- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Ship Discrimination with Deep Convolutional Neural Networks in Sar Images
摘要： With the advantages of all-time, all-weather, and wide coverage, synthetic aperture radar (SAR) systems are widely used for ship detection to ensure marine surveillance. However, the azimuth ambiguity and buildings exhibit similar scattering mechanisms of ships, which cause false alarms in the detection of ships. To address this problem, self-designed deep convolutional neural networks with the capability to automatically learn discriminative features is applied in this paper. Two datasets, including one dataset reconstructed from IEEEDataPort SARSHIPDATA and the other constructed from 10 scenes of Sentinel-1 SAR images, are used to evaluate our approach. Experimental results reveal that our model achieves more than 95% classification accuracy on both datasets, demonstrating the effectiveness of our approach.
关键词： ship discrimination,Sentinel-1 images,synthetic aperture radar,deep convolutional neural networks
更新于2025-09-23 15:23:52
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DeeptransMap: a considerably deep transmission estimation network for single image dehazing
摘要： Due to the ill-posed phenomenon of the classical physical model, single image dehazing based on the model has been a challenging vision task. In recent years, applying machine learning techniques to estimate a critical parameter transmission has proven to be an effective solution to this issue. Accordingly, the robustness and accuracy of learning-based transmission estimation model is extremely important, since it does impact on the final dehazing effects. The state-of-the-art dehazing algorithms by this means generally use haze-relevant features as the single input to their transmission estimation models. However, the used haze-relevant features sometimes are not sufficient and reliable in holding real intrinsic information related to haze due to their two shortcomings and ultimately bring about their less effectiveness for some dehazing cases. Based on related efforts on representation learning and deep convolutional neural networks, in this paper, we seek to achieve the robustness and accuracy of transmission estimation model for bolstering the effectiveness of single image dehazing. Specifically, we propose a hybrid model combining unsupervised and supervised learning in a considerably deep neural networks architecture, in order to achieve accurate transmission map from a single image. Experimental results demonstrate that our work performs favorably against several state-of-the-art dehazing methods with the same estimated goal and keeps efficient in terms of the computational complexity of transmission estimation.
关键词： Feature learning,Deep convolutional neural networks (CNNs),Image dehazing,Transmission estimation
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Ship Detection Based on Deep Convolutional Neural Networks for Polsar Images
摘要： In this paper, we proposed a ship detection method based on deep convolutional neural networks for PolSAR images. The proposed ship detector firstly segments PolSAR images into sub-samples using a sliding window of fixed size to effectively extract translational-invariant spatial features. Further, the modified faster region based convolutional neural network (Faster-RCNN) method is utilized to realize ship detection for ships with different sizes and fusion the detection result. Finally, the proposed method was validated using real measured NASA/JPL AIRSAR datasets by comparing the performance with the modified constant false alarm rate (CFAR) detector. The comparison results demonstrate the validity and generality of the proposed detection algorithm.
关键词： Deep convolutional neural networks,polarimetric synthetic aperture radar (PolSAR),ship detection
更新于2025-09-23 15:21:21
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Study of Room Temperature Photoluminescence For 1-stage Co-Evaporated Ultra-Thin Cu(In,Ga)Se <sub/>2</sub> Solar Cells
摘要： In this paper, we investigate the feasibility of recognizing human hand gestures using micro-Doppler signatures measured by Doppler radar with a deep convolutional neural network (DCNN). Hand gesture recognition using radar can be applied to control electronic appliances. Compared with an optical recognition system, radar can work regardless of light conditions and it can be embedded in a case. We classify ten different hand gestures, with only micro-Doppler signatures on spectrograms without range information. The ten gestures, which included swiping from left to right, swiping from right to left, rotating clockwise, rotating counterclockwise, pushing, double pushing, holding, and double holding, were measured using Doppler radar and their spectrograms investigated. A DCNN was employed to classify the spectrograms, with 90% of the data utilized for training and the remaining 10% for validation. After ?ve-fold validation, the classi?cation accuracy of the proposed method was found to be 85.6%. With seven gestures, the accuracy increased to 93.1%.
关键词： Doppler radar,micro-Doppler signatures,Hand gesture,deep convolutional neural networks
更新于2025-09-23 15:19:57
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[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Robust CNN-based Gait Verification and Identification using Skeleton Gait Energy Image
摘要： As a kind of behavioral biometric feature, gait has been widely applied for human verification and identification. Approaches to gait recognition can be classified into two categories: model-free approaches and model-based approaches. Model-free approaches are sensitive to appearance changes. For model-based approaches, it is difficult to extract the reliable body models from gait sequences. In this paper, based on the robust skeleton points produced from a two-branch multi-stage CNN network, a novel model-based feature, Skeleton Gait Energy Image (SGEI), has been proposed. Relevant experimental performances indicate that SGEI is more robust to the cloth changes. Another contribution is that two different CNN-based architectures have been separately proposed for gait verification and gait identification. Both these two architectures have been evaluated on the datasets. They have presented satisfying performances and increased the robustness for gait recognition in the unconstrained environments with view variances and cloth variances.
