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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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High performance computing in image processing and computer vision

High performance computing in image processing and computer ...

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international conference on pattern recognition

作者： J.A. Webb School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Image processing and computer vision are natural applications for high performance computing (here considered to be general-purpose parallel supercomputing), but there are many barriers to its effective use in computer vision. these barriers are described, two systems (the Carnegie Mellon ALVINN system and the animate vision model at Rochester) that overcame them are discussed, and then a world-wide survey is taken of application of HPC to image processing and computer vision. Finally, the future of this field is described.

关键词： High performance computing Image processing Computer vision Application software Delay Robot vision systems Computer aided manufacturing Scientific computing Supercomputers Costs

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12th international Scientific and Technical conference Computer Science and Information Technologies, CSIT 2017

12th International Scientific and Technical Conference Compu...

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12th international Scientific and Technical conference Computer Science and Information Technologies, CSIT 2017

ISBN: (纸本)9783319705804

the proceedings contain 45 papers. the special focus in this conference is on Computer Science and Information Technologies. the topics include: A general game-theoretic approach to harmonization the values of project stakeholders;information technology for assurance of veracity of quality information in the software requirements specification;a multidimensional adaptive growing neuro-fuzzy system and its online learning procedure;method of integration and content management of the information resources network;geoinformation technology for analysis and visualisation of high spatial resolution greenhouse gas emissions data using a cloud platform;comparative analysis of conversion series forecasting in e-commerce tasks;performance analysis of open source machine learning frameworks for various parameters in single-threaded and multi-threaded modes;on scalability of predictive ensembles and tradeoff between their training time and accuracy;Agent DEVS simulation of the evacuation process from a commercial building during a fire;Model of the objective clustering inductive technology of gene expression profiles based on SOTA and DBSCAN clustering algorithms;the alpha-procedure as an inductive approach to pattern recognition and its connection with lorentz transformation;analyzing project team members’ expectations;the contextual search method based on domain thesaurus;optimizing wind farm structure control;model of the system of personalized analysis of financial condition of the enterprise;Hybrid sorting-out algorithm COMBI-GA with evolutionary growth of model complexity;development of combined information technology for time series prediction;keyphrase extraction using extended list of stop words with automated updating of stop words list;involutory parametric orthogonal transforms of cosine-walsh type with application to data encryption.

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E-service delivery pattern for knowledge discovery Grid

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12th international conference on Knowledge-Based Intelligent Information and Engineering Systems

作者： Liu, Meiqun Zhejiang Wanli Univ Ningbo 315100 Peoples R China

ISBN: (纸本)9783540855644

the emergence of ASP hosting Grid based data mining services is being seen as a novel and feasible solution for organizations that value their knowledge resources but are constrained by the high cost of knowledge discover tools. In this paper, we present a novel E-service delivery pattern for the Grid based data mining services. this pattern has several strong suits than the present methods for delivering data mining services such as supporting clients with many kinds of choices, especially the choice of the Asp. We have developed tools to support the E-service delivery pattern. the detailed information is including: the preferences of tasks, the functionality of ASP and the information of data resource and computation resources to run the application. We have realized the map function to match the requirement of client, the capability of resource and ASP.

关键词： Grid computing distributed computing data mining knowledge discovery in database Grid service

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Analysis and Optimization of Sparse LU factorization of Power Matrix Based on GLU 12

Analysis and Optimization of Sparse LU factorization of Powe...

