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arXiv 2020年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Wang, Ruili Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom School of Logistics and Transportation Central South University of Forestry and Technology China School of Natural and Computational Sciences Massey University Auckland New Zealand

To read the paper please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods fail to accurately extract a road network that appears correct and complete. Moreover, they suffer from either insufficient training data or high costs of manual annotation. To address these problems, we introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. We incorporate a feature pyramid network into generative adversarial networks to minimize the difference between the source and target domains. A generator is learned to produce quality synthetic images, and the discriminator attempts to distinguish them. We also propose a feature pyramid network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales’ objects. Indeed, a novel scale-wise architecture is introduced to learn from the multi-level feature maps and improve the semantics of the features. For optimization, the model is trained by a joint reconstruction loss function, which minimizes the difference between the fake images and the real ones. A wide range of experiments on three datasets prove the superior performance of the proposed approach in terms of accuracy and efficiency. In particular, our model achieves state-of-the-art 78.86 IOU on the Massachusetts dataset with 1..89M parameters and 86.78B FLOPs, with 4× fewer FLOPs but higher accuracy (+3.47% IOU) than the top performer among state-of-the-art approaches used in the evaluation. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A novel deep structure U-net for sea-land segmentation in remote sensing images

arXiv

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arXiv 2020年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Wang, Ruili Zhou, Huiyu Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China School of Logistics and Transportation Central South University of Forestry and Technology China School of Natural and Computational Sciences Massey University Auckland New Zealand Department of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sea–land segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and lands. Although several Convolutional Neural Networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is a combination of both downsampling and upsampling paths to achieve satisfactory results. In each down- and up-sampling path, in addition to the convolution layers, several densely connected residual network blocks are proposed to systematically aggregate multi-scale contextual information. Each dense network block contains multilevel convolution layers, short-range connections and an identity mapping connection which facilitates features reuse in the network and makes full use of the hierarchical features from the original images. These proposed blocks have a certain number of connections that are designed with shorter distance backpropagation between the layers and can significantly improve segmentation results whilst minimizing computational costs. We have performed extensive experiments on two real datasets Google-Earth and ISPRS and compare the proposed RDU-Net against several variations of Dense Networks. The experimental results show that RDU-Net outperforms the other state-of-the-art approaches on the sea-land segmentation tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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COVID-MTL: Multitask learning with shift3D and random-weighted loss for automated diagnosis and severity assessment of COVID-1.

arXiv

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arXiv 2020年

作者： Bao, Guoqing Chen, Huai Liu, Tongliang Gong, Guanzhong Yin, Yong Wang, Lisheng Wang, Xiuying School of Computer Science The University of Sydney J12/1 Cleveland St Darlington SydneyNSW2008 Australia Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Radiation Oncology Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan250117 China

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-1.. Radiology and nucleic acid test (NAT) are complementary COVID-1. diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-1.. COVID-MTL learns different COVID-1. tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance;our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions;thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-1. against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 ± 0.020 and 0.81. ± 0.021.(with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P © 2020, CC BY.

关键词： Radiology

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Rotation equivariant feature image pyramid network for object detection in optical remote sensing imagery

arXiv

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arXiv 2021年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Chanussot, Jocelyn Zhou, Huiyu Yang, Jie The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China The GIPSA-Lab Université Grenoble Alpes CNRS Grenoble INP Grenoble38000 France The Faculty of Electrical and Computer Engineering University of Iceland Reykjavik101 Iceland The School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

To read the paper please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. Detection of objects is extremely important in various aerial vision-based applications. Over the last few years, the methods based on convolution neural networks have made substantial progress. However, because of the large variety of object scales, densities, and arbitrary orientations, the current detectors struggle with the extraction of semantically strong features for small-scale objects by a predefined convolution kernel. To address this problem, we propose the rotation equivariant feature image pyramid network (REFIPN), an image pyramid network based on rotation equivariance convolution. The proposed model adopts single-shot detector in parallel with a lightweight image pyramid module to extract representative features and generate regions of interest in an optimization approach. The proposed network extracts feature in a wide range of scales and orientations by using novel convolution filters. These features are used to generate vector fields and determine the weight and angle of the highest-scoring orientation for all spatial locations on an image. By this approach, the performance for small-sized object detection is enhanced without sacrificing the performance for large-sized object detection. The performance of the proposed model is validated on two commonly used aerial benchmarks and the results show our proposed model can achieve state-of-the-art performance with satisfactory efficiency. Copyright © 2021. The Authors. All rights reserved.

关键词： Object detection

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Sparse generalized canonical correlation analysis: Distributed alternating iteration based approach

arXiv

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arXiv 2020年

作者： Cai, Jia Lv, Kexin Huo, Junyi Huang, Xiaolin Yang, Jie School of Statistics and Mathematics Guangdong University of Finance & Economics Big Data and Educational Statistics Application Laboratory 21 Chisha Road Guangzhou Guangdong510320 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China School of Electronics and Computer Science University of Southampton University Road SouthamptonSO17 1BJ United Kingdom

Sparse canonical correlation analysis (CCA) is a useful statistical tool to detect latent information with sparse structures. However, sparse CCA works only for two datasets, i.e., there are only two views or two distinct objects. To overcome this limitation, in this paper, we propose a sparse generalized canonical correlation analysis (GCCA), which could detect the latent relations of multiview data with sparse structures. Moreover, the introduced sparsity could be considered as Laplace prior on the canonical variates. Specifically, we convert the GCCA into a linear system of equations and impose 1.minimization penalty for sparsity pursuit. This results in a nonconvex problem on Stiefel manifold, which is difficult to solve. Motivated by Boyd’s consensus problem, an algorithm based on distributed alternating iteration approach is developed and theoretical consistency analysis is investigated elaborately under mild conditions. Experiments on several synthetic and real world datasets demonstrate the effectiveness of the proposed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistical mechanics

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基于字典学习的图像去模糊研究

基于字典学习的图像去模糊研究

引用

第九届中国通信学会学术年会

作者： ZOU Jian-cheng 邹建成 CHE Dong-juan 车冬娟 Institute of Image Processing and Pattern Recognition North China University of TechnologyBeijing 1 北方工业大学图像处理与模式识别研究所北京 100144

图像在获取、传输等过程中会受到干扰，造成图像模糊不清。传统去模糊方法需要根据模糊原因估计出相应的点扩散函数(PSF)，然后进行反卷积估计出原始图像。本文借鉴K-SVD算法，利用对模糊图像进行训练得到自适应字典;并利用计算量相对... 详细信息

图像在获取、传输等过程中会受到干扰，造成图像模糊不清。传统去模糊方法需要根据模糊原因估计出相应的点扩散函数(PSF)，然后进行反卷积估计出原始图像。本文借鉴K-SVD算法，利用对模糊图像进行训练得到自适应字典;并利用计算量相对较小且收缩速度较快的PCD算法来求解稀疏解。实验结果表明，相对传统图像去模糊算法和固定字典算法，自适应的字典学习算法能达到更好的去模糊效果。

关键词：图像处理去模糊方法字典学习算法研究

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