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An extended ICM algorithm for range image segmentation

An extended ICM algorithm for range image segmentation

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Xiao Wang Han Wang School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)0780386531

We model the range image segmentation problem in the framework of Bayes inference and Markov random field. Thereafter an extended ICM algorithm is presented in this paper. We introduce another estimation (surface parameter set A) into the basic ICM algorithm and also propose to use edge-based segmentation as the initial state for subsequent energy minimizing procedure. Theoretical and experimental analysis of convergence is given. We have shown that in this way a little computation brings forth a high-quality segmentation.

关键词： Image segmentation Inference algorithms Layout Image edge detection Distance measurement Convergence Computer vision Robot vision systems partitioning algorithms Costs

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Algorithm to generate the Archimedean, finite, negative tomonoids

Algorithm to generate the Archimedean, finite, negative tomo...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Milan Petrik Thomas Vetterlein Department of Mathematics Czech University of Life Sciences Prague Czech Republic Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Austria

We study Archimedean, finite, negative totally ordered monoids. We describe an algorithm which generate the structures of this type in a step-wise fashion. Our approach benefits from the level set representation of mo... 详细信息

关键词： partitioning algorithms Level set Geometry Equations Computer science Educational institutions Electronic mail

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Parallelization of K-medoid clustering algorithm

Parallelization of K-medoid clustering algorithm

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International Conference on Information and Communication Technology for the Muslim World (ICT4M)

作者： Walid Aljoby Khaled Alenezi Queen Arwa University Yemen International Islamic University Malaysia Malaysia

This paper presents an approach for paralleling K-medoid clustering algorithm. The K-medoid algorithm will be divided into tasks, which will be mapped into multiprocessor system. The control structure for the way of expressing the tasks in parallel form and the communication model that satisfied the mechanism for interaction between these tasks is presented. Data parallel model is built by decomposing the tasks among the processors. The implementation and testing of the parallel model have conducted using SESE academic simulator under Fedora 11 version at Linux OS environment.

关键词： Clustering algorithms Program processors Algorithm design and analysis partitioning algorithms Classification algorithms System performance Concurrent computing

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A Hierarchical Tridiagonal System Solver for Heterogenous Supercomputers

A Hierarchical Tridiagonal System Solver for Heterogenous Su...

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Workshop on Latest Advances in Scalable algorithms for Large-Scale Systems (ScalA)

作者： Xinliang Wang Yangtong Xu Wei Xue Dept. of Computer Science & Technology Tsinghua University China

Tridiagonal system solver is an important kernel in many scientific and engineering applications. Even though quite a few parallel algorithms and implementations have been addressed in recent years, challenges still remain when solving large-scale tridiagonal system on heterogenous supercomputers. In this paper, a hierarchical algorithm framework SPIKE (pronounced 'SPIKE squared') is proposed to minimize the parallel overhead and to achieve the best utilization of CPU-GPU hybrid systems. In these systems, a layered and adaptive partitioning is presented based on the SPIKE algorithm to effectively control the sequential parts while efficiently exploiting the computation and communication overlapping in heterogeneous computing node. Moreover, the SPIKE algorithm is reformulated to reduce the matrix computations to only 1/3 in our hierarchical algorithm framework. Meanwhile, an improved implementation of the tiled-PCR-pThomas algorithm is employed for the GPU architecture, and the shared memory usage on the GPU can be reduced by 1/3 using careful dependence analysis on solving unit vector tridiagonal systems. Our experiments on Tianhe-1A show ideal weak scalability on up to 128 nodes when solving a tridiagonal system with a size of 1920M in the largest run and good strong scalability (70%) from 32 nodes to 256 nodes when solving a tridiagonal system with a size of 480M. Furthermore, the adaptive task partition across the CPU and GPU can get over 10% performance improvement in the strong scaling test with 256 nodes. In one computing node of Tianhe-1A, our GPU-only code can outperform the CUSPARSE version (non-pivoting tridiagonal solver) by 30%, and our hybrid code is about 6.7 times faster than the Intel SPIKE multi-process version for tridiagonal systems having a size of 3M, 5M, and 15M.

关键词： Graphics processing units Matrix decomposition Clustering algorithms Vectors partitioning algorithms Mathematical model Equations

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A Bottom-Up Search Algorithm for Dynamic Reformation of Agent Coalitions under Coalition Size Constraints

A Bottom-Up Search Algorithm for Dynamic Reformation of Agen...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Ayan Dutta Prithviraj Dasgupta José Baca Carl Nelson Computer Science Department University of Nebraska Omaha USA Mechanical and Materials Engineering Department University of Nebraska-Lincoln USA

ISBN: (纸本)9781479939329

We consider the problem of dynamic coalition formation among a set of agents where the value function of the agents is constrained by the size of a coalition - larger coalitions are able to get higher value but only up to a certain fixed coalition size, denoted by n_max. The objective of the coalition formation problem is to determine a partition of the agent set that gives the highest utility to the agents. This problem is non-trivial as the set of partitions that needs to be explored grows exponentially with the number of agents and an exhaustive search in the space of partitions makes the problem intractable. To address this problem, we first provide a formal framework to model the coalition formation problem using a coalition structure graph (CSG). We then propose a branch and bound based algorithm called bottom Up CSG Search that searches for the optimal partitions or coalition structures among the nodes of CSG while pruning nodes that are not going to lead to the optimal coalition structure. We have provided analytical results related to the completeness, anytime-nature and time complexity of our algorithm. We have also verified the performance of our algorithm for a dynamic reformation problem where a set of physical e-puck robots starting from arbitrary positions form sub-teams that maximize their utility. Our experimental results show that our proposed algorithm performs better in terms of number of nodes generated and the time required to find the optimal coalition structure or partition than existing CSG-search algorithms.

