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检索条件"任意字段=2017 Conference on Design and Architectures for Signal and Image Processing, DASIP 2017"

共 891 条记录，以下是391-400 订阅

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GLOBAL AND LOCALLY ADAPTIVE WARPED MOTION COMPENSATION IN VIDEO COMPRESSION 24

GLOBAL AND LOCALLY ADAPTIVE WARPED MOTION COMPENSATION IN VI...

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24th IEEE International conference on image processing (ICIP)

作者： Parker, Sarah Chen, Yue Barker, David de Rivaz, Peter Mukherjee, Debargha Google Inc Mountain View CA 94043 USA Argon Design Cambridge England

ISBN: (纸本)9781509021758

Real motion is often complex, and cannot always be sufficiently described by a translational model for the purpose of video coding. Translational motion models can only map a rectangular to a rectangular of the same size in a reference frame, but real motion observed in videos often go beyond that. For example, motion due to camera shake, panning and zoom might require transformations that support shearing, scaling, rotation and changes in aspect ratio. In order to handle these instances adequately for video encoding, we introduce two coding modes - a global motion mode and a locally adaptive warped motion mode, that make use of affine and/or homographic projections to more accurately describe and predict complex motion. These warped modes respectively capture global motion computed at the frame level, or local motion computed at the block level based on local motion statistics. We pay special attention to efficient SIMD friendly decoder-side warping operations to make these tools practically useful. Together, these techniques prove to yield considerable coding gains on standard test sets.

关键词： AV1 motion compensation homography warped motion global motion

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Enhance satellite image classification based on fuzzy clustering and Marr-Hildreth algorithm 4

Enhance satellite image classification based on fuzzy cluste...

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4th IEEE International conference on signal processing, Computing and Control, ISPCC 2017

作者： Kaur, Rupinder Sharma, Dolly Verma, Amit Department of Computer Science Chandigarh Group of Colleges Landran MohaliPunjab India

ISBN: (纸本)9781509058389

The Satellite image classification is a significant method recycled in remote detecting for the automated study and structure recognition of the satellite data, which facilitate the automated understanding of the huge amount of data or information. These days, there happen the various kinds of classification methods, such as parallelepiped and less distance classifiers, but it is static essential to get better their presentation in relations of correctness rate. After existing work, we study the cross breed fuzzy, c-mean clustering algorithm and FFNN classifier for rocks cover planning of lands, shadow, construction. It initializes with the separate step pre-processing process to create the picture appropriate for segmentation. The processing of the image is refined using the cross-genetic-Artificial Bee Colony (ABC) algorithm that is implemented by cross breading the ABC and Fuzzy c-means to find the effective division in satellite image and categorized using NN. The presentation of the offered hybrid-algorithm is compared performance with the algorithms like, Artificial Bee Colony (ABC) algorithm, ABC-GA algorithm, Affecting KFCM. We implement the edge detection using Mar Hildreth Edge Detection Algorithm;Fuzzy c means Clustering using for segmentation of the satellite image, to extract the features used Principle component analysis, to optimize the extracted feature using Bacteria Foraging Algorithm and classification SVM (Support Vector Machine) to execute the satellite picture classification. In minimum time and evaluate the better accuracy based on the support vector machine algorithm. We design the framework in satellite images classifies uses MATLAB 2013a simulation tool and evaluate the performance in the Xdb-Index and DB-Index. © 2017 IEEE.

关键词： Clustering algorithms

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Determination of Benign and Malign Lesions by Fusion of The Different Phases of Liver MR 25

Determination of Benign and Malign Lesions by Fusion of The ...

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25th signal processing and Communications Applications conference (SIU)

作者： Ervural, Saim Ceylan, Murat KTO Karatay Univ Elekt Elekt Muhendisligi Bolumu Akabe Mh Alaaddin Kap Cd 130 Konya Turkey Selcuk Univ Elekt Elekt Muhendisligi Bolumu Alaeddin Keykubat Yerleskesi Konya Turkey

ISBN: (纸本)9781509064946

In this study, different phases of T1-weighted, dynamic contrast-enhanced liver magnetic resonance (MR) images were combined with wavelet-based image fusion to support decisions of radiologists. Used images has labelled as 6 different focal lesion types which focal nodular hyperplasia (FNH), hemangioma, cyst, colangiocellular carcinoma (CCC), hepatocellular carcinoma (HCC) and liver metastases. In application used images are taken by 4 different phases called pre-contrasted, arterial, portal venous, and delay venous from 30 patient. images registered with efficient subpixel registration by cross correlation method. Discrete wavelet transform(DWT) based image fusion algorithm used and maximum selection method applied as fusion rule. As result 180 fused images obtained The performances of fusion results compared with structural similarity index (SSIM), peak to noise ratio (PSNR) and fusion factor (FF) metrics. In the fusion of portal venous phase and delay venous phase images, 98.7% SSIM and 74.95 dB PSNR values were obtained, respectively. FF value in the fusion of pre-contrast phase & arterial phase images measured as 7.258. In comparison of lesion types were represented with 98.5% SSIM

