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A Target Location Method Based on Swarm Probability Fusion 4

A Target Location Method Based on Swarm Probability Fusion

引用

4th IEEE International conference on Electronics Technology, ICET 2021

作者： Bi, Qian Wu, Shuang Huang, Yong Zhu, Yalong Hu, Zhuofei Southwest Electronic Equipment Research Institute Chinese Electronics Technology Group Corporation Chengdu China

ISBN: (纸本)9781728176734

The existing target location methods either depend on the sensors' high performance or the communication bandwidth, which are not suitable to directly apply to the effective fusion and target recognition of observation information of the large-scale heterogeneous swarm. In this paper, a target location method based on swarm probability fusion is proposed, which can represent the sensor's data in the heterogeneous swarms as the probability distribution map, and then carry out image recognition processing after normalized distributed fusion to obtain target information. The effectiveness of the method is verified by the effectiveness tests, the singlenode's direction finding accuracy tests, and the swarm nodes' number tests. Preliminary analysis shows that single-node's direction finding accuracy has a significant effect on the swarm positioning accuracy and the convergence speed, while the number of nodes has a small effect on positioning accuracy and mainly affects the convergence speed. © 2021 IEEE.

关键词： image recognition

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Efficient Volume Estimation for Dynamic Environments using Deep Learning on the Edge

Efficient Volume Estimation for Dynamic Environments using D...

引用

IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Chandan Kumar Yamini Mathur Ali Jannesari Department of Computer Science Iowa State University Ames IA USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The utility of edge devices has increased in volume estimation of uneven terrains. Existing techniques utilize several geo-tagged images of the landscape, captured in-flight by an edge device mounted over a UAV, to generate 3D models and perform volume estimation through manual boundary marking. These methods, although accurate, require significant time, human effort and are heavily dependent on GPS. We present an efficient deep learning framework that detects the object of interest and automatically determines the volume (independent of GPS) of the detected object on-the-fly. Our method employs a stereo camera for depth sensing of the object and overlays a unit mesh grid over the object's boundary to perform volume estimation. We explore the accuracy vs computational complexity trade-off on variations of our technique. Experiments indicate that our method reduces the time for volume estimation by several orders of magnitude in contrast to existing methods and is independent of GPS as well. Also, to the best of our knowledge, this is the first method that can perform volume analysis in a dynamic environment.

关键词： Deep learning Solid modeling Three-dimensional displays Error analysis Volume measurement image edge detection Heuristic algorithms

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Towards Fast Edge Detection Approach for Industrial Products

Towards Fast Edge Detection Approach for Industrial Products

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： Dhafer G. Honi Ali Hasan Ali Zaid Ameen Abduljabbar Junchao Ma Vincent Omollo Nyangaresi Keyan Abdul-Aziz Mutlaq Samir M. Umran College of Education for Pure Sciences University of Basrah Basrah Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen China Faculty of Biological and Physical Tom Mboya University Homabay Kenya It and Communication center University of Basrah Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Edge detection is a critical component of many elements of image processing applications and computer visualization, such as image segmentation, detection of imperfections in industrial products, medical image processing, and object identification. The edge detection technique seeks to accelerate image analysis by limiting the quantity of information processed. The object’s dimensions are a major component in manufacturing, engineering and other crucial fields, therefore the less time this takes, the more efficient the whole process becomes. This study details the use of unique image processing technology to advance the automated quantifying of an industrial object’s dimensions. Firstly, an image improvement approach founded on fuzzy entropy was devised to clarify the original deteriorated image. Secondly, the edges of the object were detected via the method of quick fuzzy edge detection. Finally, using Freeman Chain Codes (FCC), the essential corner locations were determined. Thus, the edges and corners of any given object image are accurately detected. This strategy is uncomplicated to implement, can efficiently identify size, shape, and any defects, and helps to determine acceptance or rejection of the final product. The results of the corresponding experiment indicate that the proposed method has a much lower computational cost in general compared to the related works.

关键词： Visualization image analysis Shape image edge detection FCC Ubiquitous computing Entropy

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FAST: A framework for high-performance medical image computing and visualization 21

FAST: A framework for high-performance medical image computi...

