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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261-270 订阅

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Cambricon-S: Addressing Irregularity in Sparse Neural Networks through A Cooperative Software/Hardware Approach 51

Cambricon-S: Addressing Irregularity in Sparse Neural Networ...

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51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Zhou, Xuda Du, Zidong Guo, Qi Liu, Shaoli Liu, Chengsi Wang, Chao Zhou, Xuehai Li, Ling Chen, Tianshi Chen, Yunji Univ Sci & Technol China Hefei Anhui Peoples R China Cambricon Technol Co Ltd Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Intelligence Processor Res Ctr Beijing Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China CAS Ctr Excellence Brain Sci Beijing Peoples R China Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781538662403

Neural networks have become the dominant algorithms rapidly as they achieve state-of-the-art performance in a broad range of applications such as image recognition, speech recognition and natural language processing. However, neural networks keep moving towards deeper and larger architectures, posing a great challenge to the huge amount of data and computations. Although sparsity has emerged as an effective solution for reducing the intensity of computation and memory accesses directly, irregularity caused by sparsity (including sparse synapses and neurons) prevents accelerators from completely leveraging the benefits;it also introduces costly indexing module in accelerators. In this paper, we propose a cooperative software/hardware approach to address the irregularity of sparse neural networks efficiently. Initially, we observe the local convergence, namely larger weights tend to gather into small clusters during training. Based on that key observation, we propose a software-based coarse-grained pruning technique to reduce the irregularity of sparse synapses drastically. The coarse-grained pruning technique, together with local quantization, significantly reduces the size of indexes and improves the network compression ratio. We further design a hardware accelerator, Cambricon-S, to address the remaining irregularity of sparse synapses and neurons efficiently. The novel accelerator features a selector module to filter unnecessary synapses and neurons. Compared with a state-of-the-art sparse neural network accelerator, our accelerator is 1.71x and 1.37x better in terms of performance and energy efficiency, respectively.

关键词： Synapses Neurons image coding Quantization (signal) Convergence Biological neural networks

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Efficient FDWT/IDWT Hardware Implementation with Line-based and Dual-scan image Memory Accesses

Efficient FDWT/IDWT Hardware Implementation with Line-based ...

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IEEE Region 10 conference (TENCON)

作者： Saad, Laila Ahmed Salleh, Mohd Fadzli Mohd Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Pulau Pinang Malaysia

ISBN: (纸本)9781509011346

image compression is of a great importance in multimedia system applications because it drastically reduces bandwidth for transmission and memory storage. image compression algorithm, like JPEG2000, utilizes the Forward Discrete Wavelet Transform (FDWT) and Inverse Discrete Wavelet Transform (IDWT). The main problems face by researchers in the hardware implementation of the FDWT/IDWT are storage memory, internal processing buffer and the limitation of the FPGA resources. Memory access method significantly affects the design architecture of FDWT/IDWT. The off-chip memory access consumes more power while the on-chip memory increases the hardware cost. In this paper, we propose an FDWT/IDWT architecture that is based on the line-based and dual-scan methods. Both architectures reduce the number of image memory accesses. The line-based architecture reduces the internal buffer by 2 x 0.5 x N where N is the dimension of image size. The implemented dual-scan architecture does not use any the internal memory. Both architectures were implemented on the Altera Cyclone II EP2C70F672C6 FPGA board and ran at frequency of 100 MHz.

关键词： Haar Wavelet Transform Filter Banks 2-D FDWT/IDWT Multilevel decomposition FPGA

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Ground Moving Target Positioning with Doppler Ambiguity and Scene Ambiguity in SAR 40

Ground Moving Target Positioning with Doppler Ambiguity and ...

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40th International conference on Telecommunications and signal processing (TSP)

作者： Liu, Qiongxiao Wang, Zhirui Xia, Wei Chen, Bing China Ship Dev & Design Ctr Wuhan 430064 Hubei Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781509039821

A synthetic aperture radar (SAR) image is often obtained based on a part of intercepted echo from the collected raw data in the azimuth direction. It is discovered that the azimuth length of a SAR image has an effect on the target position in the image domain. For a SAR image with a large azimuth length, the azimuth shift is the distance between the original position and the final position of a moving target in the image domain, which is proportional to the target's ambiguous range velocity and is directly influenced by the Doppler ambiguity. However, for a SAR image with a limited azimuth length, the target position after azimuth shift may be beyond the boundary of the image and its final position is wrapped, which is called the scene ambiguity in this paper. When the Doppler ambiguity and the scene ambiguity both occur to one moving target, the positioning is complicated. To solve this problem, the relationship between the target's original position and final position is addressed in detail in a SAR image with any azimuth size. Then, a general positioning solution is proposed. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed method.

