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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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Lineament Extraction from Open-Source Digital Elevation Models: a Comparative Analysis

Lineament Extraction from Open-Source Digital Elevation Mode...

引用

IEEE distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Swathi Shetty Pruthviraj Umesh Amba Shetty Dept. of Water Resources and Ocean Engineering NITK Surathkal Mangaluru India

ISBN: (纸本)9781665429528

The extraction of lineaments from Digital Elevation Models plays an important role in inhospitable and inaccessible mountain forest areas. In this study, the lineaments extracted from different data acquisition techniques; stereo pairs (ALOS (30m), ASTER (30m), CARTOSAT (30m)), and InSAR (SRTM (30m, 90m), TanDEM-X (90m)) are compared. There is a quantifiable difference in the extracted lineaments from 30m and 90m resolution DEMs due to the different data acquisition methods and processing algorithms used. CARTOSAT provides a more number of lineaments than other DEMs. The length of the lineaments extracted is inversely proportional to the vertical accuracy of the DEM over the region. All the DEMs have a consistency in the orientation of the lineament extracted. The DEMs generated from stereo-images have shown higher lineament density than the DEMs acquired through the InSAR technique. This study shows the difference in the lineament extracted from the same resolution DEMs acquired through various acquisition techniques and helps in the selection of suitable DEM for lineament extraction in the dense forest area.

关键词： image edge detection Data acquisition Process control Forestry Very large scale integration Data mining Integrated circuit modeling

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Optimizing Cellular Automata Execution by distributed Discrete Event Simulation Techniques

Optimizing Cellular Automata Execution by Distributed Discre...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Andrea Giordano Donato D’Ambrosio Rocco Rongo William Spataro Alessio De Rango ICAR-CNR Rende Cosenza Italy Department of Matematics and Computer Science University of Calabria Rende Cosenza Italy Department of Environmental Engineering University of Calabria Rende Cosenza Italy

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Cellular Automata (CA) are parallel models well-suited for studying complex systems that are based on local rules of evolution. Notable examples of application are found in fluid-dynamics, crowd simulation, flow-simulation and many more. Nevertheless, CA can be fruitfully exploited as a support in numerical approaches, such as finite element and finite volume methods. Though easily parallelizable by domain partitioning among the nodes of a parallel system, the performance and scalability of cellular automata executed on parallel/distributed machines are limited due to the need of synchronizing nodes at each computational step. With the aim of reducing the synchronization burden, we here present a preliminary study on techniques stemmed from the Discrete-Event Simulation field for the optimization of CA on distributed memory architectures. Preliminary results, executed in a distributed memory environment, have shown the usefulness of the considered approach in reducing execution times and therefore in improving the speed up of the parallel execution of the test case.

关键词： Computational modeling Automata Synchronization Numerical models Optimization Memory architecture Mathematical model

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AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FOR image COMPRESSIVE SENSING 26

AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FO...

引用

26th IEEE International conference on image processing (ICIP)

作者： Liu, Renhe Li, Sumei Hou, Chunping Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781538662496

Recently, deep-learning based reconstruction methods have been proposed to improve recovery performance of compressive sensed image and overcome expensive time complexity drawbacks of iteration-based traditional algorithms. In this paper, we propose an end-to-end multi-scale residual convolutional neural network (CNN), dubbed MSRNet, to simulate image compressive sensing (CS) and inverse reconstruction process in real situation. In the reconstruction stage of MSRNet, we apply three parallel channels with different convolution kernel sizes to exploit different-scale feature information. Besides, residual learning is introduced to accelerate training process and enhance prediction accuracy of network. Moreover, different from generating CS measurements by random measurement matrix in previous methods, we integrate compressive sample process into MSRNet, which means measurement matrix can be adaptively learned by training the network. Experiments on benchmark datasets show our method outperforms other state-of-the-art algorithms by large margins and set a new level for CS reconstruction with competitive time complexity.