关键词： Gait Identification,Gait Verification,Deep Convolutional Neural Networks,Skeleton Gait Energy Image
更新于2025-09-19 17:15:36
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Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks
摘要： Light ?eld imaging has recently known a regain of interest due to the availability of practical light ?eld capturing systems that offer a wide range of applications in the ?eld of computer vision. However, capturing high-resolution light ?elds remains technologically challenging since the increase in angular resolution is often accompanied by a signi?cant reduction in spatial resolution. This paper describes a learning-based spatial light ?eld super-resolution method that allows the restoration of the entire light ?eld with consistency across all angular views. The algorithm ?rst uses optical ?ow to align the light ?eld and then reduces its angular dimension using low-rank approximation. We then consider the linearly independent columns of the resulting low-rank model as an embedding, which is restored using a deep convolutional neural network (DCNN). The super-resolved embedding is then used to reconstruct the remaining views. The original disparities are restored using inverse warping where missing pixels are approximated using a novel light ?eld inpainting algorithm. Experimental results show that the proposed method outperforms existing light ?eld super-resolution algorithms, achieving PSNR gains of 0.23 dB over the second best performing method. The performance is shown to be further improved using iterative back-projection as a post-processing step.
关键词： Low-Rank Matrix Approximation,Super-Resolution,Light Field,Deep Convolutional Neural Networks
更新于2025-09-19 17:15:36
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[IEEE 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - HangZhou, China (2018.9.5-2018.9.7)] 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - RCS Reduction of 2??2 Microstrip Antenna Array Using All Dielectric Metasurface
摘要： In this paper, we investigate the feasibility of recognizing human hand gestures using micro-Doppler signatures measured by Doppler radar with a deep convolutional neural network (DCNN). Hand gesture recognition using radar can be applied to control electronic appliances. Compared with an optical recognition system, radar can work regardless of light conditions and it can be embedded in a case. We classify ten different hand gestures, with only micro-Doppler signatures on spectrograms without range information. The ten gestures, which included swiping from left to right, swiping from right to left, rotating clockwise, rotating counterclockwise, pushing, double pushing, holding, and double holding, were measured using Doppler radar and their spectrograms investigated. A DCNN was employed to classify the spectrograms, with 90% of the data utilized for training and the remaining 10% for validation. After ?ve-fold validation, the classi?cation accuracy of the proposed method was found to be 85.6%. With seven gestures, the accuracy increased to 93.1%.
关键词： Doppler radar,micro-Doppler signatures,Hand gesture,deep convolutional neural networks
更新于2025-09-16 10:30:52
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[IEEE 2018 IEEE Advanced Accelerator Concepts Workshop (AAC) - Breckenridge, CO, USA (2018.8.12-2018.8.17)] 2018 IEEE Advanced Accelerator Concepts Workshop (AAC) - Compression of Terawatt Long-Wavelength Laser Pulses Through Backward Raman Amplification
摘要： JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-rates. Soft decoding is an effective solution to improve the quality of compressed images without changing codec or introducing extra coding bits. Inspired by the excellent performance of the deep convolutional neural networks (CNNs) on both low-level and high-level computer vision problems, we develop a dual pixel-wavelet domain deep CNNs-based soft decoding network for JPEG-compressed images, namely DPW-SDNet. The pixel domain deep network takes the four downsampled versions of the compressed image to form a 4-channel input and outputs a pixel domain prediction, while the wavelet domain deep network uses the 1-level discrete wavelet transformation (DWT) coefficients to form a 4-channel input to produce a DWT domain prediction. The pixel domain and wavelet domain estimates are combined to generate the final soft decoded result. Experimental results demonstrate the superiority of the proposed DPW-SDNet over several state-of-the-art compression artifacts reduction algorithms.
关键词： JPEG,soft decoding,deep convolutional neural networks,compression artifacts,DPW-SDNet
更新于2025-09-11 14:15:04
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Global Context Encoding for Salient Objects Detection
摘要： Deep convolutional neural networks (CNNs) have gained their reputation for the success in various tasks in computer vision, including salient objects detection. However, it remains a challenge that the CNNs have repeated downsample operators and always create low-resolution predictions, which tend to loss details and finer structure of images. To detect and segment the salient objects well, it is also necessary to merge high-level semantic information and low-level fine details simultaneously. Thus, we propose a novel network structure with stage-wise refinement sub-structures. In addition, we exploit the essence of salient objects detection by encoding the global image context in a specifically designed module, which is applied to every stage of the refinement structure. So the coarse saliency map generated from the base CNN can be refined with low-level feature and global context information step-by-step. Experimental results have demonstrated that the proposed method outperforms the state-of-the-art approaches on four benchmark datasets.
关键词： stage-wise refinement,global context encoding,salient objects detection,deep convolutional neural networks
更新于2025-09-10 09:29:36
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Intraspectrum Discrimination and Interspectrum Correlation Analysis Deep Network for Multispectral Face Recognition
摘要： Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods.
关键词： multispectral face recognition,spectra selection,useful interspectrum correlation information exploration,Deep convolutional neural networks (DCNNs),intraspectrum discriminant information exploration
更新于2025-09-10 09:29:36