- 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, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Object Detection with Head Direction in Remote Sensing Images Based on Rotational Region CNN
摘要： Object detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. In this paper, we propose a novel detection framework based on rotational region convolution neural network to cope with the problem of non-maximum suppression in dense objects detection. The bounding boxes obtained by adopting our method is the minimum bounding rectangle of object with less redundant regions. Furthermore, we find the head direction of the object through prediction. There are three important changes to our framework over traditional detection methods, representation and regression of rotational bounding box, head direction prediction and rotational non-maximal suppression. Experiments based on remote sensing images from Google Earth for Object detection show that our detection method based on rotational region CNN has a competitive performance.
关键词： prediction,object detection,rotating region,convolution neural network,non-maximal suppression
更新于2025-09-10 09:29:36
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Convolution neural network-based time-domain equalizer for DFT-Spread OFDM VLC system
摘要： This paper presents a novel time-domain equalizer for visible light communication (VLC) system using machine learning (ML) method. In this work, we employ discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM) as modem scheme and convolution neural network (CNN) as kernel processing unit of equalizer. After estimating channel state information (CSI) from training sequence, the proposed equalizer recovers transmitted symbols according to the estimated CSI. Numerical simulations indicate that the equalizer can significantly enhance bit error rate (BER) performance. For example, when signal-to-noise ratio (SNR) is 20dB and 16/32/64-quadrature amplitude modulation (QAM) is exploited, original BER is about 0.5 while the BER after recovery achieves 10?5, which is much lower than forward error correction (FEC) limit 3.8×10?3. This work promotes the application of ML in VLC domain. To the best of our knowledge, this is the first time a CNN-based equalizer has been explored.
关键词： Machine learning (ML),Discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM),Visible light communication (VLC),Convolution neural network (CNN)
更新于2025-09-10 09:29:36
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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 - A Classification Method for Polsar Images using SLIC Superpixel Segmentation and Deep Convolution Neural Network
摘要： Deep convolution neural networks (DCNN) have been successfully introduced in the field of Polarimetric SAR image classification. However, the commonly used DCNN will classify each pixel in the image and neglect the fact that neighboring pixels may have similar intensity. Besides, the fixed size input in DCNN cannot be well adopted in remote sensing image which includes a great deal of different-scale information. Thus, superpixel segmentation (SS) and the input pyramid are introduced in this paper to improve the performance of DCNN. The former will guide the DCNN to classify superpixel instead of single pixel and the latter will include different-scale information around the pixel. Experiments carried out on two scenes of ALOS-2 PALSAR-2 POLSAR images demonstrate that the introduced technic can help DCNN achieve good accuracy and smooth boundary adherence with highly efficiency.
关键词： superpixel segmentation,convolution neural network,Polarimetric synthetic aperture radar
更新于2025-09-10 09:29:36
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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 - Shadow Tracking of Moving Target Based on CNN for Video SAR System
摘要： Fast Moving targets always are shifted or smeared outside the scene in different images sequence to make video by Circle Synthetic Aperture Radar (SAR).In this paper, a novel moving target tracking approach with the shadow detection and tracking (SDT) is presented based on Convolution Neural Network. Based on the shadow characteristic of moving target in SAR imagery, CNN tracking classification is employed on potential moving target candidates extracted from a sequence of temporal and spatial sub-aperture SAR images to detect and track the moving targets. By the simulation experiments and performance analysis, the validity of the proposed algorithm can be demonstrated. Real data set processing results are provided to demonstrate the effectiveness of the proposed approach.
关键词： Video SAR,Convolution Neural Network,Shadow detection,Moving target tracking
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Ostrava, Czech Republic (2018.9.17-2018.9.20)] 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Learning Based Segmentation of Skin Lesion from Dermoscopic Images
摘要： Segmentation is the pre-requisite process in most of the computer aided diagnosis systems for medical imaging. Presence of different artifacts makes segmentation of skin lesion very difficult. Abnormal growth of artifacts can appear as false positives and can degrade the performance of the diagnosis systems. It can be avoided only when false structures are removed while extracting the lesion. To address this issue, this paper proposes deep leaning for skin lesion segmentation. Within this framework, automated skin lesion segmentation is proposed which achieves high accuracy segmentation of skin lesion. Our proposed architecture is 31 layers deep with same filter size. The validity of the proposed techniques is tested on two publically available databases of PH2 and ISIC 2017. Experimental results show the efficiency of the proposed approaches. The proposed method gives Dice Coefficient of 92.3% for PH2 Dataset while Dice Coefficient of 85.5% for ISIC 2017 Dataset.
