检索结果-内蒙古大学图书馆

Light Field Compression With Disparity-Guided sparse coding Based on Structural Key Views

IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第1期27卷 314-324页

作者： Chen, Jie Hou, Junhui Chau, Lap-Pui Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world. One of the emerging technologies is light field (LF) cameras based on micro-lens arrays. To record the directional information of the light rays, a much larger storage space and transmission bandwidth are required by an LF image as compared with a conventional 2D image of similar spatial dimension. Hence, the compression of LF data becomes a vital part of its application. In this paper, we propose an LF codec with disparity guided sparse coding over a learned perspective-shifted LF dictionary based on selected Structural Key Views (SC-SKV). The sparse coding is based on a limited number of optimally selected SKVs;yet the entire LF can be recovered from the coding coefficients. By keeping the approximation identical between encoder and decoder, only the residuals of the non-key views, disparity map, and the SKVs need to be compressed into the bit stream. An optimized SKV selection method is proposed such that most LF spatial information can be preserved. To achieve optimum dictionary efficiency, the LF is divided into several coding regions, over which the reconstruction works individually. Experiments and comparisons have been carried out over benchmark LF data set, which show that the proposed SC-SKV codec produces convincing compression results in terms of both rate-distortion performance and visual quality compared with Joint Exploration Model: with 37.9% BD-rate reduction and 1.17-dB BD-PSNR improvement achieved on average, especially with up to 6-dB improvement for low bit rate scenarios.

关键词： Light field structural key view sparse coding perspective shifting disparity

来源：评论

学校读者我要写书评

暂无评论

Structure Preserving sparse coding for Data Representation

引用

NEURAL PROCESSING LETTERS 2018年第3期48卷 1705-1719页

作者： Shu, Zhenqiu Wu, Xiao-jun Hu, Cong Jiangsu Univ Technol Sch Comp Engn Changzhou Peoples R China Jiangnan Univ Sch IoT Engn Wuxi Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Socia Nanjing Jiangsu Peoples R China

sparse coding methods have shown the superiority in data representation. However, traditional sparse coding methods cannot explore the manifold structure embedded in data. To alleviate this problem, a novel method, called Structure Preserving sparse coding (SPSC), is proposed for data representation. SPSC imposes both local affinity and distant repulsion constraints on the model of sparse coding. Therefore, the proposed SPSC method can effectively exploit the structure information of high dimensional data. Beside, an efficient optimization scheme for our proposed SPSC method is developed, and the convergence analysis on three datasets are presented. Extensive experiments on several benchmark datasets have shown the superior performance of our proposed method compared with other state-of-the-art methods.

关键词： sparse coding Data representation Manifold structure Structure preserving Local affinity Distant repulsion

来源：评论

学校读者我要写书评

暂无评论

Unsupervised scene segmentation using sparse coding context

引用

MACHINE VISION AND APPLICATIONS 2013年第2期24卷 243-254页

作者： Liu, Yen-Cheng Chen, Hwann-Tzong Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30043 Taiwan

This paper presents an approach to image understanding on the aspect of unsupervised scene segmentation. With the goal of image understanding in mind, we consider 'unsupervised scene segmentation' a task of dividing a given image into semantically meaningful regions without using annotation or other human-labeled information. We seek to investigate how well an algorithm can achieve at partitioning an image with limited human-involved learning procedures. Specifically, we are interested in developing an unsupervised segmentation algorithm that only relies on the contextual prior learned from a set of images. Our algorithm incorporates a small set of images that are similar to the input image in their scene structures. We use the sparse coding technique to analyze the appearance of this set of images;the effectiveness of sparse coding allows us to derive a priori the context of the scene from the set of images. Gaussian mixture models can then be constructed for different parts of the input image based on the sparse-coding contextual prior, and can be combined into an Markov-random-field-based segmentation process. The experimental results show that our unsupervised segmentation algorithm is able to partition an image into semantic regions, such as buildings, roads, trees, and skies, without using human-annotated information. The semantic regions generated by our algorithm can be useful, as pre-processed inputs for subsequent classification-based labeling algorithms, in achieving automatic scene annotation and scene parsing.

关键词： Unsupervised image segmentation Semantic scene analysis sparse coding Markov random fields

来源：评论

学校读者我要写书评

暂无评论

Block-sparse coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

引用

IEEE INTERNET OF THINGS JOURNAL 2021年第5期8卷 3211-3223页

作者： Zhao, Lingjun Huang, Huakun Su, Chunhua Ding, Shuxue Huang, Huawei Tan, Zhiyuan Li, Zhenni Guilin Univ Elect Technol Sch Artificial Intelligence Guilin 541004 Peoples R China Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Edinburgh Napier Univ Sch Comp Edinburgh EH10 SDT Midlothian Scotland Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in the IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to an undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of the model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds -10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases.

