检索结果-内蒙古大学图书馆

作者： Lijie Zhou Xufeng Ling Shan Zhu Zheng Sun Jie Yang AI School Tianhua College Shanghai Normal University Institute of Image Processing and Pattern Recognition Shanghai Jiao tong University

Automatic detection of PCB defects become a difficult work in electronics industry,with the rapid development of Integrated Circuit.A good detection method can effectively improve production efficiency and reduce the costs of quality *** traditional image processing methods have the backwards of complicated system structure,difficult image segmentation,and low detection accuracy,*** order to solve the above problems,this paper proposes a method of extracting features by self-supervised learning with supervised learning to complete the classification *** method uses a deep neural network model based on self-supervised learning technology,uses multi-head self-attention mechanism;automatically recognize single or multiple defect areas in PCB,to extract defects features of PCB,and uses full connection classifier to classify *** open-source(DeepPCB) is selected as the dataset,including 6 defect categories and non-defect *** the training process,data enhancement technology is used to increase the training samples,train the model and make it converge,and realize low error recognition on the verification *** experimental results show that the features extracted by the selfsupervised learning method are richer than those extracted by the supervised *** composite model formed by combining the self-attention mechanism has high accuracy and good robustness,reaches the classification accuracy of 94.33%,and has high accuracy and good generalization ability.

关键词： PCB Defects Detection Self-supervised Learning Deep Learning Self-attention

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An Improved Embedding Model for Zero-Shot Classification based on Attention Mechanism

An Improved Embedding Model for Zero-Shot Classification bas...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Zongfeng Wu Qishen Li Xiao Yu Xu Peng School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

It is well-known that the auxiliary information plays a key role in zero-shot classification. However, most of the existing popular methods do not make effective use of auxiliary information. To address this issue, we propose an improved embedding model for zero-shot classification based on attention mechanism, called EMAM. In the proposed EMAM, we first add an attention mechanism to effectively extract the key information of auxiliary information in zero-shot classification. Then optimizes the objective function to improve the recognition rate of this model. Finally the experimental comparison is implemented on the standard zero-shot learning datasets. The experimental results demonstrate that our proposed EMAM not only verifies its validity, but also achieves good results.

关键词： Automation Conferences Linear programming Data mining Information technology Standards

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Human Pose Estimation Based on Multi-resolution Feature Fusion Network

Human Pose Estimation Based on Multi-resolution Feature Fusi...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Weibai Duan Qishen Li Sihao Yuan Xiao Yu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

This article focuses on the study of multi-resolution feature fusion methods for human pose estimation. Most of the existing pose estimation methods are based on high-resolution gradually reducing the resolution to learn advanced semantic features, and then gradually recovering high-resolution features from the low-resolution semantic features to locate keypoints of the person instance. The process without fusion of rich features will inevitably lose more spatial and semantic information. The purpose of the multi-resolution feature fusion method proposed in this paper is to fuse spatial and semantic information on multiple scale feature maps, in addition, our network use the multi-receptive field fusion module on the same scale features to enhance feature extraction. Our methods can utilize more feature information to accurately locate keypoints.

关键词： Fuses Semantics Pose estimation Feature extraction Spatial resolution Kernel Information technology

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3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images

arXiv

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arXiv 2023年

作者： Liu, Mengqing Shao, Xiao Jiang, Liping Wu, Kaizhi School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing China The First Affiliated Hospital of Nanchang University Nanchang University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Background: Segment prostates from transrectal ultrasound (TRUS) images plays an essential role in the diagnosis and treatment of prostate cancer. However, traditional segmentation methods are time-consuming and laborious. To address this issue, there is an urgent need to develop computer algorithms that can automatically segment prostates from TRUS images, which makes it become the direction and form of future development. Purpose: Automatic prostate segmentation in TRUS images has always been a challenging problem, since prostates in TRUS images have ambiguous boundaries and inhomogeneous intensity distribution. Although many prostate segmentation methods have been proposed, they still need to be improved due to the lack of sensibility to edge information. Consequently, the objective of this study is to devise a highly effective prostate segmentation method that overcomes these limitations and achieves accurate segmentation of prostates in TRUS images. Methods: A 3D edge-aware attention generative adversarial network (3D EAGAN)-based prostate segmentation method is proposed in this paper, which consists of an edge-aware segmentation network (EASNet) that performs the prostate segmentation and a discriminator network that distinguishes predicted prostates from real prostates. The proposed EASNet is composed of an encoder-decoder-based U-Net backbone network, a detail compensation module, four 3D spatial and channel attention modules, an edge enhance module, and a global feature extractor. The detail compensation module is proposed to compensate for the loss of detailed information caused by the down-sampling process of the encoder. The features of the detail compensation module are selectively enhanced by the 3D spatial and channel attention module. Furthermore, an edge enhance module is proposed to guide shallow layers in the EASNet to focus on contour and edge information in prostates. Finally, features from shallow layers and hierarchical features from the decod