引用

2021 International Workshop on OpenCL, IWOCL 2021

作者： Smistad, Erik Sintef Medical Technology and Norwegian University of Science and Technology Trondheim Trøndelag Norway

ISBN: (纸本)9781450390330

Medical image processing and visualization is often computationally demanding. Ultrasound images are acquired in real-Time and needs to be processed at a high framerate with low latency. Computed tomography (CT) and magnetic resonance imaging (MRI) create large three dimensional volumes with sizes up to 512 × 512 × 800 voxels. In digital pathology, whole slide microscopy images can have an extreme image size of up to 200, 000 × 100, 000 pixels, which does not even fit into the memory of most computers. Thus, there is a need for smart data storage, processing and visualization methods to handle medical image data. The development of FAST started in 2014, the goal was to create an open-source framework which made GPU and parallel processing of medical images easy and portable. While there existed popular image processing libraries such as the visualization toolkit (VTK), insight toolkit (ITK) and OpenCV, the GPU processing capabilities were still implemented ad-hoc and often implied copying data back and forth from the GPU and CPU. Thus it was decided to use the new OpenCL API to create a cross-platform framework designed bottom-up with GPU processing at the very core. One of the design goals was to remove the burden of moving data back and forth from different processors and memory spaces from the developer. Instead, the developer requests access to the data on a given processor, and FAST will copy and update data as needed. Now, seven years later FAST version 3.2 is released, it still uses OpenCL 1.2 and OpenGL 3.3 at the core of almost all of its operations. FAST can stream images in real-Time from ultrasound scanners, webcameras, Intel's RealSense depth camera, and read many different formats from disk including medical formats such as DICOM, Metaimage and huge microscopy images stored as tiled image pyramids. FAST uses a processing pipeline concept, meaning that you define a pipeline as multiple processing and visualization steps first, then initiate the processi

关键词： Pipelines

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PAN-SHARPENING BASED ON parallel PYRAMID CONVOLUTIONAL NEURAL NETWORK

PAN-SHARPENING BASED ON PARALLEL PYRAMID CONVOLUTIONAL NEURA...

引用

IEEE International conference on image processing (ICIP)

作者： Fang, Shuai Wang, Xiao Zhang, Jing Cao, Yang Hefei Univ Technol Key Lab Knowledge Engn Big Data Hefei 230000 Peoples R China Univ Sci & Technol China Dept Automat Hefei Anhui Peoples R China

ISBN: (纸本)9781728163956

Existing deep learning-based pan-sharpening methods mainly learn spatial information from a high-resolution (HR) panchromatic (PAN) image for each spectral channel. However, due to the own characteristics of remote sensing image data, the spatial information of PAN image often shows weak correlation with some spectral channel, especially for channels non-overlapped by PAN channel. In this paper, we propose a parallel pyramid network (PPN) for pan-sharpening. First, a three-branch parallel structure is proposed for dealing with PAN image detail, multispectral (MS) images detail and spectral property respectively. Second, pyramid network structure is introduced in two detail branches to solve the problem of weak correlation due to scale difference. Third, the feature level fusion in two detail branches is implemented, which utilizes redundancy between channels to solve detail representation of channels non-overlapped by PAN channel. The qualitative and quantitative experimental results on various data sets demonstrate the superiority of our proposed method over the state-of-the-art methods.

关键词： parallel pyramid network pan-sharpening feature level fusion spatial information learning-based

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Visual Analysis of Emotions Using AI image-processing Software: Possible Male/Female Differences between the Emotion Pairs "Neutral"-"Fear" and "Pleasure"-"Pain" 2021

Visual Analysis of Emotions Using AI Image-Processing Softwa...

引用

14th ACM International conference on Pervasive Technologies Related to Assistive Environments (PETRA)

作者： Prossinger, H. Hladky, T. Binter, J. Boschetti, S. Riha, D. Univ Vienna Dept Evolutionary Biol Fac Life Sci Vienna Austria Charles Univ Prague Fac Humanities Prague Czech Republic Charles Univ Prague Fac Sci Prague Czech Republic

ISBN: (纸本)9781450387927

Inferring the emotional state of an individual by viewing his/her facial expression seems to be present in all human cultures. Numerous studies have shown that various changes in facial muscles determine the resulting facial expression. The analysis of images of faces expressing emotional states promises to contribute to quantification of the claimed observations. Here, we use a suite of AI (artificial intelligence) algorithms, along with ML (maximum likelihood) estimated distributions to quantify the shift in facial expression from "neutral"."fear" and "pain"."pleasure". The images are single frames of five emotional states (neutral, fear, pain, pleasure, laugh) expressed by actors and actresses in BDSM videos. We extract a feature vector for each image, dimension-reduce these feature vectors by mapping them onto a two-dimensional manifold and calculate the norms of the normalized displacement vectors for each emotional pair. We then find that the ML distributions of the norms are Gamma-distributed and that the modes for each pair are different for both males and females. We use Wilks lambda to determine significance. We find that the distributions for the females are significantly different, but not for the males. The methodology we present here has widespread applications: monitoring the emotional states of humans in various settings;among these: determining whether participants in BDSM and similar videos are indeed volunteering their participation or are victims of criminal activity.

