检索结果-内蒙古大学图书馆

Polarization Image Demosaicking via Nonlocal sparse Tensor Factorization

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Zhang, Junchao Chen, Jianlai Yu, Hanwen Yang, Degui Liang, Buge Xing, Mengdao Cent South Univ Sch Aeronaut & Astronaut Changsha 410083 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Division-of-focal-plane (DoFP) polarimeter provides a way for snapshot acquisition, making it available to simultaneously record polarization measurements at different orientations. This polarization imaging system has gained more attention in the last few years and is promising to be used in the fields of computer vision and remote sensing. However, this system suffers from the degradation of spatial resolution. To reconstruct polarization information at full resolution, polarization image demosaicking is indispensable. To address polarization image demosaicking issue while preserving the essential structure of polarization data, a sparse tensor factorization-based model is proposed. For a target cube, its similar cubes are first grouped together as a tensor. Then, its compact dictionary and sparse core tensor are learned by factorizing the tensor using sparse coding. Moreover, the correlation among different polarization orientations and the nonlocal self-similarity are adopted to boost the performance. Experimental results on synthetic and real-world data demonstrate that our proposed model outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality.

关键词： Tensors Dictionaries Imaging Image reconstruction Machine learning Interpolation Image color analysis Dictionary learning nonlocal self-similarity polarization image demosaicking sparse coding tensor factorization

来源：评论

学校读者我要写书评

暂无评论

Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection

引用

Computers, Materials & Continua 2020年第10期65卷 777-788页

作者： Shi Li Xinyan Cao Yiting Nan School of Information Engineering Northeast Forestry UniversityHarbinChina Petabase LLC Washington DC20001USA

Stance detection is the task of attitude identification toward a *** work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level ***,because the target is not always mentioned in the text,most methods have ignored target *** order to solve these problems,we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory(LSTM)and the excellent extracting performance of convolutional neural networks(CNNs).The method can obtain multi-level features that consider both local and global *** also introduce attention mechanisms to magnify target information-related ***,we employ sparse coding to remove noise to obtain characteristic *** was improved by using sparse coding on the basis of attention employment and feature *** evaluate our approach on the SemEval-2016Task 6-A public dataset,achieving a performance that exceeds the benchmark and those of participating teams.

关键词： Attention sparse coding multi-level features ensemble model

来源：评论

学校读者我要写书评

暂无评论

L0 Norm Optimization in Scrambled sparse Representation Domain and Its Application to EtC System

引用

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2020年第12期E103A卷 1589-1598页

作者： Nakachi, Takayuki Kiya, Hitoshi NTT Corp Yokosuka Kanagawa 2390847 Japan Tokyo Metropolitan Univ Hino Tokyo 1910065 Japan

In this paper, we propose L0 norm optimization in a scrambled sparse representation domain and its application to an Encryption-then-Compression (EtC) system. We design a random unitary transform that conserves L0 norm isometry. The resulting encryption method provides a practical orthogonal matching pursuit (OMP) algorithm that allows computation in the encrypted domain. We prove that the proposed method theoretically has exactly the same estimation performance as the nonencrypted variant of the OMP algorithm. In addition, we demonstrate the security strength of the proposed secure sparse representation when applied to the EtC system. Even if the dictionary information is leaked, the proposed scheme protects the privacy information of observed signals.

关键词： sparse coding secure computation orthogonal matching pursuit (OMP) random unitary transform EtC system

来源：评论

学校读者我要写书评

暂无评论

First application of the super -resolution imaging technique using a Compton camera

引用

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2020年 969卷 164034-164034页

作者： Sato, S. Kataoka, J. Kotoku, J. Taki, M. Oyama, A. Tagawa, L. Fujieda, K. Nishi, F. Toyoda, T. Waseda Univ Shinjuku Ku 3-4-1 Ohkubo Tokyo Japan Teikyo Univ Itabashi Ku 2-11-1 Kaga Tokyo Japan RIKEN iTHEMS 2-1 Hirosawa Wako Saitama Japan

In medical imaging, precise and reliable images are very important. However, the quality of medical images is sometimes limited by low-event statistics owing to the low sensitivity of the detectors commonly used in radiology. On the other hand, long exposure to radiation and long inspection duration can become a burden for patients. In this paper, we propose a method for generating high-quality images of gamma ray sources from low statistic data by using machine learning methods based on dictionary learning and sparse coding. As the first application, we generated a high-quality image of ¹³⁷Cs, which emits 662-keV gamma rays, from low-event statistics measured using a Compton camera. We simulated with Geant4 various geometries of the gamma-ray source (¹³⁷Cs; 662 keV) as measured with a Compton camera by Geant4. Then, complete sets of low-resolution and high-resolution dictionaries were prepared. We generated super-resolution images from low-resolution test images obtained from actual measurements. The convergence of the gamma-ray images was similar for both the ground truth and predicted images, as supported by the improvements in the structural similarity (SSIM), peak signal-to-noise (PSNR) ratio, and root mean square error (RMSE) in the corresponding images. We also discuss future plans to use the super-resolution technique for visualizing radium chloride (²²³RaCl₂) in the patient’s body, which will make it possible to achieve in-vivo imaging of alpha-particle internal therapy for the first time.