关键词： Generative adversarial networks

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Radio frequency interference mitigation using pseudoinverse learning autoencoders

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Research in Astronomy and Astrophysics 2020年第8期20卷 121-128页

作者： Hong-Feng Wang Mao Yuan Qian Yin Ping Guo Wei-Wei Zhu Di Li Si-Bo Feng Image Processing and Pattern Recognition Laboratory School of Artificial IntelligenceBeijing Normal UniversityBeijing 100875China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Information Management Dezhou UniversityDezhou 253023China Image Processing and Pattern Recognition Laboratory School of System ScienceBeijing Normal UniversityBeijing 100875China Institute for Astronomical Science Dezhou UniversityDezhou 253023China University of Chinese Academy of Sciences Beijing 100049China Hanvon Technology Co. LtdBeijing 100193China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa

Radio frequency interference(RFI)is an important challenge in radio *** comes from various sources and increasingly impacts astronomical observation as telescopes become more *** this study,we propose a fast and effective method for removing RFI in pulsar *** use pseudo-inverse learning to train a single hidden layer auto-encoder(AE).We demonstrate that the AE can quickly learn the RFI signatures and then remove them from fast-sampled spectra,leaving real pulsar *** method has the advantage over traditional threshold-based filter method in that it does not completely remove contaminated channels,which could also contain useful astronomical information.

关键词： pulsars:general methods:numerical methods:data analysis

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VSGNet: visual saliency guided network for skin lesion segmentation

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Expert Systems with Applications 2025年

作者： Zhefei Cai Yingle Fan Tao Fang Wei Wu Laboratory of Pattern Recognition and Image Processing Hangzhou Dianzi University Hangzhou 310018 China Department of Biomedical Engineering University of Miami Florida 33124 USA Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang 310058 China

The accuracy of skin lesion segmentation is of great significance for the subsequent clinical diagnosis. In order to improve the segmentation accuracy, some pioneering works tried to embed multiple complex modules, or used the huge Transformer framework, but due to the limitation of computing resources, these type of large models were not suitable for the actual clinical environment. To address the coexistence challenges of precision and lightweight, we propose a visual saliency guided network (VSGNet) for skin lesion segmentation, which generates saliency images of skin lesions through the efficient attention mechanism of biological vision, and guides the network to quickly locate the target area, so as to solve the localization difficulties in the skin lesion segmentation tasks. VSGNet includes three parts: Color Constancy module, Saliency Detection module and Ultra Lightweight Multi-level Interconnection Network (ULMI-Net). Specially, ULMI-Net uses a U-shaped structure network as the skeleton, including the Adaptive Split Channel Attention (ASCA) module that simulates the parallel mechanism of biological vision dual pathway, and the Channel-Spatial Parallel Attention (CSPA) module inspired by the multi-level interconnection structure of visual cortices. Through these modules, ULMI-Net can balance the efficient extraction and multi-scale fusion of global and local features, and try to achieve the excellent segmentation results at the lowest cost of parameters and computational complexity. To validate the effectiveness and robustness of the proposed VSGNet on three publicly available skin lesion segmentation datasets (ISIC2017, ISIC2018 and PH2 datasets). The experimental results show that compared to other state-of-the-art methods, VSGNet improves the Dice and mIoU metrics by 1.84 % and 3.34 %, respectively, and with a 196 × and 106 × reduction in the number of parameters and computational complexity. This paper constructs the VSGNet integrating the biological vis

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SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution

arXiv

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arXiv 2023年

作者： Nian, Bingkun Tang, Fenghe Ding, Jianrui Zhang, Pingping Yang, Jie Kevin Zhou, S. Liu, Wei Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China China School of Computer Science and Technology Harbin Institute of Technology China School of artificial intelligence Dalian University of Technology China

