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- 2018
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- Optoelectronic Information Science and Engineering
- Wuhan University
- Central South University
- Hubei University
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[IEEE 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Changsha, China (2018.9.21-2018.9.23)] 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Detection of Diabetic Retinopathy Images Using a Fully Convolutional Neural Network
摘要： The paper discusses the development and application of a convolutional neural network (CNN) model for digital image processing in the context of data science and business analytics. It focuses on improving the accuracy and efficiency of image classification tasks.
关键词： Image Classification,Digital Image Processing,Business Analytics,Data Science,Convolutional Neural Network
更新于2025-09-23 15:22:29
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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 - Desnet: Deep Residual Networks for Descalloping of Scansar Images
摘要： Scalloping is one of the critical problems in ScanSAR images. It not only affects image visualization, but also influences the quantitative applications such as surface wind and wave retrievals in the ocean area. The existing method of descalloping needs artificial parameter setting and lacks generality in the image domain. A novel deep neural network based on residual learning for descalloping of ScanSAR images is proposed in this paper. The proposed method can eliminate scalloping patterns and has strong adaptive ability, which can handle inhomogeneous scalloping patterns and different scenarios. Experiments on GF-3 ScanSAR images verify the good performance of this method. The code for our models is available online.
关键词： synthetic aperture radar (SAR),deep neural network,scalloping patterns,ScanSAR,Residual learning
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Robust Estimation of Product Amount on Store Shelves from a Surveillance Camera for Improving On-Shelf Availability
摘要： This paper proposes a method to robustly estimate product amount on store shelves from a surveillance camera for improving on-shelf availability. We focus on changes of products on the shelves such as “product taken (decrease)” and “product replenished/returned (increase)”, and compute product amount by accurately accumulating them. The proposed method first detects change regions of products on the shelves in an image using background subtraction followed by moving object removal. The detected change regions are then classified into several classes representing the actual changes on the shelves such as “product taken” by using convolutional neural networks. Finally, the changes of products on the shelves are accumulated using classification results, and product amount on the shelves visible in the image is computed as on-shelf availability. Experimental results using two videos captured in a real store show that our method achieves success rate of 89.6% for on-shelf availability when an error margin is within one product. With high accuracy, store clerks can keep high on-shelf availability, enabling the improvement of business profit in retail stores.
关键词： product amount,image processing,on-shelf availability,convolutional neural network,surveillance camera,retail,background subtraction
更新于2025-09-23 15:22:29
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - DEMON Spectrum Extraction Method Using Empirical Mode Decomposition
摘要： The noise radiated by a ship is modulated at a rate dictated by some parameters of the propeller and engine (number of blades, rotational speed). Evaluation of that modulation provides information on the ship, such as the shaft rotation frequency, that can be used for ship classification. The method for estimation of the envelope modulation is known as DEMON (Detection of Envelope Modulation on Noise). Traditionally, the ship noise is bandpass filtered in different frequency bands before the envelope analysis. The bandwidth and the number of the bandpass filters is not known. In this paper a new DEMON spectrum extraction method is proposed using empirical mode decomposition (EMD), in which the band number and width are automatically determined. In performance test, a feedforward neural network is used for 5 kinds ship noise classification, and the percentage of correct classification reaches 91.6%.
关键词： DEMON,empirical mode decomposition,Detection of envelope modulation on noise,feedforward neural network,EMD
更新于2025-09-23 15:22:29
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[IEEE 2018 China International SAR Symposium (CISS) - Shanghai (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - Reconstruction Full-Pol SAR Data from Single-Pol SAR Image Using Deep Neural Network
摘要： Compared with single channel polarimetric (single-pol) SAR image, full polarimetric (full-pol) data convey richer information, but with compromises on higher system complexity and lower resolution or swath. In order to balance these factors, a deep neural networks based method is proposed to recover full-pol data from single-pol data in this paper. It consists of two parts: a feature extractor network is applied first to extract hierarchical multi-scale spatial features, followed by a feature translator network to predict polarimetric features with which full-pol SAR data can be recovered. Both qualitative and quantitative results show that the recovered full-pol SAR data agrees well with the real full-pol data. No prior information is assumed for scatterer media, and the framework can be easily expanded to recovery full-pol data from non-full-pol data. Traditional PolSAR applications such as model-based decomposition and unsupervised classification can now be applied directly to recovered full-pol SAR image to interpret the physical scattering mechanism.
