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2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

作者： Shuyuan Yang Yueyuan Zhang Yue Han Key Lab of Intelligent Perception and Image Understanding of Ministry of Education Department of Electrical Engineering Xidian University Xi’an China 710071

ISBN: (纸本)9781424484447

Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. In this paper, we proposed a speckle reduction technique based on sparse representation and dictionary learning. Firstly, an adaptive dictionary was learned by performing KSVD algorithm through a large amount of training patches extracted from the noisy SAR image. Considering the inaccurate recovery of point targets which is brought by the inadequate number of training samples, we employed a point target enhancing scheme to highlight the important point targets in the SAR image. Some experiments were conducted on real SAR images, and the results shows that our proposed algorithm can effectively reduce the speckle noise as well as preserve details. Some comparisons are made to prove its superiority to the available algorithms.

关键词： Speckle Reduction dictionary learning sparse representation point target enhancing

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Behavior mimics distribution: Combining individual and group behaviors for federated learning

arXiv

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arXiv 2021年

作者： Huang, Hua Shang, Fanhua Liu, Yuanyuan Liu, Hongying Key Lab of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China Peng Cheng Lab Shenzhen China

Federated Learning (FL) has become an active and promising distributed machine learning paradigm. As a result of statistical heterogeneity, recent studies clearly show that the performance of popular FL methods (e.g., FedAvg) deteriorates dramatically due to the client drift caused by local updates. This paper proposes a novel Federated Learning algorithm (called IGFL), which leverages both Individual and Group behaviors to mimic distribution, thereby improving the ability to deal with heterogeneity. Unlike existing FL methods, our IGFL can be applied to both client and server optimization. As a by-product, we propose a new attention-based federated learning in the server optimization of IGFL. To the best of our knowledge, this is the first time to incorporate attention mechanisms into federated optimization. We conduct extensive experiments and show that IGFL can significantly improve the performance of existing federated learning methods. Especially when the distributions of data among individuals are diverse, IGFL can improve the classification accuracy by about 13% compared with prior baselines. © 2021, CC BY.

关键词： Learning systems

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image DENOISING BASED ON TRANSLATION INVARIANT DIRECTIONAL LIFTING

IMAGE DENOISING BASED ON TRANSLATION INVARIANT DIRECTIONAL L...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Xiaotian Wang Guangming Shi Lili Liang Intelligent Perception and Image Understanding Key Lab of Ministry of Education Xidian University Xi'an Shaanxi 710071 P. R. China

ISBN: (纸本)9781424442959

Adaptive Directional Lifting (ADL) has been successfully implemented in image compression and denoising due to the feature of simple structure and flexible directional selectivity. However, image denoising by means of ADL introduces many visual artifacts caused by Gibbs phenomena due to the lack of translation invariance. In this paper, we propose a translation invariant directional lifting (TI-DL) by employing the cycle-spinning based technique to reduce artifacts in denoising results. Moreover, the inefficiency and high computational complexity of the orientation estimation technique in ADL strongly influences the performance. In order to achieve better denoising results, in this paper, 2-D Gabor filters are adopted for orientation estimation to achieve better orientation estimation results with lower complexity. Experimental results demonstrate that the proposed method achieves state-of-art denoising performance in terms of both objective (PSNR) and subjective (SSIM) evaluation.

关键词： adaptive directional lifting image denoising translation invariance 2-D Gabor filters

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Polarimetric convolutional network for PolSAR image classification

arXiv

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arXiv 2018年

作者： Liu, Xu Jiao, Licheng Tang, Xu Sun, Qigong D., Zhang Key Lab. of Intelligent Perception and Image Understanding of the Ministry of Education of China International Research Center of Intelligent Perception and Computation School of Artificial Intelligence Xidian University Xi'an Shaanxi Province710071 China

The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix are used as the main research object to extract features. In this paper, we focus on the original polarimetric scattering matrix and propose a polarimetric scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on convolution network to combine this feature. Based on polarimetric scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for polarimetric scattering coding is available at https://***/liuxuvip/Polarimetric-Scattering-Coding. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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Learning a Dynamic High-Resolution Network for Multi-Scale Pedestrian Detection

Learning a Dynamic High-Resolution Network for Multi-Scale P...