关键词： Encoding Wireless sensor networks Internet of Things Machine learning Wireless communication Sensors Communication system security Block device-free localization (DFL) Internet of Things machine learning (ML) multiple targets sparse coding

来源：评论

学校读者我要写书评

暂无评论

Memristive neural network circuit design based on locally competitive algorithm for sparse coding application

引用

NEUROCOMPUTING 2024年 578卷

作者： Hong, Qinghui Xiao, Pingdan Fan, Ruijia Du, Sichun Hunan Univ Coll Comp Sci & Elect Engn Changsha 418002 Hunan Peoples R China

sparse coding can quickly, accurately, and inexpensively represent the stimulus information received by biological vision neurons. However, there is no entire circuit that can realize real -time sparse coding by efficient analog computation. A memristor neural network circuit that solves the sparse coding problem in real -time and in parallel is proposed to solve such a problem. In our circuit, a novel memristor array structure can realize both reading and writing in parallel. A neural network circuit that can execute the Locally Competitive Algorithm (LCA) is designed based on this structure. Given these designs, the proposed neural networks circuit can utilize the programmability of the memristor array to real -time process various sparse coding problems. Based on the proposed circuit, the module can process binary and grayscale image sparse coding, providing a circuit implementation platform for sparse coding tasks. The simulation results demonstrate that the processing speed of sparse coding is improved compared with the MATLAB simulation and the application robustness is enhanced.

关键词： Memristor Neural network Locally competitive algorithm Circuit design sparse coding

来源：评论

学校读者我要写书评

暂无评论

Exploring Structural Consistency in Graph Regularized Joint Spectral-Spatial sparse coding for Hyperspectral Image Classification

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第3期10卷 1151-1164页

作者： Liu, Changhong Zhou, Jun Liang, Jie Qian, Yuntao Li, Hanxi Gao, Yongsheng Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Australian Natl Univ Sch Engn Canberra ACT 0200 Australia Zhejiang Univ Coll Comp Sci Inst Artificial Intelligence Hangzhou 310027 Zhejiang Peoples R China Griffith Univ Sch Engn Brisbane Qld 4111 Australia

In hyperspectral image classification, both spectral and spatial data distributions are important in describing and identifying different materials and objects in the image. Furthermore, consistent spatial structures across bands can be useful in capturing inherent structural information of objects. These imply that three properties should be considered when reconstructing an image using sparse coding methods. First, the distribution of different ground objects leads to different coding coefficients across the spatial locations. Second, local spatial structures change slightly across bands due to different reflectance properties of various object materials. Finally and more importantly, some sort of structural consistency shall be enforced across bands to reflect the fact that the same object appears at the same spatial location in all bands of an image. Based on these considerations, we propose a novel joint spectral-spatial sparse coding model that explores structural consistency for hyperspectral image classification. For each band image, we adopt a sparse coding step to reconstruct the structures in the band image. This allows different dictionaries be generated to characterize the band-wise image variation. At the same time, we enforce the same coding coefficients at the same spatial location in different bands so as to maintain consistent structures across bands. To further promote the discriminating power of the model, we incorporate a graph Laplacian sparsity constraint into the model to ensure spectral consistency in the dictionary generation step. Experimental results show that the proposed method outperforms some state-of-the-art spectral-spatial sparse codingmethods.

关键词： Graph Laplacian regularizer hyperspectral image sparse coding structural consistency

来源：评论

学校读者我要写书评

暂无评论

An Energy-Quality Scalable STDP Based sparse coding Processor With On-Chip Learning Capability

引用

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020年第1期14卷 125-137页

作者： Kim, Heetak Tang, Hoyoung Choi, Woong Park, Jongsun KETI SoC Platform Res Ctr Seongnam 13509 Gyeonggi Do South Korea Korea Univ Sch Elect Engn Seoul 136701 South Korea Sookmyung Womens Univ Dept Elect Engn Seoul 140742 South Korea

Two main bottlenecks encountered when implementing energy-efficient spike-timing-dependent plasticity (STDP) based sparse coding, are the complex computation of winner-take-all (WTA) operation and repetitive neuronal operations in the time domain processing. In this article, we present an energy-efficient STDP based sparse coding processor. The low-cost hardware is based on the algorithmic reduction techniques as following: First, the complex WTA operation is simplified based on the prediction of spike emitting neurons. Sparsity based approximation in spatial and temporal domain are also efficiently exploited to remove the redundant neurons with negligible algorithmic accuracy loss. We designed and implemented the hardware of the STDP based sparse coding using 65nm CMOS process. By exploiting input sparsity, the proposed SNN architecture can dynamically trade off algorithmic quality for computation energy (up to 74%) for Natural image (maximum 0.01 RMSE increment) and MNIST (no accuracy loss) applications. In the inference mode of operations, the SNN hardware achieves the throughput of 374 Mpixels/s and 840.2 GSOP/s with the energy-efficiency of 781.52 pJ/pixel and 0.35 pJ/SOP.