关键词： Doppler ambiguity ground moving target scene ambiguity synthetic aperture radar (SAR) target positioning

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A Comparative Analysis of Visual and Thermal Face image Fusion based on Different Wavelet Family 3

A Comparative Analysis of Visual and Thermal Face Image Fusi...

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International conference on Innovations in Electronics, signal processing and Communication (IESC)

作者： Pal, Anamika Rudra Singha, Anu Tripura Univ Dept Comp Sci & Engn Agartala India

ISBN: (纸本)9781509056200

To diminish the limitations of the visual and thermal face images, a fusion of them is carried out in order to upgrade the performance. This paper presents fusion based on maximum selection of DB4 wavelet coefficients of visual and thermal images. The dimensions of these fused images are reduced using ICA and these low dimension fused images are classified with the help of SVM. The experimental result of fusion gives approximately 2% and 6% more accuracy than the visual and thermal images respectively for both expression and illumination of IRIS datasets. Further this work is extended with a comparative study of fusion methods based on some other wavelet decompositions such as Haar/DB2, Coiflets, and Symlets. In addition to that, a comparison between two ICA architectures and PCA is also carried out over wavelet fusion methods. It has been noticed that maximum information, classification accuracy, and less error acheieved for haar/db2 fused images.

关键词： DWT ICA PCA Information Measurement Fusion

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A New approach to image processing in diffuse optical tomography and 3-D image 25

A New approach to image processing in diffuse optical tomogr...

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25th signal processing and Communications Applications conference (SIU)

作者： Uncu, Yigit Ali Mercan, Tanju Canpolat, Murat Sevim, Gencay Akdeniz Univ Tip Fak Biyofiz ABD Antalya Turkey Anadolu Univ Fen Bilimler Enstitusu Nanoteknol Eskisehir Turkey

ISBN: (纸本)9781509064946

In recent years, breast cancers have been diagnosed by several imaging modalities. One of these imaging methods is the diffuse optical tomography (DOT) system. In the presented study, in-vitro data were acquired from a breast phantom using the our designed DOT system. Breast phantoms were reconstructed in three dimensions (3D) using Truncated Conjugate Gradient (TCG) reconstruction algorithm. Then, image processing methods were applied to the raw images. image segmentation technique was used to distinguish between "object" and "backgrounds". Later, 3D Gaussian filtering was applied to smooth the image and reduce noise. Finally, a three dimensional interpolation was applied to the noise free images in order to obtain a more realistic 3D image of the tissue phantoms. Thus, we have modified most popular image processing methods to apply the DOT images. As a result, we obtained user friendly, fast, accurate and user independent images processing methods for the DOT systems.

关键词： Diffuse Optical Tomography image processing Interpolation image Segmentation Gaussian filter 3-D imaging

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Block Size Effect on image Compression Using SYMPES Method 25

Block Size Effect on Image Compression Using SYMPES Method

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25th signal processing and Communications Applications conference (SIU)

作者： Meskoob, Behnoosh Kouhalvandi, Lida Yarman, Binboga Siddik Istanbul Tech Univ Dept Elect & Commun Engn Istanbul Turkey Istanbul Univ Dept Elect & Elect Engn Istanbul Turkey

ISBN: (纸本)9781509064946

One of the aims of image compression, is decreasing unnecessary and redundant image data for easier storage and transmission in an effective way, taking less storage capacity and less transmission bandwidth. While the compression shall result in least information loss and maximum similarity to the original image. By using Systematic Procedure for Predefined Envelope and Signature Sequences (SYMPES) method, this paper shows the effect of block size on processing parameters such as Mean Square Error (MSE), Peak signal to Noise Ratio (PSNR), Compression Ratio (CR), etc. while other parameters such as image size, total number of signature, envelope and coefficient functions and other parameters are kept fixed and without change. Block size and the effect of block size on quality/distortion metrics is investigated. The advantage of this study is proving the flexibility of SYMPES method towards block sizes but up to a certain point where coefficient intensity is not lost and signature intensity has no negative effect on envelope intensity. Finally, increasing block size up to a certain point, can result to higher compression rate and better PSNR. Therefore, the best block size can be achieved to have most desirable result for image compression.