关键词： image fusion liver MR imaging wavelet transform

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Comparative Analysis of Hyperspectral Feature Extraction Methods in Vegetation Classification 25

Comparative Analysis of Hyperspectral Feature Extraction Met...

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25th signal processing and Communications Applications conference (SIU)

作者： Ocal, Mertalp Ergun, Kazim Akar, Gozde Bozdagi Orta Dogu Tekn Univ Elekt Elekt Muhendisligi Ankara Turkey

ISBN: (纸本)9781509064946

To perform an accurate vegetation classification in hyperspectral data, feature extraction process prior to classification is very important. Success rates of classifiers in vegetation are rather limited compared to classification of other types of materials. Therefore, it is required to perform an effective feature extraction before classification. Principle Component Analysis(PCA) is a common and easily applicable method for this purpose. However, PCA is not an optimal method for distinguishing between different plant species. In this study, the reasons for PCA not being an adequate method for this purpose arc discussed and alternative useful feature extraction methods in classification of plant species are examined. Tests were performed for Spectrally Segmented PCA(SSPCA), Discrete Wavelet Transform(DWT) and Genetic Algorithm(GA) feature extraction methods, their effects on classifier performances were compared and it was observed that all of the mentioned alternatives had noticable improvements in classification performances.

关键词： hyperspectral imaging feature extraction remote sensing vegetation hyperspectral image classification principal component analysis

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Linear Periodically Time-Varying (LPTV) Circuits Enable New Radio architectures for Emerging Wireless Communication Paradigms: Extended Abstract: Invited 17

Linear Periodically Time-Varying (LPTV) Circuits Enable New ...

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54th Annual design Automation conference, DAC 2017

作者： Reiskarimian, Negar Zhang, Linxiao Krishnaswamy, Harish Columbia University 1300 SW Mudd 500 W 120th St New YorkNY10027 United States

ISBN: (纸本)9781450349277

The next generation of cellular wireless communication networks (the much hyped "5G") is targeting a 1000x increase in data capacity. This has sparked an investigation of new and transformative wireless communication paradigms, including massive MIMO, full duplex and millimeter-wave wireless. These new wireless paradigms place requirements on the radio circuitry that are orders of magnitude more challenging than traditional systems, forcing us to rethink conventional radio design. Conventional analog and radio frequency circuit design has relied on linear, time-invariant (LTI) components and circuits. However, LTI components and circuits are restricted in the signal processing functionalities that can be implemented. Recently, there has been significant interest in linear, periodically time varying (LPTV) circuits that can enable new functionalities and components, such as highly-Tunable, high quality integrated filters, front-ends with spatio-spectral filtering capability and integrated non-magnetic non-reciprocal components such as circulators and isolators. This paper reviews recent research breakthroughs in LPTV circuits and systems that enable full-duplex and massive MIMO wireless. © 2017 ACM.

关键词： MIMO systems

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Non-local Color image Denoising with Convolutional Neural Networks 30

Non-local Color Image Denoising with Convolutional Neural Ne...

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30th IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lefkimmiatis, Stamatios Skolkovo Inst Sci & Technol Skoltech Moscow Russia

ISBN: (纸本)9781538604571

We propose a novel deep network architecture for grayscale and color image denoising that is based on a non-local image model. Our motivation for the overall design of the proposed network stems from variational methods that exploit the inherent non-local self-similarity property of natural images. We build on this concept and introduce deep networks that perform non-local processing and at the same time they significantly benefit from discriminative learning. Experiments on the Berkeley segmentation dataset, comparing several state-of-the-art methods, show that the proposed non-local models achieve the best reported denoising performance both for grayscale and color images for all the tested noise levels. It is also worth noting that this increase in performance comes at no extra cost on the capacity of the network compared to existing alternative deep network architectures. In addition, we highlight a direct link of the proposed non-local models to convolutional neural networks. This connection is of significant importance since it allows our models to take full advantage of the latest advances on GPU computing in deep learning and makes them amenable to efficient implementations through their inherent parallelism.

