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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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2021 IEEE 6th international conference on Intelligent Computing and signal processing, ICSP 2021

2021 IEEE 6th International Conference on Intelligent Comput...

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6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

ISBN: (纸本)9780738143705

The proceedings contain 310 papers. The topics discussed include: single color image dehazing based on variational model;application research of submersible diaphragm pump based on nonmetal coiled tubing;a study on safety issues of human-machine transitions in autonomous vehicles;intelligent diagnosis method for intermittent fault of digital circuit based on dynamic power current;selection of communication frequency points for shortwave radio network based on VOACAP;research and practice of industrial sorting technology based on machine vision;a research on age of information minimization scheme of wireless sensor network assisted by UAV;improved affine projection algorithms with momentum term;NORA based group paging overload control method for massive MTC accesses under 5G networks;a data model and algebraic operation for multidimensional analysis of oil and gas exploration data;and research on image steganography information detection based on support vector machine.

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Infrared and ISAR image Fusion Based on Quadtree Decomposition and Bézier Interpolation 6

Infrared and ISAR Image Fusion Based on Quadtree Decompositi...

引用

6th international conference on signal and image processing, ICSIP 2021

作者： Han, Chunlei Yang, Di Lu, Yao Hou, Kaifa An, Wenbo Wang, Hongmei Xi'An Research Institute of Navigation Technology Xi'an China Northwestern Polytechnical University School of Astronautics Xi'an China

ISBN: (纸本)9780738133737

Inverse Synthetic Aperture Radar (ISAR) image obtained by stationary radar is the result of longitudinal and lateral two-dimensional high-resolution imaging of moving target. Infrared image is a thermal image corresponding to the temperature distribution of the scene, which formed by collecting infrared radiation emitted from the target and the surrounding. In order to make full use of the complementary information of infrared image and ISAR image, an algorithm that combines infrared image with ISAR image is researched. Firstly, this paper applies quadtree decomposition and Bézier interpolation to reconstruct the background of the ISAR image. Secondly, the salient features of the ISAR image are extracted by subtracting the reconstructed background from the original ISAR image. Finally, this algorithm fuses the salient features with the infrared image using the fusion rule proposed in this paper, through which to get the final fused image. Experimental results show that the algorithm can better preserve both the clear target and detailed information of infrared image and ISAR image in subjective evaluation. Compared with other fusion algorithms, experiments also verify the superiority of the algorithm in objective evaluation. © 2021 IEEE.

关键词： Interpolation

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Performance Analysis of Machine Learning-based Face Detection algorithms in Face image Transmission over AWGN and Fading Channels 16

Performance Analysis of Machine Learning-based Face Detectio...

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16th IEEE international Symposium on Broadband Multimedia Systems and Broadcasting (IEEE BMSB)

作者： Kim, Junghwan Wei, Lan Univ Toledo EECS Dept 2801 W Bancroft St Toledo OH 43606 USA

ISBN: (纸本)9781665449083

Although machine learning and artificial intelligence have been widely applied, noise and interference are still major disturbances to degrade the quality of image transmission and processing efficiency in multimedia data transmission. Based on the face detection system utilizing the machine learning algorithms and artificial intelligence, this paper examines, analyzes and compares the performances of the AdaBoost machine learning algorithm and Convolutional Neural Network (CNN)-based algorithm in processing face information, which is disturbed by channel noise and fading effect encountered in the transmission of face image. The face detection system used is based on HAAR feature extraction. The extracted HAAR features are subjected to classification training and learning of the cascade classifier. Then the face detection is performed on the picture information outside the database. Results of computer simulation show that for the image data affected by the fading and AWGN, the face recognition system still marked the positions of the eyes and mouth with high accuracy. However for fading and higher AWGN, using a machine learning algorithm with a convolutional neural network is better than the ADABOOST algorithm. It can be concluded that the machine learning algorithm can effectively reduce the adverse effect of multimedia data transmission without increasing the SNR and use of higher level of modulation scheme.

关键词： Machine learning Artificial intelligence-based multimedia coding and processing advanced signal processing Face detection system BPSK/QPSK Fading Channel

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SepUnet: DEPTHWISE SEPARABLE CONVOLUTION INTEGRATED U-NET FOR MRI RECONSTRUCTION

SepUnet: DEPTHWISE SEPARABLE CONVOLUTION INTEGRATED U-NET FO...