关键词： image compressive sensing convolutional neural network reconstruction multi-scale end-to-end

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Combining Global Sparse Gradients with Local Gradients in distributed Neural Network Training 9

Combining Global Sparse Gradients with Local Gradients in Di...

引用

conference on Empirical methods in Natural Language processing / 9th International Joint conference on Natural Language processing (EMNLP-IJCNLP)

作者： Aji, Alham Fikri Heafield, Kenneth Bogoychev, Nikolay Univ Edinburgh Sch Informat 10 Crichton St Edinburgh EH8 9AB Midlothian Scotland

ISBN: (纸本)9781950737901

One way to reduce network traffic in multinode data-parallel stochastic gradient descent is to only exchange the largest gradients. However, doing so damages the gradient and degrades the model's performance. Tranformer models degrade dramatically while the impact on RNNs is smaller. We restore gradient quality by combining the compressed global gradient with the node's locally computed uncompressed gradient. Neural machine translation experiments show that Transformer convergence is restored while RNNs converge faster. With our method, training on 4 nodes converges up to 1.5x as fast as with uncompressed gradients and scales 3.5x relative to singlenode training.

关键词： Restoration

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Correlative Analysis of Denoising methods in Spectral images Embedded with Different Noises

Correlative Analysis of Denoising Methods in Spectral Images...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Sangeetha Annam Anshu Singla Chitkara University Punjab India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

Digital image is one of the primary way of communication in the present digital world. During the acquiring process, the images may become noisy. Noise reduction is a demanding task during the image analysis process without dissimilating the important features. It is the procedure of restoring the original image by discarding unwanted noises and known as image denoising. The main intention of any noise removal technique is to completely eradicate the noise from the image, such that the resulting image is better than the original image. In this digital era, remote sensing images are widely commercial for environmental monitoring. In this study, a correlative analysis of different noise removal methods using various filters in spectral images is performed. Spectral images are introduced with different types of noise and further filters are applied to denoise the image. The performances of the methods are evaluated using benchmarks: Signal-to-Noise Ratio (SNR) and Peak Signal-to-N oise Ratio (PSNR). Experimental results demonstrate that the SNR and PSNR measures were comparatively higher for all the filters when the image is introduced with Poisson noise.

关键词： Wiener filters Nonlinear filters Maximum likelihood detection Adaptive filters Noise reduction image denoising Hyperspectral imaging

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Improvement of the Contour Evolution Efficiency Based on Local Active 20

Improvement of the Contour Evolution Efficiency Based on Loc...

引用

20th IEEE/ACIS International conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD 2019)

作者： Wei, Guan Huang Yen, Shih Huang Natl Chin Yi Univ Technol Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781728116518

Local active contour frames have been widely used for image segmentation, which provides reliable results for heterogeneous images. However, convergence time is still a problem. This paper proposes a method to accelerate LRBACM. In order to improve the convergence time, we add 1-Tree, edge stop function, gradient and frozen mature contour point acceleration convergence time based on LRB. The experimental results show that the method in this paper is shortened by 26 similar to 83%.

关键词： Active contours level set methods image segmentation KD-Tree

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Fast parallel Bounding Volume Hierarchy Construction

Fast Parallel Bounding Volume Hierarchy Construction

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Xiaozi Guo Juan Zhang Mingquan Zhou School of Computer Science Shaanxi Normal University Xi’an China School of Journalism and Communication Shaanxi Normal University Xi’an China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

Our algorithm optimized the parallel structure and speeded up the parallel construction based on the locally-ordered clustering to build the bounding volume hierarchy (BVH). We use the bottom-up approach to build the BVH, merging nearest clusters and subnearest clusters in each iteration. Our algorithm was verified in CUDA and evaluated in GPU ray tracing scenarios. Compared with other methods, our method is more efficient in building rate and effectively generates a more balanced BVH under complex scenarios.