关键词： image restoration image denoising Linear programming Color Noise reduction Computational modeling Machine learning

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2017 IEEE Global conference on signal and Information processing, GlobalSIP 2017 - Proceedings

2017 IEEE Global Conference on Signal and Information Proces...

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5th IEEE Global conference on signal and Information processing, GlobalSIP 2017

ISBN: (纸本)9781509059904

The proceedings contain 281 papers. The topics discussed include: transform domain distributed video coding using larger transform blocks;image error concealment based on joint sparse representation and non-local similarity;2D visual tracking for sports UAV cinematography applications;cascaded image deblurring with combined image prior;sparse unmixing based on feature pixels for hyperspectral imagery;a linear regression framework for assessing time-varying subjective quality in http streaming;dynamic range expansion of single images using intensity-invariant patch correspondences;a new approach for robust replay spoof detection in ASV systems;I-vector Kullback-Leibler divisive normalization for PLDA speaker verification;influence of audio bandwidth on speech emotion recognition by human subjects;closed-form solution to directly design frequency modulated waveforms for beampatterns;efficient construction of polyphase sequences with optimal peak sidelobe level growth;multiband TDOA estimation from sub-Nyquist samples with distributed wideband sensing nodes;combinatorial multi-armed bandit problem with probabilistically triggered arms: a case with bounded regret;robust multidimensional scaling employing m-estimators and nuclear norm regularization;color image segmentation using iterative edge cutting, NUV-EM, and Gaussian message passing;and robust estimation of the sample mean variance for Gaussian processes with long-range dependence.

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design of High Secure Visual Secret Sharing Scheme for Gray Scale images 2

Design of High Secure Visual Secret Sharing Scheme for Gray ...

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2nd IEEE International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Ratnam, Javvaji V. K. Reddy, P. Ramana Reddy, T. Sreenivasulu Jawaharlal Nehru Technol Univ Anantapur Dept Elect & Commun Engn Ananthapuramu Andhra Prades India Sri Venkateswara Univ Dept Elect & Commun Engn Tirupati Andhra Prades India

ISBN: (纸本)9781509044429

In this paper, a (k, n) visual secret sharing scheme using Boolean and shift operations that provides high security to the secret image is designed. An algorithm is proposed to encode the original secret image to generate n share images using simple Boolean XOR and circular shift operations. The secret data cannot be revealed with any k-1 or less number of share images. The security is provided to the original secret by encrypting this secret with a random image and distinct authentication id used for each share during generation of shares. The size of generated share images is same as that of original image and requires no pixel expansion. The proposed scheme eliminates security issues observed in the related techniques. Experimental results confirm the feasibility and security of the proposed technique.

关键词： visual cryptography visual secret sharing Boolean XOR circular shift security

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International conference on Systems, signals, and image processing

International Conference on Systems, Signals, and Image Proc...

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24th International conference on Systems, signals and image processing, IWSSIP 2017

ISBN: (纸本)9781509063444

The proceedings contain 55 papers. The topics discussed include: real-time traffic sign recognition using color segmentation and SVM;improving matching performance of the keypoints in images of 3D scenes by using depth information;temporal enhancement of graph-based depth estimation method;stochastic spectral-spatial permutation ordering combination for nonlocal morphological processing;signal analysis on the ball: design of optimal basis functions with maximal multiplicative concentration in spatial and spectral domains;local statistics estimation for rapid frequency selective extrapolation;and motion artifacts free image resolution enhancement exploiting image priors.

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SINGLE image SUPER-RESOLUTION WITH DILATED CONVOLUTION BASED MULTI-SCALE INFORMATION LEARNING INCEPTION MODULE 24

SINGLE IMAGE SUPER-RESOLUTION WITH DILATED CONVOLUTION BASED...

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24th IEEE International conference on image processing (ICIP)

作者： Shi, Wuzhen Jiang, Feng Zhao, Debin Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9781509021758

Traditional works have shown that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Make full use of these multi-scale information can improve the image restoration performance. However, the current proposed deep learning based restoration methods do not take the multi-scale information into account. in this paper, we propose a dilated convolution based inception module to learn multi-scale information and design a deep network for single image super-resolution. Different dilated convolution learns different scale feature, then the inception module concatenates all these features to fuse multi-scale information. In order to increase the reception field of our network to catch more contextual information, we cascade multiple inception modules to constitute a deep network to conduct single image super-resolution. With the novel dilated convolution based inception module, the proposed end-to-end single image super resolution network can take advantage of multi-scale information to improve image super-resolution performance. Experimental results show that our proposed method outperforms many state-of-the-art single image super-resolution methods.

关键词： image super-resolution convolutional neural network multi-scale information dilated convolution inception module

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