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12th international conference on Renewable Power Generation, RPG 2023

作者： Shi, Cuncun Lin, Long Yang, He Wang, Shucai State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd Beijing China

With the continuous improvement of power grid stability and reliability requirements, improving the efficiency and accuracy of power system simulation has become an important research topic. the sparse LU factorization is one of the key modules of the sparse direct solver, which usually plays a dominant role in the solving time of the sparse linear algebra equation of power system. In order to improve the efficiency of sparse LU factorization, we propose: 1) an adaptive mode partition strategy based on GLU, and select the mode to be used according to the characteristics of the matrix to be decomposed, which improves the computational efficiency of LU factorization;2) a dynamic Nstream value taking method for the stream pattern, which determines the number of streams to be used based on the dimensions of the sub-matrix, 3) a pipeline model for the continuous layer with the size of 1 in GLU, which improves the parallelism of sparse LU factorization and helps to improve the computing efficiency. the results show that the proposed improvement can achieve better results, and the speed of LU factorization is increased by more than 30% on rajatl2 and circuit_2. © the Institution of Engineering & Technology 2023.

关键词： Computational efficiency

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A new method for handwritten scene text detection in video

A new method for handwritten scene text detection in video

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international conference on Frontiers in Handwriting recognition

作者： Shivakumara, Palaiahnakote Dutta, Anjan Pal, Umapada Tan, Chew Lim School of Computing National University of Singapore Singapore Singapore Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9780769542218

there are many video images where hand written text may appear. therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are many video frames where text line may be multi-oriented in nature. To the best of our knowledge there is no work on handwritten text detection in video, which is multi-oriented in nature. In this paper, we present a new method based on maximum color difference and boundary growing method for detection of multi-oriented handwritten scene text in video. the method computes maximum color difference for the average of R, G and B channels of the original frame to enhance the text information. the output of maximum color difference is fed to a K-means algorithm with K=2 to separate text and non-text clusters. Text candidates are obtained by intersecting the text cluster with the Sobel output of the original frame. To tackle the fundamental problem of different orientations and skews of handwritten text, boundary growing method based on a nearest neighbor concept is employed. We evaluate the proposed method by testing on our own handwritten text database and publicly available video data (Hua's data). Experimental results obtained from the proposed method are promising. © 2010 IEEE.

关键词： Color

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Semi-automatic Segmentation of Scattered and Distributed Objects 10th

Semi-automatic Segmentation of Scattered and Distributed Obj...

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10th international conference on Computer recognition Systems (CORES)

作者： Farid, Muhammad Shahid Lucenteforte, Maurizio Khan, Muhammad Hassan Grangetto, Marco Univ Punjab Coll Informat Technol Lahore Pakistan Univ Turin Dipartimento Informat Turin Italy Univ Siegen Pattern Recognit Grp Siegen Germany

ISBN: (纸本)9783319591629

this paper presents a novel object segmentation technique to extract objects that are potentially scattered or distributed over the whole image. the goal of the proposed approach is to achieve accurate segmentation with minimum and easy user assistance. the user provides input in the form of few mouse clicks on the target object which are used to characterize its statistical properties using Gaussian mixture model. this model determines the primary segmentation of the object which is refined by performing morphological operations to reduce the false positives. We observe that the boundary pixels of the target object are potentially misclassified. To obtain an accurate segmentation, we recast our objective as a graph partitioning problem which is solved using the graph cut technique. the proposed technique is tested on several images to segment various types of distributed objects e.g. fences, railings, flowers. We also show some remote sensing application examples, i.e. segmentation of roads, rivers, etc. from aerial images. the obtained results show the effectiveness of the proposed technique.

关键词： Object segmentation Gaussian mixturemodel Graph-cuts

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Artificial Immune-Based Semi-supervised Learning for Classification 15

Artificial Immune-Based Semi-supervised Learning for Classif...

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15th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC 2023

作者： Wang, Yi Hubei University of Economics School of Information Engineering Wuhan430205 China Hubei University of Economics Hubei Internet Finance Information Engineering Technology Research Center Wuhan430205 China

ISBN: (纸本)9798350308693

Recently, artificial immune algorithms have attracted great attention of researchers and been widely used in function optimization, pattern recognition and classification. Traditional artificial immune algorithms for classification are applied to supervised learning problems, which require completely labeled data for training models. However, in many real-world scenarios, it is difficult to obtain all labeled samples. To solve this problem, an efficient semi-supervised artificial immune algorithm for classification tasks is proposed. It employs a clonal selection algorithm to generate memory cells used for classification, which is achieved via selection, cloning, and mutation procedures. Moreover, it utilizes the ensemble learning technique to extend the co-training paradigm and improves classification performance by adding the most confident unlabeled samples into the labeled set. In addition, the theory of learning from noisy examples is adopted to decide whether there are enough newly labeled samples that are used to reduce the negative effects caused by noises. Experimental results show that the proposed method achieves better or comparable performance than well-known semi-supervised and supervised methods on four datasets. © 2023 IEEE.