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IEEE international conference on image processing (ICIP)

作者： Zabihi, Soheil Rahimian, Elahe Asif, Amir Mohammadi, Arash Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada

ISBN: (纸本)9781665441155

Accelerating Magnetic Resonance Imaging (MRI) acquisition process is a critical and challenging medical imaging problem as basic reconstructions obtained from the undersampled k-space often exhibit blur or aliasing effects. Despite its significance and recent advancements in the field of deep neural networks (DNNs), development of deep learning-based MRI reconstruction algorithms is not yet flourished due to unavailability of public and large datasets. The recently introduced large-scale fastMRI dataset is posed to change this state of affairs, however, existing DNN solutions developed based on fastMRI require learning a large number of parameters rendering their practical application limited due to the strict low-latency requirements of real-time MRI acquisition. In this paper, we aim to address this drawback and target reducing the computational cost associated with single-coil reconstruction task. More specifically, the paper proposes a novel deep model referred to as the SepUnet architecture achieving significant reduction in the required number of parameters while maintaining high accuracy. Performance of the proposed SepUnet architecture is evaluated based on the official test dataset from fastMRI illustrating accuracy improvement in comparison to its published counterparts while requiring significantly reduced number of trainable parameters (i.e., the SepUnet architecture is much faster and lighter than its counterparts).

关键词： Magnetic Resonance Imaging (MRI) image Reconstruction Depthwise Separable Convolution Atrous Spatial Pyramidal Pooling (ASPP)

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Gait Phase Recognition Based on Air-pressure Mechanomyogram and Sensor Fusion 6

Gait Phase Recognition Based on Air-pressure Mechanomyogram ...

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6th IEEE international conference on advanced Robotics and Mechatronics (ICARM)

作者： Wang, Enkai Huang, Jian Li, Yuge Cui, Yuqi Li, Xiaolong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9780738133645

Gait phase recognition can improve the control of lower limb exoskeleton robot and promote human-machine collaboration. One of the current mainstream methods is to use electromyogram (EMG) to recognize gait phase, but the EMG signal has shortcomings such as weak signal, difficult to wear, easy to be affected by noise and sweat. Therefore, we designed a novel air-pressure mechanomyograph (PMMG) sensor, and further made a wearable PMMG-based sensing system composed of PMMG-based thighrings and inertial measurement units (IMUs). In order to improve the performance of gait phase recognition, we used five popular machine learning algorithms to fuse the data from PMMG-based thighrings and IMUs. We recruited three experimental subjects and constructed two datasets for different walking conditions: constant speed walking and variable speed walking. Experimental results show that the proposed PMMG sensor is effective, and the gait phase recognition accuracy reached 96.25% by using only the PMMG-based thighrings. In addition, we found that the performance of multi-modal sensor fusion is better than that of single-modal sensor fusion through three comparative experiments. Among the five machine learning algorithms, the SVM fusion model got the highest average accuracy of 98.82%.

关键词： Legged locomotion Support vector machines Machine learning algorithms Mechatronics Measurement units Multimodal sensors Sensor fusion

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Multimodal Deep Kernel Learning: Application to Disaster Response in Social Media Network 4

Multimodal Deep Kernel Learning: Application to Disaster Res...

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4th international conference on Information Communication and signal processing, ICICSP 2021

作者： Yang, Xuanqi Jiu, Mingyuan Sun, Yi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665407571

Recently multimodal learning has become a popular research topic because variety of types of sensors can be used to describe the events, for example, images and texts in the social network for disaster detection and response. It is useful because of different sources of information that data will cause heterogeneous problems. The kernel function can measure the similarity of data, and the fusion of different kernels can handle this problem well. This paper proposes a method based on multimodal deep kernel learning to detect specific categories of content posted on Twitter during a disaster for humanitarian response. Specifically, the convolutional neural network is used to extract image and text features, and obtain multiple kernel functions in each mode. Then a deep neural network is used to fuse multiple basic kernel functions corresponding to the two modalities. Finally, we use Support Vector Machine (SVM) for classification. Applying this method to a real disaster data set, and compared with the baseline, the results prove the effectiveness of our model. © 2021 IEEE.

关键词： image classification

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ENHANCED HYPERSPECTRAL image SUPER-RESOLUTION VIA RGB FUSION AND TV-TV MINIMIZATION

ENHANCED HYPERSPECTRAL IMAGE SUPER-RESOLUTION VIA RGB FUSION...

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IEEE international conference on image processing (ICIP)

作者： Vella, Marija Zhang, Bowen Chen, Wei Mota, Joao F. C. Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China

ISBN: (纸本)9781665441155

Hyperspectral (HS) images contain detailed spectral information that has proven crucial in applications like remote sensing, surveillance, and astronomy. However, because of hardware limitations of HS cameras, the captured images have low spatial resolution. To improve them, the low-resolution hyperspectral images are fused with conventional high-resolution RGB images via a technique known as fusion based HS image super-resolution. Currently, the best performance in this task is achieved by deep learning (DL) methods. Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image. Conversely, model-based algorithms can typically guarantee such measurement consistency. Inspired by these observations, we propose a framework that integrates learning and model based methods. Experimental results show that our method produces images of superior spatial and spectral resolution compared to the current leading methods, whether model- or DL-based.