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International Conference on Pattern Recognition

作者： Mengyuan Ding Shanshan Zhang Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security China

ISBN: (纸本)9781728188089;9781728188096

Pedestrian detection is a canonical instance of object detection in computer vision. In practice, scale variation is one of the key challenges, resulting in unbalanced performance across different scales. Recently, the High-Resolution Network (HRNet) has become popular because high-resolution feature representations are more friendly to small objects. However, when we apply HRNet to pedestrian detection, we observe that it improves for small pedestrians on one hand, but hurts the performance for larger ones on the other hand. To overcome this problem, we propose a learnable Dynamic HRNet (DHRNet) aiming to generate different network paths adaptive to different scales. Specifically, we construct a parallel multi-branch architecture and add a soft conditional gate module allowing for dynamic feature fusion. Both branches share all the same parameters except the soft gate module. Experimental results on CityPersons and Caltech benchmarks indicate that our proposed dynamic HRNet is more capable of dealing with pedestrians of various scales, and thus improves the performance across different scales consistently.

关键词： Computer vision Adaptive systems Object detection Computer architecture Logic gates Benchmark testing Pattern recognition

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Multiple functional connectivity networks fusion for schizophrenia diagnosis

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Medical & biological engineering & computing 2020年第8期58卷 1779-1790页

作者： Hongliang Zou Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China. hongliangzou@***. PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China.

Accurate diagnosis of schizophrenia is of great importance to patients and clinicians. Recent studies have found that different frequency bands contain complementary information for diagnosis and prognosis. However, conventional multiple frequency functional connectivity (FC) networks using Pearson's correlation coefficient (PCC) are usually based on pairwise correlations among different brain regions on single frequency band, while ignoring the interactions between regions in different frequency bands, the relationship among different networks, and the nonlinear properties of blood-oxygen-level-dependent (BOLD) signal. To take into account these relationships, we propose in this study a multiple networks fusion method for schizophrenia diagnosis. Specifically, we first construct FC networks within the same and across frequency from the resting-state functional magnetic resonance imaging (rs-fMRI) time series by using extended maximal information coefficient (eMIC) based on four frequency bands: slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), and slow-2 (0.198-0.25 Hz). Then, these networks are combined nonlinearly through network fusion, which generates a unified network for each subject. Features extracted from the unified network are used for final classification. Experimental results demonstrated that the interaction between distinct brain regions across different frequency bands can significantly improve the classification performance, comparing with conventional FC analysis based on specific or entire low-frequency band. The promising results suggest that our proposed framework would be a useful tool in computer-aided diagnosis of schizophrenia. Graphical abstract The flowchart of proposed classification framework.

关键词： Across-frequency Functional connectivity Schizophrenia diagnosis eMIC rs-fMRI

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Large motion video super-resolution with dual subnet and multi-stage communicated upsampling

arXiv

引用

arXiv 2021年

作者： Liu, Hongying Zhao, Peng Ruan, Zhubo Shang, Fanhua Liu, Yuanyuan Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China Peng Cheng Lab Shenzhen China

Video super-resolution (VSR) aims at restoring a video in low-resolution (LR) and improving it to higher-resolution (HR). Due to the characteristics of video tasks, it is very important that motion information among frames should be well concerned, summarized and utilized for guidance in a VSR algorithm. Especially, when a video contains large motion, conventional methods easily bring incoherent results or artifacts. In this paper, we propose a novel deep neural network with Dual Subnet and Multi-stage Communicated Upsampling (DSMC) for super-resolution of videos with large motion. We design a new module named U-shaped residual dense network with 3D convolution (U3D-RDN) for fine implicit motion estimation and motion compensation (MEMC) as well as coarse spatial feature extraction. And we present a new Multi-Stage Communicated Upsampling (MSCU) module to make full use of the intermediate results of upsampling for guiding the VSR. Moreover, a novel dual subnet is devised to aid the training of our DSMC, whose dual loss helps to reduce the solution space as well as enhance the generalization ability. Our experimental results confirm that our method achieves superior performance on videos with large motion compared to state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal sampling

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Nighttime Pedestrian Detection Based on Feature Attention and Transformation

Nighttime Pedestrian Detection Based on Feature Attention an...