关键词： Neuromorphic system on-chip learning sparse coding spiking neural network spike timing dependent plasticity (STDP)

来源：评论

学校读者我要写书评

暂无评论

Joint spatial-angular sparse coding for dMRI with separable dictionaries

引用

MEDICAL IMAGE ANALYSIS 2018年 48卷 25-42页

作者： Schwab, Evan Vidal, Rene Charon, Nicolas Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA

Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers in the brain, in vivo, by measuring water diffusion along angular gradient directions in q-space. High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tensor imaging, but the high number of samples needed to estimate diffusivity requires longer patient scan times. To accelerate dMRI, compressed sensing (CS) has been utilized by exploiting a sparse dictionary representation of the data, discovered through sparse coding. The sparser the representation, the fewer samples are needed to reconstruct a high resolution signal with limited information loss, and so an important area of research has focused on finding the sparsest possible representation of dMRI. Current reconstruction methods however, rely on an angular representation per voxel with added spatial regularization, and so, for non-zero signals, one is required to have at least one non-zero coefficient per voxel. This means that the global level of sparsity must be greater than the number of voxels. In contrast, we propose a joint spatial-angular representation of dMRI that will allow us to achieve levels of global sparsity that are below the number of voxels. A major challenge, however, is the computational complexity of solving a global sparse coding problem over large-scale dMRI. In this work, we present novel adaptations of popular sparse coding algorithms that become better suited for solving large-scale problems by exploiting spatial-angular separability. Our experiments show that our method achieves significantly sparser representations of HARDI than is possible by the state of the art. (C) 2018 Elsevier B.V. All rights reserved.

关键词： sparse coding Separable dictionaries Kronecker product Diffusion MRI

来源：评论

学校读者我要写书评

暂无评论

Adaptive Multiple Subtraction Based on sparse coding

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2017年第3期55卷 1318-1324页

作者： Liu, Jinlin Lu, Wenkai Zhang, Yingqiang Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Dept Automat Beijing 100084 Peoples R China

Multiple removal is one of the key steps in seismic data processing. In the surface-related multiple elimination method, the adaptive multiple subtraction technique is of great importance. In this paper, we propose a new pattern-based adaptive multiple subtraction method using the sparse coding technique (AMS-SC). By assuming that the multiples consist of different patterns from those of the primaries in the time-space domain, the proposed method first obtains some basis vectors, which represent the patterns of the multiples compactly, from the predicted multiples by sparse coding, and then estimates the multiples contained in the recorded seismic data using these basis vectors obtained in the previous step. Different from the traditional matching filter methods, which estimate the multiples by fitting the predicted multiples to the recorded seismic data directly, AMS-SC obtains the multiple estimations by reconstructing the recorded seismic data with the basis vectors obtained from the predicted multiples. Benefitting from sparse coding, AMS-SC is robust to the differences between the predicted and the true multiples, and preserves the primaries well. Applications on several data sets give some promising results of AMS-SC.

关键词： Adaptive multiple subtraction basis vectors sparse coding

来源：评论

学校读者我要写书评

暂无评论

Matching Pursuit and sparse coding for Auditory Representation

引用

IEEE ACCESS 2021年 9卷 167084-167095页

作者： Tran, Dung Kim Unoki, Masashi Japan Adv Inst Sci & Technol Grad Sch Adv Sci & Technol Nomi Ishikawa 9231292 Japan

Previous studies have revealed that by mimicking the neural activity patterns of the auditory periphery to obtain perceptual features of speech signals, the resultant auditory representation is beneficial to speech-coding and pattern-analysis applications in comparison with spectrogram and spikegram representations. However, current solutions use outdated techniques such as the Bark scale and gammatone basis to decompose speech signals. We propose a method of using more physiological accurate techniques such as the equivalent rectangular bandwidth scale, gammachirp basis, and auditory masking effects of gammachirp kernels. Our experimental results indicate that the auditory representation created with our proposed method requires the lowest bitrate (1066 coefficients per second on average) to achieve similar perceptual evaluation scores (0.89 PEMO-Q and 3.27 PESQ scores) compared with spectrogram and spikegram representations. The proposed method also provides the highest matching accuracy with a pattern-matching algorithm.

关键词： Kernel Time-frequency analysis Spectrogram Matching pursuit algorithms Bandwidth Dictionaries Psychoacoustics Auditory filterbank equivalent rectangular bandwidth gammatone gammachirp masking effect matching pursuit perceptual features sparse coding spectrogram spikegram

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：