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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是91-100 订阅

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An Adaptive BSCO Algorithm of Solid Color Optimization for 3D Reconstruction System with PIFuHD 17th

An Adaptive BSCO Algorithm of Solid Color Optimization for 3...

引用

17th International conference on Wireless algorithms, systems, and Applications (WASA)

作者： Hsieh, Chao-Hsien Song, Yubo Wang, Zhen Li, Changfeng Qufu Normal Univ Jining 273165 Shandong Peoples R China

ISBN: (纸本)9783031192135;9783031192142

PIFuHD can generate high-resolution model in the process of human 3D reconstruction. However, PIFuHD will produce debris outside the human body when the image background is more complex. In order to solve this problem, this paper develops an adaptive BSCO algorithm for the background of human body and image of human body in 3D reconstruction system. the BSCO algorithm is divided into four steps in processing. First, BSCO algorithm uses Go-selfies to separate the background. Second, BSCO algorithm converges the RGB of all pixels of the character into a set. third, BSCO algorithm finds the greatest difference from the set through HSV conversion. Fourth, BSCO algorithm weighs the set and then calculates the RGB score. the highest score of RGB is used as the RGB of the background after solid color optimization. the experimental results show that the proposed method improves the reconstruction effect of PIFuHD.

关键词： PIFuHD 3D reconstruction image processing Background adaptive

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Hardware Architecture for Advanced image processing

Hardware Architecture for Advanced Image Processing

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IEEE Nuclear Science Symposium (NSS)/Medical Imaging conference (MIC)/17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors

作者： Grabowski, Kamil Napieralski, Andrzej Tech Univ Lodz Dept Microelect & Comp Sci PL-90924 Lodz Poland

ISBN: (纸本)9781424491063

the computation speed offered by nowadays embedded systems allows combining advanced signal processing and data acquisition in dedicated architectures optimized for given applications. Such architectures can be used for 2D/3D signal analysis and reconstruction in such broad areas as: medical signal processing - tomography object reconstruction (USG, PET, CT, OCT, etc.), biomedical image analysis - automated object segmentation and diagnostic support, biometry - pattern recognition, etc. the technological advances in the scale of integration of integrated circuits as well as image acquisition and processing systems have played a major part in this trend. In particular, for medical image analysis common problems are content segmentation, analysis of selected objects, pattern estimation and storage. In the paper general architecture for advanced image processing is presented which can be adapted to specific needs, depending on intended use. this article describes a system for analysis of biometric data developed as part of the work conducted by the authors on the complete biometric identification system. thus, the article focuses mostly on the biometrical iris identification system (1:N), chosen as an example well suited to the validation of the developed system. Several issues concerning efficient data processing using Field Programmable Arrays and Digital Signal Processors are presented on the basis of the described architecture. algorithms computed on a desktop computer were adapted to this specialized, hardware-oriented architecture composed of Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGA). Obtained results are presented and the developed system is compared with some commercially-available solutions for iris recognition.

关键词： image analysis multicore processing biometrics iris recognition biomedical image processing

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APDPk-means: A new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation 21

APDPk-means: A new differential privacy clustering algorithm...

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21st IEEE International conference on High Performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Fan, Zexuan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security AND Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering algorithms focus on optimizing the selection of initial centroids. However, the traditional privacy budget allocation has the problem that the random noise becomes too large as the number of iterations increases, which will reduce the performance of data clustering. To solve the problem, we improved the way of privacy budget allocation in differentially private clustering algorithm DPk-means, and proposed APDPk-means, a new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation. APDPk-means decomposes the total privacy budget into a decreasing arithmetic progression, allocating the privacy budgets from large to small in the iterative process, so as to ensure the rapid convergence in early iteration. the experiment results show that compared with the other differentially private k-means algorithms, APDPk-means has better performance in availability and quality of the clustering result under the same level of privacy protection. © 2019 IEEE.

关键词： Data mining

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Attributed Relational SIFT-Based Regions Graph for Art Painting Retrieval

Attributed Relational SIFT-Based Regions Graph for Art Paint...

