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oe1(光电查) - 科学论文

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出版时间
  • 2018
研究主题
  • Conditional Random Fields (CRF)
  • Convolutional Neural Network (CNN)
  • Fine Classification
  • Airborne hyperspectral
应用领域
  • Optoelectronic Information Science and Engineering
机构单位
  • Wuhan University
  • Central South University
  • Hubei University
404 条数据
?? 中文（中国） ▾

  • [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) - FV-Net: learning a finger-vein feature representation based on a CNN

    摘要： Finger vein pattern has been proven to be an effective biometric for personal identification in recent years. Nevertheless, there remain challenges that need to be solved, such as finger-vein features that lack robustness and expressiveness. In this paper, we propose a deep convolutional neural network (CNN) model, named the Finger-vein Network (FV-Net), to learn the features representative of a finger vein that is more discriminative and robust than handcrafted features. Next, to address the issue of translation and rotation in vein imaging, we propose a template-like matching strategy while designing the top architecture of the FV-net to extract features with spatial information. Finally, the extensive experimental results show that our proposed method can achieve excellent performance on several public datasets.

    关键词： feature representation,deep learning,convolutional neural network,finger vein verification

    更新于2025-09-09 09:28:46

  • Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images

    摘要： Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.

    关键词： surface water bodies,Landsat-8,MNDWI,deep neural network,perceptron neural network,AWEI,PDWF

    更新于2025-09-04 15:30:14

  • Machine Learning for 100Gb/s/λ Passive Optical Network

    摘要： To respond the growing bandwidth demand by emerging applications such as fixed-mobile convergence for 5G and beyond 5G, 100Gb/s/λ access network becomes the next research focus of passive optical network (PON) roadmap. Intensity modulation and direct detection (IMDD) technology is still considered as a promising candidate for 100Gb/s/λ PON attributed to its low cost, low power consumption and small footprint. In this paper, we achieve 100Gb/s/λ IMDD PON by using 20G-class optical and electrical devices due to its commercial linear and nonlinear availability. To mitigate the system distortions, neural network (NN) based equalizer is used and the performance is compared with feedforward equalizer (FFE) and Volterra nonlinear equalizer (VNE). We introduce the rules to train and test the data when using NN-based equalizer to guarantee a fair comparison with FFE and VNE. Random data has to be used for training, but for test, both random data and psudo-random bit sequence (PRBS) are applicable. We found NN-based equalizer has the same performance with FFE and VNE in the case of linear distortion only, but outperforms them in strong nonlinearity case. In the experiment, to improve the loss budget, we increase the launch power to 18 dBm, achieving a 30-dB loss budget for 33Gbaud/s PAM8 signal at the system frequency response of 16.2 GHz, attributed to the strong nonlinear equalization capability of NN.

    关键词： neural network (NN),machine learning,intensity modulation and direct detection (IMDD),digital signal processing (DSP),Passive optical network (PON)

    更新于2025-09-04 15:30:14

  • [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

  • Evaluation of texture features at staging liver fibrosis based on phase contrast X-ray imaging

    摘要： Background: The purpose of this study is to explore the potential of phase contrast imaging to detect fibrotic progress in its early stage; to investigate the feasibility of texture features for quantified diagnosis of liver fibrosis; and to evaluate the performance of back propagation (BP) neural net classifier for characterization and classification of liver fibrosis. Methods: Fibrous mouse liver samples were imaged by X-ray phase contrast imaging, nine texture measures based on gray-level co-occurrence matrix were calculated and the feasibility of texture features in the characterization and discrimination of liver fibrosis at early stages was investigated. Furthermore, 36 or 18 features were applied to the input of BP classifier; the classification performance was evaluated using receiver operating characteristic curve. Results: The phase contrast images displayed a vary degree of texture pattern from normal to severe fibrosis stages. The BP classifier could distinguish liver fibrosis among normal, mild, moderate and severe stages; the average accuracy was 95.1% for 36 features, and 91.1% for 18 features. Conclusion: The study shows that early stages of liver fibrosis can be discriminated by the morphological features on the phase contrast images. BP network model based on combination of texture features is demonstrated effective for staging liver fibrosis.

    关键词： Liver fibrosis,Phase contrast imaging,Mouse liver specimen,Neural network,Texture features

    更新于2025-09-04 15:30:14

  • Total Variation Based Neural Network Regression for Nonuniformity Correction of Infrared Images

    摘要： Many existing scene-adaptive nonuniformity correction (NUC) methods suffer from slow convergence rate together with ghosting effects. In this paper, an improved NUC algorithm based on total variation penalized neural network regression is presented. Our work mainly focuses on solving the overfitting problem in least mean square (LMS) regression of traditional neural network NUC methods, which is realized by employing a total variation penalty in the cost function and redesigning the processing architecture. Moreover, an adaptive gated learning rate is presented to further reduce the ghosting artifacts and guarantee fast convergence. The performance of the proposed algorithm is comprehensively investigated with artificially corrupted test sequences and real infrared image sequences, respectively. Experimental results show that the proposed algorithm can effectively accelerate the convergence speed, suppress ghosting artifacts, and promote correction precision.

    关键词： total variation,neural network,infrared imaging,nonuniformity correction

    更新于2025-09-04 15:30:14

  • [Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11257 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part II) || Feature Visualization Based Stacked Convolutional Neural Network for Human Body Detection in a Depth Image

    摘要： Human body detection is a key technology in the ?elds of biometric recognition, and the detection in a depth image is rather challenging due to serious noise e?ects and lack of texture information. For addressing this issue, we propose the feature visualization based stacked convolutional neural network (FV-SCNN), which can be trained by a two-layer unsupervised learning. Speci?cally, the next CNN layer is obtained by optimizing a sparse auto-encoder (SAE) on the reconstructed visualization of the former to capture robust high-level features. Experiments on SZU Depth Pedestrian dataset verify that the proposed method can achieve favorable accuracy for body detection. The key of our method is that the CNN-based feature visualization actually pursues a data-driven processing for a depth map, and signi?cantly alleviates the in?uences of noise and corruptions on body detection.

    关键词： Feature visualization,Sparse auto-encoder,Convolutional neural network,Depth image,Human detection

    更新于2025-09-04 15:30:14

  • [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

  • DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering

    摘要： Nonlinear electromagnetic (EM) inverse scattering is a quantitative and super-resolution imaging technique, in which more realistic interactions between the internal structure of scene and EM wavefield are taken into account in the imaging procedure, in contrast to conventional tomography. However, it poses important challenges arising from its intrinsic strong nonlinearity, ill-posedness, and expensive computation costs. To tackle these difficulties, we, for the first time to our best knowledge, exploit a connection between the deep neural network (DNN) architecture and the iterative method of nonlinear EM inverse scattering. This enables the development of a novel DNN-based methodology for nonlinear EM inverse problems (termed here DeepNIS). The proposed DeepNIS consists of a cascade of multi-layer complex-valued residual convolutional neural network (CNN) modules. We numerically and experimentally demonstrate that the DeepNIS outperforms conventional nonlinear inverse scattering methods in terms of both the image quality and computational time. We show that DeepNIS can learn a general model approximating the underlying EM inverse scattering system. It is expected that the DeepNIS will serve as powerful tool in treating highly nonlinear EM inverse scattering problems over different frequency bands, which are extremely hard and impractical to solve using conventional inverse scattering methods.

    关键词： Complex-valued Residual CNN,Nonlinear Inverse Scattering,Convolutional Neural Network

    更新于2025-09-04 15:30:14

  • 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