关键词： SYMPES Mean Square Error Peak signal to Noise Ratio Compression Ratio Signature Envelope

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LARGE RECEPTIVE FIELD CONVOLUTIONAL NEURAL NETWORK FOR image SUPER-RESOLUTION 24

LARGE RECEPTIVE FIELD CONVOLUTIONAL NEURAL NETWORK FOR IMAGE...

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24th IEEE International conference on image processing (ICIP)

作者： Wang, Qiang Fan, Huijie Cong, Yang Tang, Yandong Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Grad Univ Beijing 100049 Peoples R China

ISBN: (纸本)9781509021758

This paper presents a new approach to Single image Super Resolution (SISR), based upon Convolutional Neural Network (CNN). Although the SISR is ill-posed which can be seen as finding a non-linear mapping from a low to high dimensional space. Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration and non-linear mapping problems. We consider the single image Super-Resolution (SR) problem as convolution operators and develop a CNN to capture the characteristics of Low-Resolution (LR) input image. We find that increasing the receptive field shows the improvement in accuracy. Our solution is to establish the connection between traditional optimization-based schemes and neural network architectures. In the paper a novel, separable structure is introduced as a reliable support for robust convolution against artifacts. Our proposed method performs better than existing methods in terms of accuracy and visual improvements in our results are easily noticeable.

关键词： Super resolution Convolutional neural network Receptive field Multi-scale

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Demo: MPPA® manycore processor towards future ADAS system solutions High-computing platform for next generation vehicles

Demo: MPPA® manycore processor towards future ADAS system s...

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conference on design and architectures for signal and image processing (dasip)

作者： Beaucamps, Pierre-Edouard Blanc, Frederic KALRAY Grenoble France

This document describes a demonstration, proposed at dasip Demo Night session. This demonstration is running on a low-power manycore processor from Kalray, named MPPA (R) and is highlighting capabilities of this new g... 详细信息

ISBN: (纸本)9791092279153

关键词： manycore MPPA (R) processor image processing signal processing low-power standalone ADAS

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CAMERA MODEL IDENTIFICATION WITH RESIDUAL NEURAL NETWORK 24

CAMERA MODEL IDENTIFICATION WITH RESIDUAL NEURAL NETWORK

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24th IEEE International conference on image processing (ICIP)

作者： Chen, Yunshu Huang, Yue Ding, Xinghao Xiamen Univ Sch Informat Sci & Engn Xiamen Peoples R China

ISBN: (纸本)9781509021758

With the development of multimedia, camera model identification from given images has attract large attentions in cyber-forensic area recently. The task has achieved a great improvement due to some deep learning methods, where the features are extracted with the stacked architectures. However, it should be considered that both low-level and high-level features have contributions to the recognition. In this paper, we investigate the task with another deep learning model, residual neural network (ResNet). Proposed framework has been evaluated on the experiments of brand-attribution, model-attribution and device-attribution. Besides, we also include cell phone model identification in the brand-attribution experiment for the first time. The classification results have demonstrated that the proposed work has the ability of enhancing the identification performances compared with existing methods in each specific task. The proposed work can be considered as an effective approach on image forensics.

关键词： camera model identification image forensics deep learning ResNet

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DATA DRIVEN CODED APERTURE design FOR DEPTH RECOVERY 24

DATA DRIVEN CODED APERTURE DESIGN FOR DEPTH RECOVERY

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24th IEEE International conference on image processing (ICIP)

作者： Shedligeri, Prasan A. Mohan, Sreyas Mitra, Kaushik Indian Inst Technol Dept Elect Engn Madras Tamil Nadu India

ISBN: (纸本)9781509021758

Inserting a patterned occluder at the aperture of a camera lens has been shown to improve the recovery of depth map and all-focus image compared to a fully open aperture. However, design of the aperture pattern plays a very critical role. Previous approaches for designing aperture codes make simple assumptions on image distributions to obtain metrics for evaluating aperture codes. However, real images may not follow those assumptions and hence the designed code may not be optimal for them. To address this drawback we propose a data driven approach for learning the optimal aperture pattern to recover depth map from a single coded image. We propose a two stage architecture where, in the first stage we simulate coded aperture images from a training dataset of all-focus images and depth maps and in the second stage we recover the depth map using a deep neural network. We demonstrate that our learned aperture code performs better than previously designed codes even on code design metrics proposed by previous approaches.

关键词： Coded Aperture design Optimal Code Depth from Defocus

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