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arXiv 2018年

作者： Li, Hui Wu, Xiao-Jun Kittler, Josef Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China CVSSP University of Surrey GuildfordGU2 7XH United Kingdom

In recent years, deep learning has become a very active research tool which is used in many image processing fields. In this paper, we propose an effective image fusion method using a deep learning framework to generate a single image which contains all the features from infrared and visible images. First, the source images are decomposed into base parts and detail content. Then the base parts are fused by weighted-averaging. For the detail content, we use a deep learning network to extract multi-layer features. Using these features, we use l1-norm and weighted-average strategy to generate several candidates of the fused detail content. Once we get these candidates, the max selection strategy is used to get the final fused detail content. Finally, the fused image will be reconstructed by combining the fused base part and the detail content. The experimental results demonstrate that our proposed method achieves state-of-the-art performance in both objective assessment and visual quality. The Code of our fusion method is available at https://***/hli1221/imagefusion_deeplearning. Copyright © 2018, The Authors. All rights reserved.

关键词： Image fusion

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Infrared and visible image fusion with ResNet and zero-phase component analysis

arXiv

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arXiv 2018年

作者： Li, Hui Wu, Xiao-Jun Durrani, Tariq S. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Department of Electronic and Electrical Engineering University of Strathclyde GlasgowG1 1XW United Kingdom

Feature extraction and processing are key tasks in the Image fusion algo- rithm, while most of deep learning-based methods use deep features directly without feature processing. This leads to the fusion performance degradation in some cases. To solve this drawback, in this paper, a novel fusion frame- work based on deep features and zero-phase component analysis (ZCA) is pro- posed. Firstly, the residual network (ResNet) is used to extract deep features from source images. Then ZCA and l1-norm are utilized to normalize the deep features and obtain initial weight maps. The final weight maps are obtained by employing a soft-max operation in association with the initial weight maps. Finally, the fused image is reconstructed using a weighted-averaging strategy. Compared with the existing fusion methods, experimental results demonstrate that the proposed algorithm achieves better performance in both objective assessment and visual quality. The code of our fusion algorithm is available at https://***/hli1221/imagefusion-resnet50. Copyright © 2018, The Authors. All rights reserved.

关键词： Infrared imaging

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Pose-invariant 3D face reconstruction

arXiv

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arXiv 2018年

作者： Jiang, Lei Wu, Xiao-Jun Kittler, Josef School of IoT Engineering Jiangnan University Wuxi214122 China University of Surry GuildfordGU2 7XH United Kingdom Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

3D face reconstruction is an important task in the field of computer vision. Although 3D face reconstruction has being developing rapidly in recent years, it is still a challenge for face reconstruction under large pose. That is because much of the information about a face in a large pose will be unknowable. In order to address this issue, this paper proposes a novel 3D face reconstruction algorithm (PIFR) based on 3D Morphable Model (3DMM). After input a single face image, it generates a frontal image by normalizing the image. Then we set weighted sum of the 3D parameters of the two images. Our method solves the problem of face reconstruction of a single image of a traditional method in a large pose, works on arbitrary Pose and Expressions, greatly improves the accuracy of reconstruction. Experiments on the challenging AFW, LFPW and AFLW database show that our algorithm significantly improves the accuracy of 3D face reconstruction even under extreme poses (±90 yaw angles). Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

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A localization method avoiding flip ambiguities for micro-UAVs with bounded distance measurement errors

arXiv

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arXiv 2018年

作者： Guo, Qingbei Zhang, Yuan Lloret, Jaime Kantarci, Burak Seah, Winston K.G. Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan Shandong China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Integrated Management Coastal Research Institute Universidad Politecnica de Valencia Spain School of Electrical Engineering and Computer Science University of Ottawa Canada School of Engineering and Computer Science Victoria University of Wellington New Zealand

Localization is a fundamental function in cooperative control of micro unmanned aerial vehicles (UAVs), but is easily affected by flip ambiguities because of measurement errors and flying motions. This study proposes a localization method that can avoid the occurrence of flip ambiguities in bounded distance measurement errors and constrained flying motions;to demonstrate its efficacy, the method is implemented on bilateration and trilateration. For bilateration, an improved bi-boundary model based on the unit disk graph model is created to compensate for the shortage of distance constraints, and two boundaries are estimated as the communication range constraint. The characteristic of the intersections of the communication range and distance constraints is studied to present a unique localization criterion which can avoid the occurrence of flip ambiguities. Similarly, for trilateration, another unique localization criterion for avoiding flip ambiguities is proposed according to the characteristic of the intersections of three distance constraints. The theoretical proof shows that these proposed criteria are correct. A localization algorithm is constructed based on these two criteria. The algorithm is validated using simulations for different scenarios and parameters, and the proposed method is shown to provide excellent localization performance in terms of average estimated error. Our code can be found at: https://***/QingbeiGuo/***. Copyright © 2018, The Authors. All rights reserved.

关键词： Measurement errors

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