关键词： Dice Coefficient,Deep Learning,Melanoma,Dermoscopy,Automatic segmentation,Convolution Neural Network
更新于2025-09-10 09:29:36
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[ASME ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems - San Antonio, Texas, USA (Monday 10 September 2018)] Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies - Real-Time Detection of Ancient Architecture Features Based on Smartphones
摘要： Due to the particularity of texture features in ancient buildings, which refers to the fact that these features have a high historical and artistic value, it is of great significance to identify and count them. However, the complexity and large number of textures are a big challenge for the artificial identification statistics. In order to overcome these challenges, this paper proposes an approach that uses smartphones to achieve a real- time detection of ancient buildings’ features. The training process is based on SSD-Mobilenet, which is a kind of Convolutional Neural Network (CNN). The results show that this method shows well performance in reality and can indeed detect different ancient building features in real time.
关键词： real- time object detection,smartphone,ancient architecture feature,deep learning,convolution neural network,SSD-Mobilenet
更新于2025-09-04 15:30:14
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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) - Mammographic mass detection based on convolution neural network
摘要： Mammography is one of the broadly used imaging modality for breast cancer screening and detection. Locating mass from the whole breast is an important work in computer-aided detection. Traditionally, handcrafted features are employed to capture the difference between a mass region and a normal region. Recently convolution neural network (CNN) which automatically discovers features from the images shows promising results in many pattern recognition tasks. In this paper, three mass detection schemes based on CNN are evaluated. First, a suspicious region locating method based on heuristic knowledge is employed. Then three different CNN schemes are designed to classify the suspicious region as mass or normal. The proposed schemes are evaluated on a dataset of 352 mammograms. Compared with several handcrafted features, CNN-based methods shows better mass detection performance in terms of free receiver operating characteristic (FROC) curve.
关键词： deep learning,convolution neural network,mass detection,mammogram
更新于2025-09-04 15:30:14
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Recognition of incorrect assembly of internal components by X-ray CT and deep learning
摘要： It is important to make sure that all components of a complex product are assembled correctly. Because in many cases, some components are enclosed in an opaque shell, x-ray imaging is currently used to extract their characteristics and match prior-known ones. However, x-ray imaging is not very robust in recognition of incorrect assembly of internal components, because some of them may overlap. To solve this problem, we propose a new method to detect internal component assembly fault, by x-ray computed tomography (CT) and convolutional neural network (CNN). Multi-view imaging is implemented by mechanical rotation of a product in respect with an x-ray CT machine to capture multiple projection information on each internal component, and then the component can be recognized by making use of deep learning. A CNN model is trained to classify the internal components and give the coordinates of each component. Based on the CNN recognition results and the CT projection sinogram, a projection corresponding to a reference in a projection data set of a standard product can be found. By comparing and matching the locations of each component, transposition or dislocation can be recognized. Both simulation and experiment show that this new method can effectively identify incorrect assembly, missing assembly, transposition, and other problems, improving the product quality.
关键词： Projection sinogram,Assembly recognition,Convolution neural network (CNN),x-ray CT
更新于2025-09-04 15:30:14
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Hyperspectral Image Classification Using Spatial and Edge Features Based on Deep Learning
摘要： In recent years, deep learning has been widely used in the classification of hyperspectral images and good results have been achieved. But it is easy to ignore the edge information of the image when using the spatial features of hyperspectral images to carry out the classification experiments. In order to make full use of the advantages of convolution neural network (CNN), we extract the spatial information with the method of minimum noise fraction (MNF) and the edge information by bilateral filter. The combination of the two kinds of information not only increases the useful information but also effectively removes part of the noise. The convolution neural network is used to extract features and classify for hyperspectral images on the basis of this fused information. In addition, this article also uses another kind of edge-filtering method to amend the final classification results for a better accuracy. The proposed method was tested on three public available datasets: the University of Pavia, the Salinas, and the Indian Pines. The competitive results indicate that our approach can realize a classification of different ground targets with a very high accuracy.
关键词： hyperspectral images classification,Deep learning,spatial features,convolution neural network,minimum noise fraction
更新于2025-09-04 15:30:14