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14th International Conference on Digital Image processing, ICDIP 2022

作者： Ding, Hui Chen, Weifeng Li, Bo He, Zhifen School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

ISBN: (纸本)9781510657564

Inspired by the state-of-the-art performance of MLP-Mixer on image classification task, multilayer perceptron (MLP) model attracts a number of researcher's attention again. Although various MLP architectures have been proposed, most of them focus on image classification domain. In this paper, we extend MLP to the task of image generation based on generative adversarial network (GAN). The pure MLP model is not friendly to small dataset because it is a data-hungry architecture. Thus, we leverage a hybrid model to solve the problem which uses MLP blocks as generator and CNN blocks as discriminator. Experimental results demonstrate that our model outperforms the pure CNN network on CIFAR-10 dataset in view of two different evaluation metrics. Besides, we apply eight widely popular MLP structures to our generator to validate which one achieves the most excellent performance in image generation task. © 2022 SPIE.

关键词： Generative adversarial networks

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A Novel Wideband CPW-Fed 5.8GHz RFID Tag Antenna

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电子学报(英文版) 2012年第2期21卷 202-208页

作者： LI Huihui MOU Xuanqin JI Zhen YU Hang LI Yan Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an 710049 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Shenzhen Key Laboratory of Embedded System Design Shenzhen 518060 China

A novel wideband 5.8GHz CPW-fed an- tenna is presented for Radio frequency identification (RFID) tag. Four U-shaped and four L-shaped branches are used as additional resonators to achieve wideband operation. The proposed antenna was analyzed numeri- cally using the Method of moment (MOM) and the Fi- nite element method (FEM). With the antenna size lim- ited to 30 × 30mm2, the ?10dB bandwidth obtained by MOM is 3.235GHz (5.765～9GHz) and the ?9.5dB band- width obtained by FEM is 2.74GHz (5.32～8.06GHz), cor- responding to 55.7% and 47.2% of the center frequency 5.8GHz respectively. Moreover, the simulated results show that the proposed antenna has gain of more than 4.8dBi and the radiation pattern is nearly omnidirectional in the H-plane. The measured ?10dB bandwidth is 2.68GHz (5.63GHz～8.31GHz), 46.2% of the 5.8GHz frequency. Fur- thermore, there are three measured resonant frequencies at 1.34GHz, 3.23GHz and 5.8GHz with lower than ?10dB return loss respectively. The measurement result achieves a wideband RFID tag antenna performance and is in good agreement with the calculated results.

关键词： RFID标签天线尺寸共面波导宽带无线电频率识别有限元分析网络连接数值分析

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FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud 26

FASTC: A Fast Attentional Framework for Semantic Traversabil...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie Liu, Wei Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

ISBN: (纸本)9781643684369

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2023 The Authors.

关键词： Semantics

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Adaptive synthetic aperture radar image enhancement

Adaptive synthetic aperture radar image enhancement

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MIPPR 2009 - Multispectral Image Acquisition and processing: 6th International Symposium on Multispectral Image processing and pattern recognition

作者： Cheng, Hua Tian, Jinwen Huazhong University of Science and Technology Institute for Pattern Recognition and Artificial Intelligence State Key Laboratory for Multi-spectral Information Processing Technologies China

ISBN: (纸本)9780819478054

An adaptive SAR image enhancement method is presented for reducing the speckle noise and increasing the contrast of synthetic aperture radar (SAR) images. First, a fuzzy logic based filter, employing fuzzy edge to weight the contributions of pixel values in filter window, is used to filter the speckles. Second, the original SAR image is decomposed into lowfrequency component and high-frequency component. The fuzzy filtered image is viewed as the low-frequency component, and the contrast limited adaptive histogram equalization algorithm is used to increase its contrast. The highfrequency component is obtained by subtracting the low-frequency component from the original image, and its gain is controlled by fuzzy structural which employed to express the degree of a pixel belonging to structures. After processed one after the other, the two components are added together to form the final enhanced SAR image. Experimental results show the excellent effect of the proposed method by visual observation and numerical measurement. Many fine structures and little speckle noise can be seen from the enhanced SAR images. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Image enhancement

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Imaging lidar based 3D terrain matching using feature vector

Imaging lidar based 3D terrain matching using feature vector

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MIPPR 2007: Multispectral Image processing

作者： Hua, Cheng Jinwen, Tian Jie, Ma Junbin, Gong Lin, Wu State Key Laboratory for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology China

ISBN: (纸本)9780819469519

A novel 3D terrain matching algorithm is presented in this paper. A terrain feature vector map (FVM), composed of local mean and local gradient, is employed to represent the terrain elevation map (TEM). Compared with traditional matching algorithm using the magnitude of gradient to match, the new algorithm uses each component of the gradient vector to match individually, and it is able to generate two interim matching positions. Different from traditional matching algorithms which usually estimate an optimum matching position under some criterions at the end, the new algorithm fused the two interim matching positions to generate a final matching position or refuse to position in order to increase the matching confidence, which is very important because it is hardly acceptable to employ a mismatched position to correct the error of Inertial Navigation System (INS). Due to the stability of terrain and the high-precision of lidar ranging, the mean of a sensed terrain elevation map (STEM) sized terrain is quite stable. So it is bestowed to accelerate the matching process and to reduce mismatches at different terrain heights. Compared with other mismatch-eliminated methods based on neural network (NN) or support vector machine (SVM), the new method do not need training samples and is more stable and robust. Experimental results show that the proposed algorithm is effective and robust.