关键词： synthetic aperture radar (SAR),deep neural network (DNN),polarimetric reconstruction
更新于2025-09-23 15:22:29
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Visualizing Interactions of Circulating Tumor Cell and Dendritic Cell in the Blood Circulation Using In Vivo Imaging Flow Cytometry
摘要： Objective: Visualizing cell interactions in blood circulation is of great importance in studies of anticancer immunotherapy or drugs. However, the lack of a suitable imaging system hampers progress in this field. Methods: In this work, we built a dual-channel in vivo imaging flow cytometer to visualize the interactions of circulating tumor cells (CTCs) and dendritic cells (DCs) simultaneously in the bloodstream. Two artificial neural networks were trained to identify blood vessels and cells in the acquired images. Results and Conclusion: Using this technique, single CTCs and CTC clusters were readily distinguished by their morphology. Interactions of CTCs and DCs were identified, while their moving velocities were analyzed. The CTC-DC clusters moved at a slower velocity than that of single CTCs or DCs. This may provide new insights into tumor metastasis and blood rheology. Significance: This in vivo imaging flow cytometry system holds great potential for assessing the efficiency of targeting CTCs with anticancer immune cells or drugs.
关键词： Cell Interaction,Circulating Tumor Cell,In Vivo Imaging Flow Cytometry,Artificial Neural Network,Dendritic Cell
更新于2025-09-23 15:22:29
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Multi-level Features Convolutional Neural Network for Multi-focus Image Fusion
摘要： Multi-focus image fusion is an important technique that aims to generate a single clean image by fusing multiple input images. In this paper, we propose a novel multi-level features convolutional neural network (MLFCNN) architecture for image fusion. In the MLFCNN model, all features learned from previous layers are passed to the subsequent layer. Inside every path between the previous layer and the subsequent layer, we add a 1x1 convolution module to reduce the redundancy. In our method, the source images first are fed to our pre-trained MLFCNN model to obtain the initial focus map. Then, the initial focus map is performed by morphological opening and closing operations and followed by a Gaussian filter to obtain the final decision map. Finally, the fused all-in-focus image is generated based on a weighted-sum strategy with the decision map. The experimental results demonstrate that the proposed method outperforms some state-of-the-art image fusion algorithms in terms of both qualitative and objective evaluations.
关键词： convolutional neural network,decision map,multi-focus image fusion,multi-level features
更新于2025-09-23 15:22:29
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A Novel Patch Variance Biased Convolution Neural Network for No-Reference Image Quality Assessment
摘要： Deep Convolutional Neural Networks (CNNs) have been successfully applied on no-reference image quality assessment (NR-IQA) with respect to human perception. Most of these methods deal with small image patches and use the average score of the test patches for predicting the whole image quality. We discovered that image patches from homogenous regions are unreliable for both neural network training and final image quality score estimation. In addition, image patches with complex structures have much higher chances to achieve better image quality prediction. Based on these findings, we enhanced the conventional CNN-based NR-IQA algorithm to avoid homogenous patches for the network training and quality score estimation. Moreover, we also use a variance-based weighting average to bias the final image quality score to the patches with complex structure. Experimental results show that this simple approach can achieve state-of-the-art performance as compared with well-known NR-IQA algorithms.
关键词： deep learning,no-reference image quality assessment,convolution neural network
更新于2025-09-23 15:22:29
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Robust Landmark Detection and Position Measurement Based on Monocular Vision for Autonomous Aerial Refueling of UAVs
摘要： In this paper, a position measurement system, including drogue's landmark detection and position computation for autonomous aerial refueling of unmanned aerial vehicles, is proposed. A multitask parallel deep convolution neural network (MPDCNN) is designed to detect the landmarks of the drogue target. In MPDCNN, two parallel convolution networks are used, and a fusion mechanism is proposed to accomplish the effective fusion of the drogue's two salient parts' landmark detection. Considering the drogue target's geometric constraints, a position measurement method based on monocular vision is proposed. An effective fusion strategy, which fuses the measurement results of drogue's different parts, is proposed to achieve robust position measurement. The error of landmark detection with the proposed method is 3.9%, and it is obviously lower than the errors of other methods. Experimental results on the two KUKA robots platform verify the effectiveness and robustness of the proposed position measurement system for aerial refueling.
关键词： landmark detection,multitask parallel deep convolution neural network (MPDCNN),monocular vision,position measurement,Aerial refueling
更新于2025-09-23 15:22:29
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[IEEE 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hangzhou (2018.10.18-2018.10.20)] 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Dynamic Hand Gesture Recognition Using FMCW Radar Sensor for Driving Assistance
摘要： Dynamic hand gesture recognition is very important for human-computer interaction. In vehicles, hand gesture recognition can be used as the driver's auxiliary system to achieve remote control of the instrument. To a certain extent, this system can avoid physical buttons and touch screens causing interference to the driver. In this paper, we describe a driver-assisted dynamic gesture recognition system to classify nine hand gestures based on micro-Doppler signatures obtained by 77GHz FMCW radar using a convolutional neural network (CNN). We further explore the changes in the accuracy of same gestures in a variety of experimental scenarios to help optimize the robustness of the system.
关键词： convolutional neural network,hand gesture recognition,driver assistance system,FMCW radar sensor
更新于2025-09-23 15:22:29