In this paper, we present a high-performance deep neural network for weak target image segmentation, including medical image segmentation and infrared image segmentation. To this end, this work analyzes the existing dynamic convolutions and proposes dynamic parameter convolution (DPConv). Furthermore, it reevaluates the relationship between reconstruction tasks and segmentation tasks from the perspective of DPConv, leading to the proposal of a dual-network model called the Siamese Reconstruction-Segmentation Network (SRSNet). The proposed model is not only a universal network but also enhances the segmentation performance without altering its structure, leveraging the reconstruction task. Additionally, as the amount of training data for the reconstruction network increases, the performance of the segmentation network also improves synchronously. On seven datasets including five medical datasets and two infrared image datasets, our SRSNet consistently achieves the best segmentation results. The code is released at https://***/fidshu/SRSNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Expression-Latent-Space-guided GAN for Facial Expression Animation based on Discrete Labels

Expression-Latent-Space-guided GAN for Facial Expression Ani...

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International Conference on Automatic Face and Gesture recognition

作者： Xueping Wang Weixin Li Di Huang Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781665431774

Facial expression animation aims to synthesize face images that correspond to the target expression in a continuum. This is a challenging task because the animation not only cares about the smooth transition in the generated sequence but also needs to take the facial expression and identity details into consideration. Most existing expression animation methods resort to continuous expression labels, e.g. Action Units (AUs) or landmark sequences. Compared with discrete expression labels, the annotations of a considerable part of them are ambiguous and prone to errors. However, how to animate facial expression conditioned on discrete expression labels is less investigated and existing methods cannot generate satisfactory facial details. To tackle these problems, we propose an end-to-end Expression-Latent-Space-guided Generative Adversarial Network (ELS-GAN) model, which utilizes discrete expression labels as input to generate images with expected expressions, and employs expression latent space learning to control the expression changing process. An expression ranking loss is also proposed to strengthen expression intensity learning during generation. Moreover, we put forward a Self-Attention Generator to synthesize face images with fine details by considering both local areas and the long-range dependency of different areas. Extensive experiments show that our method can generate continuous intermediate expression between source and target expressions only conditioned on discrete labels and superior results are achieved compared with state-of-the-art methods.

关键词： Interpolation Face recognition Genetic expression Process control Gesture recognition Animation Generative adversarial networks

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Building Extraction Method in Remote Sensing image

Building Extraction Method in Remote Sensing Image

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International Conference on Data processing Techniques and Applications for Cyber-Physical Systems, DPTA 2019

作者： Han, Qinzhe Zheng, Xin Yin, Qian Chen, Ziyi Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China College of Information Science and Technology University parkPA United States Beijing Normal University Beijing China

ISBN: (纸本)9789811514678

Identifying buildings in disaster areas quickly and conveniently plays an important role in post-disaster reconstruction and disaster assessment. Aiming at the technical requirements of earthquake relief projects, this paper proposed a building extraction method in remote sensing images by combining traditional digital image processing methods and convolution neural network. First, In the pretreatment process, remote sensing images are transformed from RGB space to HSV space. Then, shadows in the images are weakened by using the Retinex model de-shadowing algorithm. Finally, buildings in the image are classified and recognized by using a convolution neural network. Experiments prove that compared with the fast R-CNN algorithm, the detection accuracy is improved. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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SiamCMN: Jointing cycle memory and correlation network for Siamese object tracking

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Optik 2023年第1期286卷

作者： Zhang, Baohua Zhang, Nianchao Li, Yongxiang Lu, Xiaoqi Gu, Yu Li, Jianjun School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia014010 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou Inner Mongolia014010 China School of Information Engineering Mongolia Industrial University Hohhot Inner Mongolia010051 China College of Energy and Transportation Engineering Inner Mongolia Agricultural University Hohhot Inner Mongolia010018 China

In heterogeneous scenes with latent non-deterministic states, template features determine the performance of the Siamese network-based trackers, however, background noise is easily introduced in the search area matching process, which causes the trackers lack sufficient discrimination. To solve this problem, this paper proposes an object tracking algorithm based on the cyclic memory module. Firstly, the extracted primary features are mapped to the cyclic spatial memory network, the spatial memory unit is used to store the interference components. On this basis, the cyclic structure is used to filter out the interference components and refine the template features. Then a channel correlation network is introduced into the proposed method, which can mine semantic complementary information and capture global contextual relevance by modeling the positive and the negative correlation between the channels. The experimental results on tracking benchmarks such as OTB50, OTB100, UAV123, VOT2018, GOT10k, and LaSOT have shown that the proposed method outperforms recent representative algorithms in tracking accuracy in challenging scenarios. © 2023 Elsevier GmbH

关键词： Semantics

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