关键词： pattern matching

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Face recognition with Convolutional Neural Networks and subspace learning 2

Face Recognition with Convolutional Neural Networks and subs...

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2nd International Conference on Image, Vision and Computing, ICIVC 2017

作者： Wan, Lihong Liu, Na Huo, Hong Tao, Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781509062379

Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture. Then, two types of subspace learning methods, namely, linear discriminate analysis (LDA) and whitening principal component analysis (WPCA), are respectively introduced to learn the subspace of the activation vectors for face recognition under multiple samples per subject and single sample per subject circumstances. The goals of applying subspace learning to the activation vectors are obtaining compact representation (dimensionality reduction) and performance improvement. Experiments on two face databases (CMU PIE and FERET) demonstrate the effectiveness of VGG-Face + LDA and VGG-Face + WPCA, compared with state-of-the-art methods. © 2017 IEEE.

关键词： Principal component analysis

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Blurred image recognition based on complex moment invariants

Blurred image recognition based on complex moment invariants

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2004 International Conference on Image processing, ICIP 2004

作者： Tianxu, Zhang Jin, Liu Institute for Pattern Recognition and Artifical Intelligence State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology China Institute for Pattern Recognition and Artifical Intelligence HuaZhong University of Science and Technology China

ISBN: (纸本)0780385543

An important class of radiometric degradations we are faced with often in practice is image blurring. Special attention is paid to the recognition of the blurred image by moment invariant approach. Some important rules of complex moments for the blurred image are presented. Based on these rules, a useful subset of moment invariants is introduced, that are not affected by the blur, rotation, scale, and translation of the images. The experiments have shown that these invariants can be successfully used in recognition of the blurred image. ©2004 IEEE.

关键词： pattern recognition

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Prototype-guided Unsupervised Domain Adaptation for Semantic Segmentation

Prototype-guided Unsupervised Domain Adaptation for Semantic...

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2023 IEEE International Conference on Control, Electronics and Computer Technology, ICCECT 2023

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

ISBN: (纸本)9798350399028

Semantic segmentation is one of the most important research directions in the field of computer vision, and has a wide range of applications for autonomous driving, medical imaging, intelligent security, etc. Unsupervised domain adaptation is the mainstream research topic in recent years, which can use a large number of labeled source samples to complete the segmentation task in target domain without labeled target samples. In this paper, we propose a prototype-guided unsupervised domain adaptation for semantic segmentation based on ProDA model. Due to lacking of labeled target samples and the prior probability, a prototype distance loss based on target domain is proposed to optimize the distribution of features by measuring the distance between features and the updated prototype and designing an adaptive threshold strategy. Meanwhile, a smoothing loss is proposed to alleviate the impact of source samples on our model and improve the prediction performance of the network. By conducting experiments on the GTA5 to Cityscapes scenarios, the results show that compared with the original model, the loss optimization improves mIoU by1.52. © 2023 IEEE.

关键词： Semantic Segmentation

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Motion Planning and Tracking Control Method Based on CoppeliaSim for UGV in Irregular Terrain 5

Motion Planning and Tracking Control Method Based on Coppeli...

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

作者： Huang, Jiale Chen, Zhihua Zhong, Yongbo Zeng, Wenrui Ji, Chuanmin Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China

ISBN: (纸本)9798350307320

Path planning and tracking algorithms are one of the cores of autonomous navigation technology for unmanned ground vehicle (UGV). In this paper, a local obstacle avoidance method based on event-triggered control is proposed to ensure that the UGV avoids obstacles in the tracking process. First, the path planning and tracking control problem of the UGV is modeled and formulated. Second, the improvement and logical correlation of the relevant algorithms are described in detail. Finally, ajoint simulation of MATLAB and V-rep is performed. The simulation results show that event-triggered control can largely improve the reliability of the UGV path planning and tracking process, and verify the effectiveness of the algorithm. © 2023 IEEE.

关键词： Motion planning

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Encapsulation strategies on 2D materials for field effect transistors and photodetectors

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Chinese Chemical Letters 2022年第5期33卷 2281-2290页

作者： Wenjuan Huang Yue Zhang Mengting Song Bugao Wang Huayi Hou Xiaozong Hu Xiangbai Chen Tianyou Zhai Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205China State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Green Catalysis Center College of ChemistryZhengzhou UniversityZhengzhou 450001China

Two-dimensional(2D)layered materials provide a promising alternative solution for overcoming the scal-ing limits in conventional Si-based ***,practical applications of 2D materials are facing crucial bottlenecks,particularly that arising from the instability under ambient *** studies of degradation mechanisms and protecting strategies for overcoming the ambient instability of 2D materials have attracted extensive research attentions,both experimentally and *** review attempts to provide an overview on the recent progress of the encapsulation strategies for 2D *** en-capsulation strategies of mechanical transfer,polymer capping,atomic layer deposition,in-situ oxidation,and surface functionalization are systematically discussed for improving the ambient stability of 2D *** addition,the current advances in air-stable and high-performance 2D materials-based field effect transistors(FETs)and photodetectors assisted by the encapsulation strategies are ***,the future directions of encapsulation techniques of 2D materials for FETs and photodetectors applications are suggested.

关键词： Two-dimensional materials Encapsulation Degradation Stability Field effect transistors Photodetectors

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