关键词： Deep learning super-resolution hyper-spectral imaging optimization total variation

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Densely connected convolutional networks for breast cancer histopathological image classification

Densely connected convolutional networks for breast cancer h...

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2021 international conference on signal image processing and Communication, ICSIPC 2021

作者： Li, Jie Chen, Jinling Zhao, Chengming Sichuan Chengdu610599 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

The computer-assisted classification of breast cancer histopathological image in the future is an essential method for the improvement of the diagnostic performance, thus reducing breast cancer deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs, many image classification tasks still remain challenging due to the insufficiency of training data and the lack of the ability to focus on improving classification efficiency. To address these issues, we share a densely connected convolutional network (DenseNet) model that is composed of 3 layers DenseNet, pooling layer, and a classification layer for breast cancer classification in microscopic images. Each layer of DenseNet contains 4 dense blocks. Each dense block jointly uses the dense connection and a novel attention learning mechanisms to increase its ability for discriminative representation. Meanwhile, the transfer learning algorithm is applied to determine the model parameters to extract the features of the patient image that is performed. In order to ensure sufficient data volume, a data enhancement method based on the quad-tree principle is proposed for high-resolution images. On the other hand, the classification probability of each part after dicing is fused by three algorithms of addition, product, and maximum. We evaluated our DenseNet model on the BreastKHis dataset. Our results indicate that the DenseNet model and data enhancement method we adopted can adaptively focus on the study of breast cancer histopathological image classification, thus achieving the state-of-the-art performance in breast cancer classification. The results of the experiments are in terms of patient-level and image-level accuracy. The best recognition accuracy increased to 90.9%-92.5% and 89.3%-91.8%, respectively, compared with previous studies. © 2021 SPIE.

关键词： image classification

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A META-LEARNING FRAMEWORK FOR FEW-SHOT CLASSIFICATION OF REMOTE SENSING SCENE

A META-LEARNING FRAMEWORK FOR FEW-SHOT CLASSIFICATION OF REM...

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Zhang, Pei Bai, Yunpeng Wang, Dong Bai, Bendu Li, Ying Northwestern Polytech Univ Sch Comp Sci Natl Engn Lab Integrated AeroSp Ground Ocean Big Shaanxi Prov Key Lab Speech Image Informat Proc Xian 710129 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Shaanxi Peoples R China

ISBN: (纸本)9781728176055

While achieving remarkable success in remote sensing (RS) scene classification for the past few years, convolutional neural network (CNN) based methods suffer from the demand for large amounts of training data. The bottleneck in prediction accuracy has shifted from data processing limits toward a lack of ground truth samples, usually collected manually by experienced experts. In this work, we provide a meta-learning framework for few-shot classification of RS scene. Under the umbrella of meta-learning, we show it is possible to learn much information about a new category from only 1 or 5 samples. The proposed method is based on Prototypical Networks with a pre-trained stage and a learnable similarity metric. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN, on two challenging datasets: NWPU-RESISC45 and RSD46-WHU.

关键词： Remote sensing scene classification few-shot learning meta-learning

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Junction Monitoring System for Emergency Vehicles and Density control Using image processing

Junction Monitoring System for Emergency Vehicles and Densit...

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2021 international conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2021

作者： Nagarathinam, S. Dhivyapriya, R. Pavithra, C. Harshapradha, M. Saralesh Kumar, R. Kumaraguru College of Technology Department of ECE Tamil Nadu India

ISBN: (纸本)9781665428293

In today's world, everyone is confronted with one of the most important challenges of traffic congestion, which affects almost all major cities. The main cause of increased traffic bottlenecks is a large number of vehicles on the road and insufficient infrastructure. With the help of current traffic lights, it is essential to successfully regulate traffic flow. Because most traffic signals have a predetermined green light sequence, the green light sequence is established without regard for the presence of emergency vehicles. As a result, emergency vehicles such as ambulances, police cars, fire engines, and other emergency vehicles become trapped in traffic and are delayed in arriving at their destination. The goal of this project is to regulate traffic by determining traffic density on each side of the road and enabling a controlling option;in the event that an emergency vehicle arrives, its direction is determined from its siren signal, and the signals are enabled. The image was shot by the traffic junction's camera when it was free, and it serves as the reference image. The camera continuously captures real-time images of the traffic intersection. The microcontroller, on the other hand, generates the difference frame by comparing the real-time image to the reference image and calculating traffic density using Digital image processing algorithms. The density data is transferred to the server, and traffic lights are managed in accordance with it. When an emergency vehicle is recognized, traffic light sequences are automatically activated in such a way that a green signal is given in the direction where the vehicle is detected, and normal operation is assumed once it has passed through the signal. © 2021 IEEE.

关键词： image processing

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