关键词： BVH construction parallel optimization ray tracing Ray tracing bounding volume BVH parallel architectures parallel configuration algorithms Social Hierarchy iterative methods

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image Defogging Improvement Under Non-uniform Fog Concentration

Image Defogging Improvement Under Non-uniform Fog Concentrat...

引用

Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Dapeng Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9781728196688

ISBN: (纸本)9781728196695

Atmospheric scattering model is the main method of image defogging. Aiming at the defects of the existing defogging algorithms in different aspects, this paper puts forward some improvement measures based on the analysis of the defects of the existing defogging methods. In view of the irrationality of estimation of atmospheric light and atmospheric scattering coefficient, a method of locating atmospheric light region based on edge extraction and a method of selecting atmospheric scattering coefficient based on fog concentration are proposed. The estimation of depth of field and transmittance is improved. Finally, adaptive contrast enhancement is carried out for foggy image. The effectiveness of the proposed method is verified by comparative experiments.

关键词： Information science image segmentation Atmospheric measurements Atmospheric modeling image edge detection Scattering Estimation

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Multimedia Forensics - An approach to detect and analyze Human faces in multimedia files 5

Multimedia Forensics - An approach to detect and analyze Hum...

引用

5th International conference on image Information processing (ICIIP)

作者： Gowada, Raghavendra Pawar, Digambar Tetali, Satish Reddy Univ Hyderabad Sch CIS Hyderabad Telangana India

ISBN: (纸本)9781728108995

Manual Security is being replaced by video surveillance systems by the advent of multimedia forensics. Face detection is an important technique in multimedia forensics. Many proposed methods identify and localize the faces in an image or in an image sequence. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Since, innumerable video data that is collected from video surveillance are to be automatically processed and get valuable information out of it, we need to scale the processing framework accordingly. In this paper, we propose face recognition system and minimize the overall processing time of large data set using parallel processing method. Given a video from a video surveillance, we need to convert it to frames, detect the human faces in these frames and recognize the face by comparing with a given face or comparing against database entries where criminal data is present. We use cascade classifier that takes a vector of features (decision variables) and results the probability that the vector belongs to the class. Using cascade classifier, we identify the faces from a given image and finally compare the faces with database entries.

关键词： Video surveillance Multimedia forensics Face detection Face recognition parallel processing Cascade classifier Decision variables Spark streaming YARN Mapreduce

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Large-Scale Training System for 100-Million Classification at Alibaba 20

Large-Scale Training System for 100-Million Classification a...

引用

26th ACM SIGKDD International conference on Knowledge Discovery and Data Mining (KDD)

作者： Song, Liuyihan Pan, Pan Zhao, Kang Yang, Hao Chen, Yiming Zhang, Yingya Xu, Yinghui Jin, Rong Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781450379984

In the last decades, extreme classification has become an essential topic for deep learning. It has achieved great success in many areas, especially in computer vision and natural language processing (NLP). However, it is very challenging to train a deep model with millions of classes due to the memory and computation explosion in the last output layer. In this paper, we propose a large-scale training system to address these challenges. First, we build a hybrid parallel training framework to make the training process feasible. Second, we propose a novel softmax variation named KNN softmax, which reduces both the GPU memory consumption and computation costs and improves the throughput of training. Then, to eliminate the communication overhead, we propose a new overlapping pipeline and a gradient sparsification method. Furthermore, we design a fast continuous convergence strategy to reduce total training iterations by adaptively adjusting learning rate and updating model parameters. With the help of all the proposed methods, we gain 3.9x throughput of our training system and reduce almost 60% of training iterations. The experimental results show that using an in-house 256 GPUs cluster, we could train a classifier of 100 million classes on Alibaba Retail Product Dataset in about five days while achieving a comparable accuracy with the naive softmax training process.

关键词： Extreme Classification distributed Deep Learning KNN Softmax Communication Optimization Fast Convergence

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