关键词： Machine learning sparse coding Dictionary learning Compton camera Radio nuclide therapy

来源：评论

学校读者我要写书评

暂无评论

Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible With Various Temporal Codes

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第4期31卷 1285-1296页

作者： Hong, Chaofei Wei, Xile Wang, Jiang Deng, Bin Yu, Haitao Che, Yanqiu Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Penn State Univ Coll Med Dept Neurosurg Hershey PA 17033 USA Penn State Univ Ctr Neural Engn University Pk PA 16802 USA

Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for theoretical neuroscience studies. Here, we propose a modified SpikeProp learning algorithm, which ensures better learning stability for SNNs and provides more diverse network structures and coding schemes. Specifically, we designed a spike gradient threshold rule to solve the well-known gradient exploding problem in SNN training. In addition, regulation rules on firing rates and connection weights are proposed to control the network activity during training. Based on these rules, biologically realistic features such as lateral connections, complex synaptic dynamics, and sparse activities are included in the network to facilitate neural computation. We demonstrate the versatility of this framework by implementing three well-known temporal codes for different types of cognitive tasks, namely, handwritten digit recognition, spatial coordinate transformation, and motor sequence generation. Several important features observed in experimental studies, such as selective activity, excitatory-inhibitory balance, and weak pairwise correlation, emerged in the trained model. This agreement between experimental and computational results further confirmed the importance of these features in neural function. This work provides a new framework, in which various neural behaviors can be modeled and the underlying computational mechanisms can be studied.

关键词： Neurons Computational modeling Biological neural networks Task analysis Biological system modeling Training Synapses Neural dynamics sparse coding spiking neural network (SNN) supervised learning temporal code

来源：评论

学校读者我要写书评

暂无评论

Deconvolution Technique for InSAR Phase Restoration

Deconvolution Technique for InSAR Phase Restoration

引用

2021 CIE International Conference on Radar, Radar 2021

作者： Kang, Jian Li, Zhe School of Electronic and Information Engineering Soochow University Suzhou215006 China

ISBN: (纸本)9781665498142

Interferometric phase generated from coherent processing from Synthetic Aperture Radar (SAR) plays a vital role in large-scale deformation monitoring and digital elevation model (DEM) generation. Due to the coherence loss between acquisitions, the produced interferometric phases contain noise, which affects the application performance of interferometric SAR (InSAR) phases. Most state-of-the-art InSAR phase restoration methods are generally based on the nonlocal filtering processing scheme. In this paper, we propose a data-driven approach which formulates the InSAR phase restoration problem from a deconvolution perspective. Then, based on the learned convolutional filters from the data, we then obtain the denoised InSAR phases via solving a sparse coding problem. The proposed method can not only suppress interferometric phase noise, but also avoid the staircase effect and preserve the details. The experimental results on synthetic and one RADARSAT-2 dataset, respectively, demonstrate that the proposed method achieves the best performance among those previous state-of-the art nonlocal InSAR filter-based methods. © 2021 IEEE.

关键词： Convolutional dictionary learning deconvolution InSAR SAR denoising sparse coding

来源：评论

学校读者我要写书评

暂无评论

A Robust Polarmetric SAR Terrain Classification Based on sparse Deep Autoencoder Model Combined With Wavelet Kernel-Based Classifier

引用

IEEE ACCESS 2020年 8卷 64810-64819页

作者： Chen, Xiangdong Deng, Jianghong Huanghuai Univ Sch Informat Engn Zhumadian 463000 Peoples R China Huanghuai Univ Sch Animat Zhumadian 463000 Peoples R China

Since the existing terrain classification algorithm based on deep learning is not ideal for unbalanced PolSAR classification, a effective terrain classification algorithm based on wavelet kernel sparse deep coding network under unbalanced data set is proposed in this paper. The algorithm firstly adopts a structured sparse operation so as to enhance the accuracy of feature propagation and reduce the amount of stored data, where the unimportant parameter connections in each group are gradually reduced by dividing the network convolution kernel into multiple groups during the training process;The wavelet kernel-based classifier is used instead of the Sigmoid function to classify and identify features for different terrain, which has high generalization performance for small sample, nonlinear and high-dimensional mode classification problems. The experimental results show that our proposed classification algorithm can improve the classification performance of unbalanced samples, and improve the classification efficiency while ensuring the accuracy of classification.

