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- 2018
- Conditional Random Fields (CRF)
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- Airborne hyperspectral
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- Wuhan University
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[IEEE 2018 Asia Communications and Photonics Conference (ACP) - Hangzhou, China (2018.10.26-2018.10.29)] 2018 Asia Communications and Photonics Conference (ACP) - Recursive Neural Network Based RRH to BBU Resource Allocation in 5G Fronthaul Network
摘要： A recursive neural network based BBU pool resource allocation scheme in C-RAN is proposed. Simulation results indicate the proposed scheme achieves lower power consumption and blocking rate with higher total throughput compared with traditional network.
关键词： Resource allocation,5G fronthaul network,Recursive neural network,C-RAN
更新于2025-09-19 17:15:36
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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) - Adversarial Context Aggregation Network for Low-Light Image Enhancement
摘要： Image captured in the low-light environments usually suffers from the low dynamic ranges and noise which degrade the quality of the image. Recently, convolutional neural network (CNN) has been employed for low-light image enhancement to simultaneously perform the brightness enhancement and noise removal. Although conventional CNN based techniques exhibit superior performance compared to traditional non-CNN based methods, they often produce the image with visual artifacts due to the small receptive field in their network. In order to cope with this problem, we propose an adversarial context aggregation network (ACA-net) for low-light image enhancement, which effectively aggregates the global context via full-resolution intermediate layers. In the proposed method, we first increase the brightness of a low-light image using the two different gamma correction functions and then feed the brightened images to CNN to obtain the enhanced image. To this end, we train ACA network using L1 pixel-wise reconstruction loss and adversarial loss which encourages the network to generate a natural image. Experimental results show that the proposed method achieves state-of-the-art results in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
关键词： context aggregation,Low-light image enhancement,Convolutional neural network,generative adversarial network
更新于2025-09-19 17:15:36
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Fuzzy-NN approach with statistical features for description and classification of efficient image retrieval
摘要： Image retrieval based on content not only relies heavily upon the type of descriptors, but on the steps taken further. This has been an extensively utilized methodology for finding and fetching out images from the big database of images. Nowadays, a number of methodologies have been organized to increase the CBIR performance. This has an ability to recover pictures relying upon their graphical information. In the proposed method, Neuro-Fuzzy classifier and Deep Neural Network classifier are used to classify the pictures from a given dataset. The proposed approach obtained the highest accuracy in terms of Precision, Recall, and F-measure. To show the efficiency and effectiveness of proposed approach, statistical testing is used in terms of standard deviation, skewness, and kurtosis. The results reveal that the proposed algorithm outperforms other approaches using low computational efforts.
关键词： classifier.,Image retrieval,fuzzy,Deep Neural Network
更新于2025-09-19 17:15:36
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Auto-Tuning PID Distributed Power Control for Next-Generation Passive Optical Networks
摘要： This work proposes an adaptive auto-tuning distributed power control strategy aided by proportional-integral-derivative (PID) and by Adaline artificial neural network (AANN) approaches. The power control mechanism is realized for the upstream of next-generation passive optical networks (NG-PON), primarily deployed in the context of optical code division multiplexing access passive networks. The primary results demonstrate the ability of control and adaptive auto-tuning of the proposed AANN-based DPCA, considering realistic error estimates in the optical channel. For the sake of comparison, an adaptive auto-tuning procedure through the Tyreus–Lyuben method is included, indicating superior Euclidean norm of the NMSE performance combined with the lower complexity of the proposed NN-based DPCA method.
关键词： Tyreus-Lyuben (TL),Adaptive auto-tuning,Distributed power control algorithm (DPCA),Proportional-integral-derivative (PID),Adaline Neural Network (AANN),Next-generation passive optical networks (NG-PONs)
更新于2025-09-19 17:15:36
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A multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training
摘要： Mobile Laser Scanning (MLS) data of outdoor environment are typically characterised by occlusion, noise, clutter, large data size and high quantum of information which makes their classification a challenging problem. This paper presents three deep Convolutional Neural Network (CNN) architectures in three dimension (3D), namely single CNN (SCN), multi-faceted CNN (MFC) and MFC with reproduction (MFCR) for automatic classification of MLS data. The MFC uses multiple facets of an MLS sample as inputs to different SCNs, thus providing additional information during classification. The MFC, once trained, is used to reproduce additional samples with the help of existing samples. The reproduced samples are employed to further refine the MFC training parameters, thus giving a new method called MFCR. The three architectures are evaluated on an ensemble of 3D outdoor MLS data consisting of four classes, i.e. tree, pole, house and ground covered with low vegetation along with car samples from KITTI dataset. The total accuracy and kappa values of classifications reached up to (i) 86.0% and 81.3% for the SCN (ii) 94.3% and 92.4% for the MFC and (iii) 96.0% and 94.6% for the MFCR, respectively. The paper has demonstrated the use of multiple facets to significantly improve classification accuracy over the SCN. Finally, a unique approach has been developed for reproduction of samples which has shown potential to improve the accuracy of classification. Unlike previous works on the use of CNN for structured point cloud of indoor objects, this work shows the utility of different proposed CNN architectures for classification of varieties of outdoor objects, viz., tree, pole, house and ground which are captured as unstructured point cloud by MLS.
