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International Conference on Electronics, Communications and Information Technology (CECIT)

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

ISBN: (纸本)9781665437585

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 inspection. The traditional image processing methods have the backwards of complicated system structure, difficult image segmentation, and low detection accuracy, etc. In 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 task. This 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 features. The open-source (DeepPCB) is selected as the dataset, including 6 defect categories and non-defect categories. In 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 dataset. The experimental results show that the features extracted by the self-supervised learning method are richer than those extracted by the supervised learning. The 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： Training image segmentation Electronics industry Supervised learning Quality control Feature extraction Robustness

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Learning with asymmetric kernels: Least squares and feature interpretation

arXiv

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arXiv 2022年

作者： He, Mingzhen He, Fan Shi, Lei Huang, Xiaolin Suykens, Johan A.K. Institute Of Image Processing And Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory For Contemporary Applied Mathematics Fudan University Shanghai200433 China School Of Mathematical Sciences Fudan University Shanghai200433 China KU Leuven LeuvenB-3001 Belgium

Asymmetric kernels naturally exist in real life, e.g., for conditional probability and directed graphs. However, most of the existing kernel-based learning methods require kernels to be symmetric, which prevents the use of asymmetric kernels. This paper addresses the asymmetric kernel-based learning in the framework of the least squares support vector machine named AsK-LS, resulting in the first classification method that can utilize asymmetric kernels directly. We will show that AsK-LS can learn with asymmetric features, namely source and target features, while the kernel trick remains applicable, i.e., the source and target features exist but are not necessarily known. Besides, the computational burden of AsK-LS is as cheap as dealing with symmetric kernels. Experimental results on the Corel database, directed graphs, and the UCI database will show that, in the case asymmetric information is crucial, the proposed AsK-LS can learn with asymmetric kernels and performs much better than the existing kernel methods that have to do symmetrization to accommodate asymmetric kernels. © 2022, CC BY.

关键词： Directed graphs

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Two-dimensional multi-fiber spectrum image correction based on machine learning techniques

arXiv

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arXiv 2020年

作者： Xu, Jiali Yin, Qian Guo, Ping Zheng, Xin Image Processing and Pattern Recognition Lab. School of Artificial Intelligence Beijing Normal University Beijing100875 China Image Processing and Pattern Recognition Lab School of Systems Science Beijing Normal University Beijing100875 China

Due to limited size and imperfect of the optical components in a spectrometer, aberration has inevitably been brought into two-dimensional multi-fiber spectrum image in LAMOST, which leads to obvious spacial variation of the point spread functions (PSFs). Consequently, if spatial variant PSFs are estimated directly, the huge storage and intensive computation requirements result in deconvolutional spectral extraction method become intractable. In this paper, we proposed a novel method to solve the problem of spatial variation PSF through image aberration correction. When CCD image aberration is corrected, PSF, the convolution kernel, can be approximated by one spatial invariant PSF only. Specifically, machine learning techniques are adopted to calibrate distorted spectral image, including Total Least Squares (TLS) algorithm, intelligent sampling method, multi-layer feed-forward neural networks. The calibration experiments on the LAMOST CCD images show that the calibration effect of proposed method is effectible. At the same time, the spectrum extraction results before and after calibration are compared, results show the characteristics of the extracted one-dimensional waveform are more close to an ideal optics system, and the PSF of the corrected object spectrum image estimated by the blind deconvolution method is nearly central symmetry, which indicates that our proposed method can significantly reduce the complexity of spectrum extraction and improve extraction accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical transfer function

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Unsupervised Learning of Depth and Pose Based on Monocular Camera and Inertial Measurement Unit (IMU)

Unsupervised Learning of Depth and Pose Based on Monocular C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yanbo Wang Hanwen Yang Jianwei Cai Guangming Wang Jingchuan Wang Yi Huang University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Weitong Vision Technology Co. Ltd.

The main content of the research in this paper is the estimation of depth and pose based on monocular vision and Inertial Measurement Unit (IMU). The usual depth estimation network and pose estimation network require depth ground truth or pose ground truth as a supervised signal for training, while the depth ground truth and pose ground truth are hard to obtain, and monocular vision based depth estimation cannot predict absolute depth. In this paper, with the help of IMU, which is inexpensive and widely used, we can obtain angular velocity and acceleration information. Two new supervision signals are proposed and the calculation expressions are given. Among them, the model trained with acceleration constraint shows a good ability to estimate the absolute depth during the test. It can be considered that the model can estimate the absolute depth. We also derive the method of estimating the scale factor during the test from the acceleration constraint, and also achieve good results as the acceleration constraint does. In addition, this paper also studies the method of using IMU information as pose network input and as selecting conditions. Moreover, it analyzes and discusses the experimental results. At the same time, we also evaluate the effect of the pose estimation of the relevant models. This article starts by reviewing the achievements and deficiencies of the work in this field, combines the use of IMU, puts forward three new methods such as a new loss function, and conducts a test analysis and discussion of relevant indicators on the KITTI data set.