关键词： Radar polarimetry Feature extraction Classification algorithms Support vector machines Kernel Training Deep learning Terrain classification wavelet kernel support vector machine deep model sparse coding sigmoid function

来源：评论

学校读者我要写书评

暂无评论

Learning Structured sparse Representations for Voice Conversion

引用

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 343-354页

作者： Ding, Shaojin Zhao, Guanlong Liberatore, Christopher Gutierrez-Osuna, Ricardo Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

sparse-coding techniques for voice conversion assume that an utterance can be decomposed into a sparse code that only carries linguistic contents, and a dictionary of atoms that captures the speakers' characteristics. However, conventional dictionary-construction and sparse-coding algorithms rarely meet this assumption. The result is that the sparse code is no longer speaker-independent, which leads to lower voice-conversion performance. In this paper, we propose a Cluster-Structured sparse Representation (CSSR) that improves the speaker independence of the representations. CSSR consists of two complementary components: a Cluster-Structured Dictionary Learning module that groups atoms in the dictionary into clusters, and a Cluster-Selective Objective Function that encourages each speech frame to be represented by atoms from a small number of clusters. We conducted four experiments on the CMU ARCTIC corpus to evaluate the proposed method. In a first ablation study, results show that each of the two CSSR components enhances speaker independence, and that combining both components leads to further improvements. In a second experiment, we find that CSSR uses increasingly larger dictionaries more efficiently than phoneme-based representations by allowing finer-grained decompositions of speech sounds. In a third experiment, results from objective and subjective measurements show that CSSR outperforms prior voice-conversion methods, improving the acoustic quality of the synthesized speech while retaining the target speaker's voice identity. Finally, we show that the CSSR captures latent (i.e., phonetic) information in the speech signal.

关键词： Dictionaries Phonetics Training Machine learning Encoding Speech processing Clustering algorithms Voice conversion sparse coding sparse representation dictionary learning

来源：评论

学校读者我要写书评

暂无评论

Evolutionary variational optimization of generative models

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2022年第1期23卷 935-985页

作者： Jakob Drefs Enrico Guiraud Jörg Lücke Machine Learning Lab University of Oldenburg Oldenburg Germany CERN Geneva Switzerland and Machine Learning Lab University of Oldenburg Oldenburg Germany

We combine two popular optimization approaches to derive learning algorithms for generative models: variational optimization and evolutionary algorithms. The combination is realized for generative models with discrete latents by using truncated posteriors as the family of variational distributions. The variational parameters of truncated posteriors are sets of latent states. By interpreting these states as genomes of individuals and by using the variational lower bound to define a fitness, we can apply evolutionary algorithms to realize the variational loop. The used variational distributions are very flexible and we show that evolutionary algorithms can effectively and efficiently optimize the variational bound. Furthermore, the variational loop is generally applicable ("black box") with no analytical derivations required. To show general applicability, we apply the approach to three generative models (we use Noisy-OR Bayes Nets, Binary sparse coding, and Spike-and-Slab sparse coding). To demonstrate effectiveness and efficiency of the novel variational approach, we use the standard competitive benchmarks of image denoising and inpainting. The benchmarks allow quantitative comparisons to a wide range of methods including probabilistic approaches, deep deterministic and generative networks, and non-local image processing methods. In the category of "zero-shot" learning (when only the corrupted image is used for training), we observed the evolutionary variational algorithm to significantly improve the state-of-the-art in many benchmark settings. For one well-known inpainting benchmark, we also observed state-of-the-art performance across all categories of algorithms although we only train on the corrupted image. In general, our investigations highlight the importance of research on optimization methods for generative models to achieve performance improvements.

关键词： expectation maximization variational methods evolutionary algorithms sparse coding denoising inpainting

来源：评论

学校读者我要写书评

暂无评论

Automatic image annotation via category labels

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第17-18期79卷 11421-11435页

作者： Zhang, Weifeng Hu, Hua Hu, Haiyang Yu, Jing Jiaxing Univ Coll Math Phys & Informat Engn Jiaxing Zhejiang Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou Peoples R China Hangzhou Normal Univ Sch Informat Sci & Engn Hangzhou Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

Automatic image annotation aims to assign relevant keywords to images and has become a research focus. Although many techniques have been proposed to solve this problem in the last decade, giving promissing performance on standard datasets, we propose a novel automatic image annotation technique in this paper. Our method uses a label transfer mechanism to automatically recommend those promising tags to each image by using the category information of images. As image representation is one of the key technique in image annotation, we use sparse coding based spatial pyramid matching and deep convolutional neural networks to model image features. And metric learning technique is further used to combine these features to achieve more effective image representation in this paper. Experimental results illustrate that the proposed method get similar or better results than the state-of-the-art methods on three standard image datasets.

关键词： Automatic image annotation Image understanding Deep learning sparse coding

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：