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arXiv 2020年

作者： Chen, Congmin Mou, Xuanqin The Institute of Image Processing and Pattern Recognition Xi’an Jiaotong University Xi’an710049 China

image quality assessment that aims at estimating the subject quality of images, builds models to evaluate the perceptual quality of the image in different applications. Based on the fact that the human visual system (HVS) is highly sensitive to structural information, the edge information extraction is widely applied in different IQA metrics. According to previous studies, the image gradient magnitude (GM) and the Laplacian of Gaussian (LOG) operator are two efficient structural features in IQA tasks. However, most of the IQA metrics achieve good performance only when the distorted image is totally registered with the reference image, but fail to perform on images with small translations. In this paper, we propose an FR-IQA model with the quadratic sum of the GM and the LOG signals, which obtains good performance in image quality estimation considering shift-insensitive property for not well-registered reference and distortion image pairs. Experimental results show that the proposed model works robustly on three large scale subjective IQA databases which contain a variety of distortion types and levels, and stays in the state-of-the-art FR-IQA models no matter for single distortion type or across whole database. Furthermore, we validated that the proposed metric performs better with shift-insensitive property compared with the CW-SSIM metric that is considered to be shift-insensitive IQA so far. Meanwhile, the proposed model is much simple than the CW-SSIM, which is efficient for *** Codes 94A08 Copyright © 2020, The Authors. All rights reserved.

关键词： Laplace transforms

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Multi-object tracking with pre-classified detection 5th

Multi-object tracking with pre-classified detection

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5th International Conference on Robot Intelligence Technology and Applications, RiTA 2017

作者： Ren, Siqi Zhou, Yue He, Liming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783319784519

Tracking-by-detection is a popular tracking framework nowadays. The paradigm determines that detections will bring huge impact on the final tracking result. Based on the idea, to improve the tracking precision, we propose a novel algorithm which will first divide the detections into false ones, high uncertainty and low uncertainty ones. After that, we first penalize the false detection boundingboxes and then we construct low and high uncertainty tree for different types of detections. For low uncertainty detections, we construct their tracking trees once, for high uncertainty detections, we adopt an improved MHT to delay the data association decision till the end of the sliding windows and make the decision with all the information in the sliding window. Experiments demonstrate that our algorithm can achieved competitive results with state-of-the-art trackers on some challenging datasets such as MOTChallenge2016 [11]. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Object detection

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SSIM-based CTU-level joint optimal bit allocation and rate distortion optimization

arXiv

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arXiv 2020年

作者： Li, Yang Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an710049 China

Structural similarity (SSIM)-based distortion DSSIM is more consistent with human perception than the traditional mean squared error DMSE. To achieve better video quality, many studies on optimal bit allocation (OBA) and rate-distortion optimization (RDO) used DSSIM as the distortion metric. However, many of them failed to optimize OBA and RDO jointly based on SSIM, thus causing a non-optimal R-DSSIM performance. This problem is due to the lack of an accurate R-DSSIM model that can be used uniformly in both OBA and RDO. To solve this problem, we propose a DSSIM-DMSE model first. Based on this model, the complex R-DSSIM cost in RDO can be calculated as simpler RDMSE cost with a new SSIM-related Lagrange multiplier. This not only reduces the computation burden of SSIM-based RDO, but also enables the R-DSSIM model to be uniformly used in OBA and RDO. Moreover, with the new SSIM-related Lagrange multiplier in hand, the joint relationship of R-DSSIM-λSSIM (the negative derivative of R-DSSIM) can be built, based on which the R-DSSIM model parameters can be calculated accurately. With accurate and unified R-DSSIM model, SSIM-based OBA and SSIM-based RDO are unified together in our scheme, called SOSR. Compared with the HEVC reference encoder HM16.20, SOSR saves 4%, 10%, and 14% bitrate under the same SSIM in all-intra, hierarchical and non-hierarchical low-delay-B configurations, which is superior to other state-of-the-art schemes. An improved version of this manuscript has been accepted by IEEE Transactions on Broadcasting DOI 10.1109/TBC.2021.3068871. The project page is located at http://***/web/xqmou/sosr. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal distortion

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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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End-To-End Graph-Based Deep Semi-Supervised Learning with Extended Graph Laplacian

End-To-End Graph-Based Deep Semi-Supervised Learning with Ex...

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Chinese Automation Congress (CAC)

作者： Zihao Wang Enmei Tu Meng Zhou Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The process of labeling samples costs time and resources but unlabeled samples are easier to obtain. Recently, graph-based deep semi-supervised learning (GDSSL) training a deep network using a small number of labeled samples and the abundant unlabeled samples has been demonstrated to be promising on image classification tasks. These methods construct a graph to represent the structure of the input data (or hidden features). The successes of these GDSSL algorithms depend upon the structure of the similarity graph. However, existing GDSSL approaches construct the graph using predefined rules (such as knn graph) or fixed similarity measures (such as Gaussian kernel), which may limit the potential of GDSSL. In this paper, we move further in this direction to propose a novel end-to-end GDSSL approach which fully optimizes the whole graph without such limitations. To this end, we concatenate two neural networks (feature network and similarity network) together to learn the categorical label and semantic similarity, respectively, and train the networks with a new regularization term, the extended graph Laplacian, to minimize a unified objective function. Extensive experiments on several benchmark datasets demonstrate that our approach could outperform existing approaches on image classification. Furthermore, as a side-product, the similarity network could give faithful semantic similarity measure of samples, which is not possessed by other GDSSL approaches.

关键词： Semantics Training Laplace equations Semisupervised learning Task analysis Neural networks Linear programming

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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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Hierarchical correlation stereo matching network 2nd

Hierarchical correlation stereo matching network

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2nd Chinese Conference on pattern recognition and Computer Vision, PRCV 2019

作者： Chen, Xuliang Zhou, Yue Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783030317256

Recently, stereo matching from a pair of rectified images has been cast as a supervised learning task using the powerful representation of convolutional neural networks. However, existing methods only utilize last feature maps output from Siamese Networks to compute similarity measurement, which are lack of multi-levels similarity information to construct an informative cost volume. To solve this problem, we propose a hierarchical correlation operation to compute similarity of stereo pairs at multiple levels. In addition, to yield accurate disparity in illposed region, we propose a stacked hourglass feature network with dense connections to effectively incorporate context information. Then, hybrid matching cost volume is built leveraging hierarchical correlation features and concatenation features of left and right. 3D CNN encoder-decoder architecture is utilized to regularize the cost volume and regress disparity. Experiments demonstrate that our network achieves competitive performance with state-of-the-art methods on Scene Flow, KITTI 2012, and KITTI 2015 datasets. © Springer Nature Switzerland AG 2019.

关键词： Neural networks

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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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An Self-supervised Learning & Self-attention Based Method for Defects Classification on PCB Surface images

An Self-supervised Learning & Self-attention Based Method fo...

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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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