 USA Guizhou Univ Sch Mech Engn Guiyang 550025 Guizhou Peoples R China

The creation of composition-processing-structure relationships currently represents a key bottleneck for data analysis for high-throughput experimental (HTE) material studies. Here we propose an automated phase diagram attribution algorithm for HTE data analysis that uses a graph based segmentation algorithm and Delaunay tessellation to create a crystal phase diagram from high throughput libraries of X-ray diffraction (XRD) patterns. We also propose the sample-pair based objective evaluation measures for the phase diagram prediction problem. Our approach was validated using 278 diffraction patterns from a Fe-Ga-Pd composition spread sample with a prediction precision of 0.934 and a Matthews Correlation Coefficient score of 0.823. The algorithm was then applied to the open Ni-Mn-Al thin-film composition spread sample to obtain the first predicted phase diagram mapping for that sample.

关键词： graph segmentation phase segmentation high-throughput experiments phase diagram X-ray dfffiraction

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Parallel Large-Scale Structure from Motion by Distributed Averaging 20

Parallel Large-Scale Structure from Motion by Distributed Av...

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Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

作者： Pengfei Lin Yongtang Bao Wenxiang Du Yue Qi Beihang University Beihang University Qingdao Research Institute Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Beihang University Qingdao Research Institute Qingdao China State Key Laboratory of Virtual Reality and Technology Beihang University Beihang University Qingdao Research Institute Beijing China

ISBN: (纸本)9781450387811

With the development of computer vision, Structure from Motion (SFM) which recovers sparse point clouds from image sequences has achieved great success. Large-scale scenes cannot be reconstructed with a single compute node so that we introduce a divide-and-conquer framework to solve the distributed SFM problem. First, we attach great importance to the efficiency of image matching and geometric filtering, which takes up a lot of time in the traditional SFM problem. We use the GPS information of images to calculate the GPS neighborhood. The number of image matches is greatly reduced by matching each image with only valid GPS neighbors, and a robust matching relationship is obtained. Second, the calculated matching relationship is used as the initial camera graph to be divided into multiple subgraphs by the clustering algorithm, and local SFM is executed on several computing nodes to register the local cameras. Finally, all local camera poses are integrated and optimized to complete the global camera registration. Our system can solve the structure from motion problem in large-scale scenes with accuracy and efficiency.

关键词： Computer Vision Structure from Motion graph segmentation

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Heart Coronary Artery segmentation and Disease Risk Warning Based on a Deep Learning Algorithm

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IEEE ACCESS 2020年 8卷 140108-140121页

作者： Xiao, Can Li, Yi Jiang, Yimin Henan Univ Dept Ultrasound Huaihe Hosp Kaifeng 475003 Peoples R China Sun Yat Sen Univ Affiliated Hosp 5 Dept Thyroid & Breast Surg Zhuhai 519000 Peoples R China Sun Yat Sen Univ Affiliated Hosp 5 Guangdong Prov Key Lab Biomed Imaging Zhuhai 519000 Peoples R China Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6 Dept Ultrasound Shanghai 201306 Peoples R China

This paper is based on an improved three-dimensional U-net convolutional neural network deep learning algorithm for heart coronary artery segmentation for disease risk prediction, and it is practical with multiple data sets under two backgrounds without centerline and with the centerline. By using a new local feature to extract the ventricular information, and using the deep belief network to extract the features to regress the contour coordinates of the biventricular. Combining features and deep belief networks and training regression networks can not only extract high-level information but also accurately divide the left and right ventricles at a small computational cost. The performance of segmentation based on the dice coefficient compared between the two datasets. The results show that the model training effect of the centerline preprocessing is superior to the original data. The experimental results show that the best effect reaches the dice coefficient of 0.8291. In the experiment, it found that simple data expansion may be detrimental to the test data. From the training curve, it is believed that with the improvement of the quality of training data, the performance of coronary artery segmentation can be further improved, and it is of great significance to provide doctors and patients with more accurate and efficient opinions and suggestions in clinical practice to improve the quality of diagnosis and treatment. The purpose of assisting experts in real-time diagnosis and analysis achieved.

关键词： Image segmentation Diseases Arteries Biomedical imaging Feature extraction Machine learning Heart Deep learning heart coronary artery graph segmentation disease risk warning

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Fast Recognition and Location of Target Fruit Based on Depth Information

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IEEE ACCESS 2019年 7卷 170553-170563页

作者： Tian, Yuyu Duan, Huichuan Luo, Rong Zhang, Yan Jia, Weikuan Lian, Jian Zheng, Yuanjie Ruan, Chengzhi Li, Chengjiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Qilu Univ Technol Sch Light Ind Sci & Engn Jinan 250351 Peoples R China Shandong Management Univ Coll Ind & Commerce Jinan 250357 Peoples R China Shandong Univ Sci & Technol Dept Elect Engn & Informat Technol Jinan 250031 Peoples R China Wuyi Univ Coll Mech & Elect Engn Wuyishan 354300 Peoples R China

In order to realize the recognition and localization of the target fruit more accurately and efficiently, an optimized graph-based recognition algorithm based on image depth information is proposed. Firstly, the gradient information of the depth image is obtained from the acquired depth apple image, the gradient vector is reflected from the 3D space to the 2D space, and then rotated 90 degrees clockwise, and the regular region forms the center of the vortex, which is the target fruit center to determine target fruit location. In the RGB space, the graph-based segmentation algorithm is introduced. By constructing the adaptive threshold, the mapping relationship between the number of centers and the parameters of k and min is established to find the target fruit region. Finally, scan the maximum radius of the super pixel area where the center of the circle is located, and fit the target fruit to realize the recognition and localization of the apple. The experimental results show that the target recognition rate is 96.61, and the recognition time has been reduced significantly.

关键词： Target recognition Image segmentation Image recognition Feature extraction Robot sensing systems Object segmentation Target fruit depth image graph segmentation predicate optimization

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