关键词： pattern recognition

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Abstracting transport to an ontology design pattern for the geosciences 4

Abstracting transport to an ontology design pattern for the ...

引用

4th Workshop on Ontology and Semantic Web patterns, WOP 2013 - Co-located with 12th international Semantic Web conference, ISWC 2013

作者： Whitehead, Brandon Adams, Benjamin Schildhauer, Mark Vardeman, Charles Kuhn, Werner Shepard, Adam Sinha, Krishna Centre for eResearch University of Auckland New Zealand National Center for Ecological Analysis and Synthesis UCSB United States Center for Research Computing University of Notre Dame United States University of Münster Germany Woods Hole Oceanographic Institution United States Virginia Polytechnic Institute and State University United States

A core concept in geoscience/physical science research is the concept of transport. We present an ontology design pattern for the notion of transport in the geosciences using natural language coupled with a concept map. the top level concepts of the Transport pattern are trans- port entity, transport mechanism, and transport event. these concepts are described in detail, and a brief example is provided to illustrate the usefulness of the pattern. Copyright © 2013 for the individual papers by the papers' authors.

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Gyral Folding pattern Analysis via Surface Profiling

引用

12th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Li, Kaiming Guo, Lei Li, Gang Nie, Jingxin Faraco, Carlos Zhao, Qun Miller, Stephen Liu, Tianming Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Univ Georgia Dept Comp Sci Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Dept Psychol Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Dept Phys & Astron Athens GA 30602 USA

ISBN: (纸本)9783642042676

Human cortical folding pattern has been Studied for decades. this paper proposes a gyrus scale folding pattern analysis technique via cortical surface profiling. Firstly, we sample the cortical Surface into 2D profiles and model them using power function. this step provides both the flexibility of representing arbitrary shape by profiling and the compactness of representing shape by parametric modeling. Secondly, based on the estimated model parameters, we extract affine-invariant features on the cortical Surface and apply the affinity propagation Clustering algorithm to parcellate the cortex into regions with different shape patterns. Finally, a second-round surface profiling is performed on the parcellated cortical regions, and the number of hinges is detected to describe the gyral folding pattern. Experiments demonstrate that our method could successfully classify human gyri into 2-hinge, 3-hinge and 4-hinge gyri. the proposed method has the potential to significantly contribute to automatic segmentation and recognition of cortical gyri.

关键词： Clustering algorithms

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Generalized Median Computation for Consensus Learning: A Brief Survey 20th

Generalized Median Computation for Consensus Learning: A Bri...

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20th international conference on Computer Analysis of Images and patterns (CAIP)

作者： Jiang, Xiaoyi Nienkoetter, Andreas Univ Munster Fac Math & Comp Sci Munster Germany Sichuan Univ Hongkong Polytech Univ Inst Disaster Management & Reconstruct Chengdu Sichuan Peoples R China

ISBN: (纸本)9783031442360;9783031442377

computing a consensus object from a set of given objects is a core problem in machine learning and pattern recognition. A popular approach is the formulation of generalized median as an optimization problem. the concept of generalized median has been studied for numerous problem domains with a broad range of applications. Currently, the research is widely scattered in the literature and no comprehensive survey is available. this brief survey contributes to closing this gap and systematically discusses the relevant issues of generalized median computation. In particular, we present a taxonomy of computation frameworks and methods. We also outline a number of future research directions.

关键词： pattern recognition

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