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17th International conference on image Analysis and processing (ICIAP)

作者： Manzo, Mario Petrosino, Alfredo Univ Naples Parthenope Ctr Direz Dept Sci & Technol Naples Italy

ISBN: (纸本)9783642411816;9783642411809

Recently, image retrieval and analysis algorithms have been extensively applied to art related domains. In this field, state-of-the-art approaches mainly focus on feature extraction with the aim of improving reliability of authentication, classification and retrieval of art paintings. In this paper we propose an effective modeling, based on a graph structure, and a retrieval strategy, based on a graph matching algorithm, for art paintings. the proposed approach has been tested on different datasets with high quality results allowing an user to run effective content-based queries on painting records.

关键词： CBIR systems graph matching graph based image representation local invariant features extraction

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GPU Implementation of Orthogonal Matching Pursuit for Compressive Sensing

GPU Implementation of Orthogonal Matching Pursuit for Compre...

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17th IEEE International conference on Parallel and Distributed systems (ICPADS)

作者： Fang, Yong Chen, Liang Wu, Jiaji Huang, Bormin NW A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian Shaanxi Peoples R China Univ Wisconsin Space Sci & Engn Ctr Madison WI USA

ISBN: (纸本)9780769545769

Recovery algorithms play a key role in compressive sampling (CS). Currently, a popular recovery algorithm for CS is the orthogonal matching pursuit (OMP), which possesses the merits of low complexity and good recovery quality. Considering that the OMP involves massive matrix/vector operations, it is very suited to being implemented in parallel on graphics processing unit (GPU). In this paper, we first analyze the complexity of each module in the OMP and point out the bottlenecks of the OMP lie in the projection module and the least-squares module. To speedup the projection module, Fujimoto's matrix-vector multiplication algorithm is adopted. To speedup the least-squares module, the matrix-inverse-update algorithm is adopted. Experimental results show that +40x speedup is achieved by our implementation of OMP on GTX480 GPU over on Intel(R) Core(TM) i7 CPU. Since the projection module occupies more than 2/3 of the total run time, we are looking for a faster matrix-vector multiplication algorithm.

关键词： compressive sampling recovery algorithm orthogonal matching pursuit graphics processing unit

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ResRnnNet: Learning to Reconstruct the 3D Room Layout from a Single RGB Panorama 17th

ResRnnNet: Learning to Reconstruct the 3D Room Layout from a...

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17th International conference on Intelligent Information Hiding and Multimedia Signal processing (IIH-MSP) / 14th International conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Zhao, Shaonan Li, Wei Univ New South Wales Sch Civil Engn & Environm Sydney NSW 2052 Australia Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9789811910579;9789811910562

3D room layout reconstruction from a single RGB panoramic image has been an emerging research topic in recent years. To achieve better prediction accuracy, in this paper, we propose a new approach to predict 3D room layout from a single panoramic image. Our reconstruction flow follows a common framework which is same as LayoutNet [9] and HorizonNet [4];however, we redesign a new deep learning architecture with recurrent neural networks (RNNs) encoder-decoder as an extension for keypoints refinement and use a gradient ascent optimization algorithm to minimize the similar loss. Experiments on both cuboid-shaped and general Manhattan layouts show that the proposed work outperforms recent algorithms in prediction accuracy.

关键词： 3D room layout Deep learning Manhattan world Panorama image

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Simple Linear Iterative Clustering with Efficiency 1

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作者： Liao, Nannan Liu, Hui Li, Cheng Ren, Xia Guo, Baolong Xidian Univ Inst Intelligent Control & Image Engn Xian Peoples R China

ISBN: (数字)9789811910579

ISBN: (纸本)9789811910579;9789811910562

As an instructive work to generate satisfactory superpixels, simple linear iterative clustering (SLIC), has become fundamental and popular in various computer vision tasks. In this work, the algorithm is reconsidered, and an integrated framework is proposed to further improve the running speed and segmentation performance. In the first stage, a fast convergence strategy for clustering is presented on SLIC to redistribute the seeds efficiently. this is done to initialize a set of clustering centers that is fairly representative of local information on the image plane. then, a followup work of SLIC termed simple non-iterative clustering (SNIC) is utilized to process more accurate segmentation without any post-processing to enforce connectivity. Experimental results show that the framework could generate a synergetic effect and performs better than previous superpixel algorithms in a limited computational time.