关键词： Heuristic algorithms partitioning algorithms Search problems Robot sensing systems Uncertainty Algorithm design and analysis

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Spectral LPM: an optimal locality-preserving mapping using the spectral (not fractal) order

Spectral LPM: an optimal locality-preserving mapping using t...

引用

International Conference on Data Engineering

作者： M.F. Mokbel W.G. Aref A. Grama Department of Computer Sciences Purdue University West Lafayette IN USA

For the past two decades, fractals (e.g., the Hilbert and Peano space-filling curves) have been considered the natural method for providing a locality-preserving mapping. The idea behind a locality-preserving mapping is to map points that are nearby in the multidimensional space into points that are nearby in the one-dimensional space. We argue against the use of fractals in locality-preserving mapping algorithms, and present examples with experimental evidence to show why fractals produce poor locality-preserving mappings. In addition, we propose an optimal locality-preserving mapping algorithm, termed the spectral locality-preserving mapping algorithm (Spectral LPM, for short), that makes use of the spectrum of the multidimensional space. We give a mathematical proof for the optimality of Spectral LPM, and also demonstrate its practical use.

关键词： Fractals Hilbert space Clustering algorithms partitioning algorithms Spatial databases Multidimensional systems Geographic Information Systems Cranes Data engineering Eigenvalues and eigenfunctions

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The invisible cultural heritage in spatial organization

The invisible cultural heritage in spatial organization

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Yun-Shang Chiou Yohana Natalia Cahyono Department of Architecture National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781509020942

This paper applies four centrality measures, Newman grouping algorithm, central graph and depth study to delineate the characteristics of two sets of spatial networks. One dataset contains the spatial networks of three Chinese-Indonesian houses in Surabaya, Indonesia, which are three merchants' houses built in different time periods in the past 100 years. The other dataset contains the spatial networks of six farmers' houses in the village of JinHsing, Taiwan, which were also built in different time periods in the past 100 years. The houses in Surabaya have been adapted for commerce-based urban life. The houses in JinHsing are part of the fabric of an agriculture settlement. Two groups of houses rooted in Chinese culture are differentiated by their natural environments, life-styles and social conditions. While the appearance, materials, construction and even floor-plans of these houses have changed dramatically over past century, certain patterns in spatial organizations persist. Using in-group and cross-group comparisons, the paper illustrates how the development of spatial organization of these houses reflects the cultural, social and economic statuses of the residents and the community.

关键词： Buildings Organizations Social network services Algorithm design and analysis Spatial databases Cultural differences partitioning algorithms

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Optimal parallel algorithm for edge-coloring partial k-trees with bounded degrees

Optimal parallel algorithm for edge-coloring partial k-trees...

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International Symposium on Parallel Architectures, algorithms and Networks (ISPAN)

作者： Xiao Zhou T. Nishizeki Department of System Information Sciences Graduate School of Information Sciences University of Tohoku Sendai Japan

Many combinatorial problems can be efficiently solved for partial k-trees (graphs of treewidth bounded by k). The edge-coloring problem is one of the well-known combinatorial problems for which no NC algorithms have been obtained for partial k-trees. This paper gives an optimal and first NC parallel algorithm to find an edge-coloring of any given partial k-tree using a minimum number of colors if k and the maximum degree /spl Delta/ are bounded.< >

关键词： Parallel algorithms Tree graphs Dynamic programming Concurrent computing partitioning algorithms Polynomials Knee Logic Heuristic algorithms Parallel architectures

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A new multiobjective simulated annealing based clustering technique using stability and symmetry

A new multiobjective simulated annealing based clustering te...

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International Conference on Pattern Recognition

作者： Sriparna Saha Sanghamitra Bandyopadhyay Machine Intelligence Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781424421749

Most clustering algorithms operate by optimizing (either implicitly or explicitly) a single measure of cluster solution quality. Such methods may perform well on some data sets but lack robustness with respect to variations in cluster shape, proximity, evenness and so forth. In this paper, we have proposed a multiobjective clustering technique which optimizes simultaneously two objectives, one reflecting the total symmetry present in the data set and the other reflecting the stability of the obtained partitions over different bootstrap samples of the data set. The proposed algorithm utilizes a recently developed simulated annealing based multiobjective optimization technique, AMOSA, as the underlying optimization method. Here assignment of points to different clusters are done based on the point symmetry based distance rather than the Euclidean distance. Results on several artificial and real-life data sets show that the proposed technique is well-suited to detect the number of clusters from data sets having point symmetric clusters.

关键词： Simulated annealing Stability Clustering algorithms partitioning algorithms Optimization methods Euclidean distance Machine intelligence Robustness Shape Data mining

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Blocked stochastic sampling versus Estimation of Distribution algorithms

Blocked stochastic sampling versus Estimation of Distributio...

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Congress on Evolutionary Computation

作者： R. Santana H. Muhlenbein ICIMAF Havana Cuba AiS. FHG Schloss Birlinghoven Sankt Augustin Germany

The Boltzmann distribution is a good candidate for a search distribution for optimization problems. We compare two methods to approximate the Boltzmann distribution - Estimation of Distribution algorithms (EDA) and Markov Chain Monte Carlo methods (MCMC). It turns out that in the space of binary functions even blocked MCMC methods outperform EDA on a small class of problems only. In these cases a temperature of T = 0 performed best.

关键词： Stochastic processes Sampling methods Boltzmann distribution Temperature Annealing Electronic design automation and methodology partitioning algorithms Distributed computing Proposals Search methods

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