关键词： image processing using AI (artificial intelligence) Maximum likelihood methods for Gamma distributions Facial expression of emotions Facial expression of fear of pain and of pleasure

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A parallel Drone image Mosaic Method Based on Apache Spark 9th

A Parallel Drone Image Mosaic Method Based on Apache Spark

引用

9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Wu, Yirui Ge, Lanbo Luo, Yuchi Teng, Deqiang Feng, Jun College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9783030485122

MapReduce has been widely used to process large-scale data in the past decade. Among the quantity of such cloud computing applications, we pay special attention to distributed mosaic methods based on numerous drone images, which suffers from costly processing time. In this paper, a novel computing framework called Apache Spark is introduced to pursue instant responses for the quantity of drone image mosaic requests. To assure high performance of Spark-based algorithms in a complex cloud computing environment, we specially design a distributed and parallel drone image mosaic method. By modifying to be fit for fast and parallel running, all steps of the proposed mosaic method can be executed in an efficient and parallel manner. We implement the proposed method on Apache Spark platform and apply it to a few self-collected datasets. Experiments indicate that our Spark-based parallel algorithm is of great efficiency and is robust to process low-quality drone aerial images. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Drones

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Improving Efficiency in Large-Scale Decentralized distributed Training

Improving Efficiency in Large-Scale Decentralized Distribute...

引用

IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mang, Wei Cui, Xiaodong Kayi, Abdullah Liu, Mingrui Finkler, Ulrich Kingsbury, Brian Saon, George Mroueh, Youssef Buyuktosunoglu, Alper Das, Payel Kung, David Picheny, Michael IBM Res Yorktown Hts NY 10598 USA Univ Iowa Iowa City IA USA

ISBN: (纸本)9781509066315

Decentralized parallel SGD (D-PSGD) and its asynchronous variant Asynchronous parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks. One drawback of (A)D-PSGD is that the spectral gap of the mixing matrix decreases when the number of learners in the system increases, which hampers convergence. In this paper, we investigate techniques to accelerate (A)D-PSGD based training by improving the spectral gap while minimizing the communication cost. We demonstrate the effectiveness of our proposed techniques by running experiments on the 2000-hour Switch-board speech recognition task and the imageNet computer vision task. On an IBM P9 supercomputer, our system is able to train an LSTM acoustic model in 2.28 hours with 7.5% WER on the Hub5-2000 Switchboard (SWB) test set and 13.3% WER on the CallHome (CH) test set using 64 V100 GPUs and in 1.98 hours with 7.7% WER on SWB and 13.3% WER on CH using 128 V100 GPUs, the fastest training time reported to date.

关键词： distributed training decentralized SGD parallel computing automatic speech recognition image recognition

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Research on distributed parallel computing technology of airborne test data

Research on distributed parallel computing technology of air...

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2020 Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2020

作者： Yan, Lijun Shi, Fenglei Zhu, Hongxiang Xi'an710089 China

ISBN: (纸本)9781728160665

Aiming at the task requirements of a small fast-moving test vehicle with small space, short flight support interval and real-time task evaluation, at the same time, the future flight test is faced with many test parameters, wide distribution of test systems, and high real-time data requirements. To solve the rapid acquisition test evaluation data in a specific environment, we design distributed system, data analysis and fusion technology, embedded parallel computing and other technologies to realize the parallel computing and real-time processing on airborne data. The calculated data results are directly telemetry or recorded for real-time monitoring and rapid assessment of missions. The design meets the requirements of rapid real-time evaluation during the flight test and improves the flight test efficiency. © 2020 IEEE.

关键词： Real time systems

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Encoder Fusion Network with Co-Attention Embedding for Referring image Segmentation

Encoder Fusion Network with Co-Attention Embedding for Refer...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Feng, Guang Hu, Zhiwei Zhang, Lihe Lu, Huchuan Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Dalian Univ Technol Ningbo Inst Dalian Peoples R China

ISBN: (纸本)9781665445092

Recently, referring image segmentation has aroused widespread interest. Previous methods perform the multi-modal fusion between language and vision at the decoding side of the network. And, linguistic feature interacts with visual feature of each scale separately, which ignores the continuous guidance of language to multi-scale visual features. In this work, we propose an encoder fusion network (EFN), which transforms the visual encoder into a multi-modal feature learning network, and uses language to refine the multi-modal features progressively. Moreover, a co-attention mechanism is embedded in the EFN to realize the parallel update of multi-modal features, which can promote the consistent of the cross-modal information representation in the semantic space. Finally, we propose a boundary enhancement module (BEM) to make the network pay more attention to the fine structure. The experiment results on four benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance under different evaluation metrics without any post-processing.

关键词： Measurement Visualization image segmentation Grounding Semantics Transforms Information representation

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