关键词： Sample reproduction,Mobile Laser Scanning (MLS),Automatic classification,Convolutional Neural Network (CNN)
更新于2025-09-19 17:15:36
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A Ship Rotation Detection Model in Remote Sensing Images Based on Feature Fusion Pyramid Network and Deep Reinforcement Learning
摘要： Ship detection plays an important role in automatic remote sensing image interpretation. The scale difference, large aspect ratio of ship, complex remote sensing image background and ship dense parking scene make the detection task difficult. To handle the challenging problems above, we propose a ship rotation detection model based on a Feature Fusion Pyramid Network and deep reinforcement learning (FFPN-RL) in this paper. The detection network can efficiently generate the inclined rectangular box for ship. First, we propose the Feature Fusion Pyramid Network (FFPN) that strengthens the reuse of different scales features, and FFPN can extract the low level location and high level semantic information that has an important impact on multi-scale ship detection and precise location of dense parking ships. Second, in order to get accurate ship angle information, we apply deep reinforcement learning to the inclined ship detection task for the first time. In addition, we put forward prior policy guidance and a long-term training method to train an angle prediction agent constructed through a dueling structure Q network, which is able to iteratively and accurately obtain the ship angle. In addition, we design soft rotation non-maximum suppression to reduce the missed ship detection while suppressing the redundant detection boxes. We carry out detailed experiments on the remote sensing ship image dataset, and the experiments validate that our FFPN-RL ship detection model has efficient detection performance.
关键词： feature map fusion,deep reinforcement learning,ship detection,convolution neural network
更新于2025-09-19 17:15:36
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Recurrent neural networks for discrimination of exo-atmospheric targets based on infrared radiation signature
摘要： Exo-atmospheric infrared (IR) target discrimination is an important research problem in space attack and defense. The different micro-motion states of the targets result in respective characteristics in the obtained IR radiation intensity sequences, and this difference is difficult to describe intuitively and extract effectively. Few methods can effectively contact the data with the micro-motion model, resulting in a low classification accuracy and difficult to meeting actual application requirements. We set up four types of targets by constructing the micro-motion model of the exo-atmospheric targets, including the warhead and heavy decoy, with spinning and coning motion; and the light decoy and debris, with tumbling motion, to get the IR radiation intensity sequences. We use random projection to improve the discrimination power of recurrent neural network, and to classify the time series of IR radiation intensity. Experimental results demonstrate that random projection recurrent neural network (R-RNN) is more effective than several other typical algorithms in time series classification (TSC) task, which can achieve an excellent target discrimination. We also analyze the effect of noise on the performance of the algorithm.
关键词： Recurrent neural network,Exo-atmospheric target discrimination,Micro-motion model,IR radiation intensity,Random projection
更新于2025-09-19 17:15:36
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Unit panel node detection by CNN on FAST reflector
摘要： The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has an active reflector. During observations, the reflector will be deformed into a paraboloid 300 meters in diameter. To improve its surface accuracy, we propose a scheme for photogrammetry to measure the positions of 2226 nodes on the reflector. The way to detect the nodes in the photos is the key problem in this application of photogrammetry. This paper applies a convolutional neural network (CNN) with candidate regions to detect the nodes in the photos. Experimental results show a high recognition rate of 91.5%, which is much higher than the recognition rate for traditional edge detection.
关键词： photogrammetry,FAST,telescopes,nodes detect,convolutional neural network
更新于2025-09-19 17:15:36
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Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network
摘要： The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processing and pattern recognition technology can easily classify these images, identify the subcellular location of proteins, and subsequently carry out related work such as analysis and investigation of protein function. Here, based on a fluorescence microscopy 2D image dataset of HeLa cells, the CapsNet network model was used to classify ten types of images of proteins in different subcellular compartments. Capsules in the CapsNet network model were trained to capture the possibility of certain features and variants rather than to capture the characteristics of a specific variant. The capsule at the same level predicted the instantiation parameters of the higher level capsule through the transformation matrix, and the higher level capsule became active when multiple dynamic routing forecasts were consistent. Experiments show that using the CapsNet network model to classify 2D HeLa datasets can achieve higher accuracy.
关键词： CapsNet,Image classification,2D HeLa,Subcellular localization,Fluorescence microscopy,Neural network
更新于2025-09-19 17:15:36
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[IEEE 2018 7th European Workshop on Visual Information Processing (EUVIP) - Tampere, Finland (2018.11.26-2018.11.28)] 2018 7th European Workshop on Visual Information Processing (EUVIP) - A Hybrid Approach to Hand Detection and Type Classification in Upper-Body Videos
摘要： Detection of hands in videos and their classification into left and right types are crucial in various human-computer interaction and data mining systems. A variety of effective deep learning methods have been proposed for this task, such as region-based convolutional neural networks (R-CNNs), however the large number of their proposal windows per frame deem them computationally intensive. For this purpose we propose a hybrid approach that is based on substituting the 'selective search' R-CNN module by an image processing pipeline assuming visibility of the facial region, as for example in signing and cued speech videos. Our system comprises two main phases: preprocessing and classification. In the preprocessing stage we incorporate facial information, obtained by an AdaBoost face detector, into a skin-tone based segmentation scheme that drives Kalman filtering based hand tracking, generating very few candidate windows. During classification, the extracted proposal regions are fed to a CNN for hand detection and type classification. Evaluation of the proposed hybrid approach on four well-known datasets of gestures and signing demonstrates its superior accuracy and computational efficiency over the R-CNN and its variants.
关键词： region-based convolutional neural network (R-CNN),hand type classification,Hand detection,AdaBoost face detection,Kalman filtering
更新于2025-09-19 17:15:36