引用

International Conference on Pattern Recognition

作者： Gang Li Shanshan Zhang Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security China

Pedestrian detection at nighttime is an important yet challenging task, which is fundamental for many practical applications, e.g. autonomous driving, video surveillance. To address this problem, in this work we start with some analysis, from which we find that the nighttime features have much more noise than that of daytime, resulting in low discrimination ability. Besides, we also observe some pedestrian examples are under adverse illumination conditions, and they can hardly provide sufficient information for accurate detection. Based on these findings, we propose the Feature Attention Module (FAM) and Feature Transformation Module (FTM) to enhance nighttime features. In FAM, guided by progressive segmentation supervision, hierarchical feature attention is produced to enhance multilevel features. On the other hand, FTM is introduced to enforce features from adverse illumination to approach that from better illumination. Based on feature attention and transformation (FAT) mechanism, a two-stage detector called FATNet is constructed for nighttime pedestrian detection. We conduct extensive experiments on nighttime datasets of EuroCity Persons (Night) and NightOwls to demonstrate the effectiveness of our method. On both datasets, our method achieves significant improvements to the baseline and also outperforms state-of-the-art detectors.

关键词： Lighting Detectors Feature extraction Video surveillance Pattern recognition Fats Noise measurement

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Occluded Pedestrian Detection Through Guided Attention in CNNs 31

Occluded Pedestrian Detection Through Guided Attention in CN...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shanshan Zhang Jian Yang Bernt Schiele Ministry of Education Key Lab of Intelligent Perception and Systems for High-Dimensional Information Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology China Saarland Informatics Campus Max Planck Institute for Informatics Germany

ISBN: (纸本)9781538664216;9781538664209

Pedestrian detection has progressed significantly in the last years. However, occluded people are notoriously hard to detect, as their appearance varies substantially depending on a wide range of occlusion patterns. In this paper, we aim to propose a simple and compact method based on the FasterRCNN architecture for occluded pedestrian detection. We start with interpreting CNN channel features of a pedestrian detector, and we find that different channels activate responses for different body parts respectively. These findings motivate us to employ an attention mechanism across channels to represent various occlusion patterns in one single model, as each occlusion pattern can be formulated as some specific combination of body parts. Therefore, an attention network with self or external guidances is proposed as an add-on to the baseline FasterRCNN detector. When evaluating on the heavy occlusion subset, we achieve a significant improvement of 8pp to the baseline FasterRCNN detector on CityPersons and on Caltech we outperform the state-of-the-art method by 4pp.

关键词： Detectors Feature extraction Computer architecture Object detection Training Body regions Correlation

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Unsupervised Detailed Human Shape Estimation from Multi-view Color images 11th

Unsupervised Detailed Human Shape Estimation from Multi-view...

引用

11th International Conference on image and Graphics, ICIG 2021

作者： Zheng, Huayu Wang, Kangkan Li, Wei Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Lab for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030873578

This paper presents a novel framework to estimate detailed human body shape from color images in an unsupervised manner. It is a challenging task due to factors such as variations in human shapes, occlusion, and cloth details. The existing methods are mainly supervised and require a large number of ground truth real training data which is usually hard to obtain. To solve this problem, we propose an unsupervised detailed human shape estimation method from multi-view color images. Specifically, we first predict the depth map for the source view through robust photometric consistency with different views. Then, we predict the initial SMPL model from the color image and refine it by an iterative error feedback regressor based on point clouds of the predicted depth map. Finally, the refined SMPL model is deformed to fit the details (i.e., clothes and faces) on the point clouds to recover the detailed human shapes which are represented by adding a set of offsets to the SMPL model. The experimental results on different dataset demonstrate that our method outperforms the state-of-the-art methods and achieves higher reconstruction accuracy. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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