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Learning from partially labeled data 28

Learning from partially labeled data

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28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020

作者： Mehrkanoon, Siamak Huang, Xiaolin Suykens, Johan A.K. Department of Data Science and Knowledge Engineering Maastricht University Netherlands Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Department of Electrical Engineering ESAT-STADIUS KU Leuven LeuvenB-3001 Belgium

ISBN: (纸本)9782875870742

Providing sufficient labeled training data in many application domains is a laborious and costly task. Designing models that can learn from partially labeled data, or leveraging labeled data in one domain and unlabeled data in a different but related domain is of great interest in many applications. In particular, in this context one can refer to semi-supervised modelling, transfer learning, domain adaptation and multi-view learning among oth- ers. There are several possibilities for designing such models ranging from shallow to deep models. These type of models have received increasing in- terest due to their successful applications in real-life problems. This paper provides a brief overview of recent techniques in learning from partially labeled data. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Neural networks

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Random Fourier Features for Asymmetric kernels

arXiv

引用

arXiv 2022年

作者： He, Mingzhen He, Fan Liu, Fanghui Huang, Xiaolin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dongchuan RD. Minhang District Shanghai200240 China Laboratory for Information and Inference Systems École Polytechnique Fédérale de Lausanne EPFL SV-BMC AA-B118 Lausanne1015 Switzerland

The random Fourier features (RFFs) method is a powerful and popular technique in kernel approximation for scalability of kernel methods. The theoretical foundation of RFFs is based on the Bochner theorem (Bochner, 2005) that relates symmetric, positive definite (PD) functions to probability measures. This condition naturally excludes asymmetric functions with a wide range applications in practice, e.g., directed graphs, conditional probability, and asymmetric kernels. Nevertheless, understanding asymmetric functions (kernels) and its scalability via RFFs is unclear both theoretically and empirically. In this paper, we introduce a complex measure with the real and imaginary parts corresponding to four finite positive measures, which expands the application scope of the Bochner theorem. By doing so, this framework allows for handling classical symmetric, PD kernels via one positive measure;symmetric, non-positive definite kernels via signed measures;and asymmetric kernels via complex measures, thereby unifying them into a general framework by RFFs, named AsK-RFFs. Such approximation scheme via complex measures enjoys theoretical guarantees in the perspective of the uniform convergence. In algorithmic implementation, to speed up the kernel approximation process, which is expensive due to the calculation of total mass, we employ a subset-based fast estimation method that optimizes total masses on a sub-training set, which enjoys computational efficiency in high dimensions. Our AsK-RFFs method is empirically validated on several typical large-scale datasets and achieves promising kernel approximation performance, which demonstrate the effectiveness of AsK-RFFs. © 2022, CC BY.

关键词： Computational efficiency

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Diabetic Retinopathy Lesion Segmentation Using Deep Multi-Scale Framework

SSRN

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SSRN 2023年

作者： Guo, Tianjiao Yang, Jie Yu, Qi Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong Univeristy Shanghai China Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Clinical Research Center for Eye Diseases Shanghai China

The segmentation of diabetic retinopathy (DR) lesions is important for large-scale screening using color fundus photography (CFP) images. The difficulty of this task is that the DR lesions have various sizes, shapes, and intensities. Traditional handcrafted feature-based approaches are unsatisfactory, and recent deep-learning-based approaches ignore the features of DR lesions. In this paper, we propose a segmentation approach that segments four typical DR lesions simultaneously based on convolutional neural networks (CNN). The raw CFP image is first pre-processed and resized to different sizes. A set of fully convolutional neural networks (FCNs) with various input sizes are then trained to extract the lesions from different scales. An auxiliary CNN is then introduced to fuse the output from these FCNs and refine the segmentation *** conducted our experiments on one local dataset and three public datasets including IDRiD, DDR, and DIARETDB1. The results of the area under the precision-recall-curve (AUPR) show that our approach achieves competitive *** improvement in performance indicates that this approach is beneficial to DR lesion segmentation and has potential in other segmentation tasks. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Abundant Semantic Information Module for Object Detection

Abundant Semantic Information Module for Object Detection

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Taida Wu Qishen Li Qiufeng Li Jun Gao School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement.

关键词： Location awareness Fuses High performance computing Semantics Neural networks Object detection Detectors

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Low-light image enhancement model based on self-calibration illumination and color adjustment

Low-light image enhancement model based on self-calibration ...

引用

Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Guiwen Zhang Qishen Li Qiufeng Li Taida Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved.

关键词： Training Pollution image color analysis Convolution Brightness Lighting Information filters

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Indoor Elderly Fall recognition System Based on Target Detection and Self-attention Model

Indoor Elderly Fall Recognition System Based on Target Detec...

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International Conference on Electronics, Communications and Information Technology (CECIT)

作者： Min Liu Xufeng Ling Jie Yang School of Health Tianhua College Shanghai Normal University Shanghai China AI School Tianhua College Shanghai Normal University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781665437585

With the increasing aging of the society, a series of consequences caused by falls of the elderly have become a serious medical problem and a real social problem. Aiming at the fall behavior of the elderly, the research and development of human fall recognition technology has practical application value. This paper proposes a method to recognize human targets in indoor images using target detection technology, and then use the trained neural network based on Self-attention technique named Vision Transformer (ViT) to recognize the falling posture of the elderly. Firstly, the target detection model is optimized and trained for indoor scene, so that the model can accurately detect human targets in indoor scene; Next, the indoor scene human target image is preprocessed, and the indoor human pose image data set is constructed; Then, the data set is used to train the ViT depth network, and enable it to correctly classify five postures (standing, sitting, lying, bending and crawling). The experimental results show that ViT network achieves high accuracy and excellent generalization capacity for the above 5 human posture and can realize the recognition of elderly falls.

关键词： Technological innovation image recognition Target recognition Object detection Transformers Data models Robustness

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