关键词： Clustering Initialization Redistribution Superpixels

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Address randomization for dynamic memory allocators on the GPU 21

Address randomization for dynamic memory allocators on the G...

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作者： Peng, Can Huang, Chenlin Hu, Daokun Bang, Di Sun, Jianhua Chen, Hao Zhong, Xionghu College of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9781728120584

GPUs have been widely used in multi-user environments such as the cloud due to its rich thread-level parallelism. In such an environment, multiple kernels can execute in parallel. However, it is possible that a kernel leverages buffer overflow to attack other kernels on the same GPU. the limited existing work focuses on the detection of buffer overflows instead of prevention. Address randomization is an effective approach to preventing memory-related attacks on the CPU. However, current GPUs lack similar support to defend the increasing threats of memory overflow. In this paper, we propose an address randomization method for dynamic memory allocation on the GPU. We have implemented and compared different pseudo-random algorithms on the GPU, and integrated the address randomization into an existing allocator. Elaborate discussions are presented to analyze the security of our proposed address randomization. Experimental evaluations show that the overhead incurred by our randomized algorithm is less than 20% on top of existing memory allocators. © 2019 IEEE.

关键词： Graphics processing unit

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Which Players will Leave their Community? Predicting Guild Abandonments in World of Warcraft Game Data 17

Which Players will Leave Their Community? Predicting Guild A...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Posfai, Gergely Magyar, Gabor Koczy, Laszlo T. Budapest Univ Technol & Econ Dept Telecommun & Media Informat Budapest Hungary

ISBN: (纸本)9781509049172

World of Warcraft (WoW) is one of the most popular massively multiplayer online role-playing games (MMORPGs) having more than 10 million subscribers over the world. In order to engage and retain users understanding and predicting their behavior can be very useful for game developers. An important component of WoW are so-called guilds, which are social communities whose members can act together efficiently to accomplish more difficult goals and also provide a social atmosphere in which the game might be more entertaining. In this paper, we build predictive models to forecast which of the players will leave their guild in the close future. Our best model uses fuzzy c-means clustering to capture groups of similar guilds, that serve as the basis of an ensemble model, which computes predictions for each cluster separately and combines individual predictions into one final prediction using the memberships of the fuzzy clusters. Empirical analysis on WoW game data shows that our methods convincingly outperform the only existing method in the literature. To ensure transparency and reproducibility we publish the source codes of the research and also provide a Docker image, which makes it possible for anyone who has Docker installed to reproduce all of our results with a single command.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Convolutional Neural Networks: Estimating Relations in the Ising Model on Overfiting 17

Convolutional Neural Networks: Estimating Relations in the I...

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17th IEEE International conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Gavrilov, Andrei Jordache, Alex Vasdani, Maya Deng, Jack Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781538633601

A CNN (Convolutional Neural Network) is one of actively researched and broadly applied deep machine learning methods. A CNN is composed of a feed-forward neural network that takes in images as inputs, and outputs a probability value associated to a class that best describes the image. As well, it is constructed of multiple layers, which include convolutional, max-pooling and fully connected layers. However, the training set has a large influence on the accuracy of a network, and hence it is paramount to create a network architecture that prevents overfitting (when a trained model cannot differentiate newly input data from its test data) and underfitting (the inability of a model to find relationships among inputs). this paper addresses the above deficiencies by comparing the statistics of CNN image recognition algorithms to the Ising model. the Ising model consists of magnetic dipole moments that can be in one of two states: +1 or -1. Using a two-dimensional square-lattice array once a training set of such data is complete, we determine the impact that network parameters, specifically learning rate and regularization rate, have on the adaptability of convolutional neural networks for image classification. Our results not only contribute to a better theoretical understanding of a CNN, but also provide concrete guidance on preventing model overfitting and underfitting when a CNN is applied for image recognition.

关键词： Machine Learning Convolutional Neural Network image processing Ising Model Data Overfitting Learning Rate Regularization Rate Cognitive systems

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