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INTERACTIVE 3D LANDSCAPES ON LINE 4

INTERACTIVE 3D LANDSCAPES ON LINE

引用

4th ISPRS international workshop 3D-ARCH - 3D Virtual Reconstruction and Visualization of Complex Architectures

作者： Fanini, B. Calori, L. Ferdani, D. Pescarin, S. CINECA Casalecchio Di Reno BO Italy CNR ITABC I-00015 Rome Italy

The paper describes challenges identified while developing browser embedded 3D landscape rendering applications, our current approach and work-flow and how recent development in browser technologies could affect. All the data, even if processed by optimization and decimation tools, result in very huge databases that require paging, streaming and Level-of-Detail techniques to be implemented to allow remote web based real time fruition. Our approach has been to select an open source scene-graph based visual simulation library with sufficient performance and flexibility and adapt it to the web by providing a browser plug-in. Within the current Montegrotto VR Project, content produced with new pipelines has been integrated. The whole Montegrotto Town has been generated procedurally by CityEngine. We used this procedural approach, based on algorithms and procedures because it is particularly functional to create extensive and credible urban reconstructions. To create the archaeological sites we used optimized mesh acquired with laser scanning and photogrammetry techniques whereas to realize the 3D reconstructions of the main historical buildings we adopted computer-graphic software like blender and 3ds Max. At the final stage, semi-automatic tools have been developed and used up to prepare and clusterise 3D models and scene graph routes for web publishing. Vegetation generators have also been used with the goal of populating the virtual scene to enhance the user perceived realism during the navigation experience. After the description of 3D modelling and optimization techniques, the paper will focus and discuss its results and expectations.

关键词： Web applications Web Graphics OpenSceneGraph Virtual Museums Real-Time Graphics Landscape reconstruction

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An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows

An improved multi-objective evolutionary algorithm for the v...

引用

11th international workshop on Project Management and Scheduling

作者： Garcia-Najera, Abel Bullinaria, John A. Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

The vehicle routing problem with time windows is a complex combinatorial problem with many real-world applications in transportation and distribution logistics. Its main objective is to find the lowest distance set of routes to deliver goods, using a fleet of identical vehicles with restricted capacity, to customers with service time windows. However, there are other objectives, and having a range of solutions representing the trade-offs between objectives is crucial for many applications. Although previous research has used evolutionary methods for solving this problem, it has rarely concentrated on the optimization of more than one objective, and hardly ever explicitly considered the diversity of solutions. This paper proposes and analyzes a novel multi-objective evolutionary algorithm, which incorporates methods for measuring the similarity of solutions, to solve the multi-objective problem. The algorithm is applied to a standard benchmark problem set, showing that when the similarity measure is used appropriately, the diversity and quality of solutions is higher than when it is not used, and the algorithm achieves highly competitive results compared with previously published studies and those from a popular evolutionary multi-objective optimizer. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Vehicle routing problem Combinatorial optimization Multi-objective optimization Evolutionary algorithms

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Localization with a mobile anchor using ABC-GA hybrid algorithm in wireless sensor networks

Localization with a mobile anchor using ABC-GA hybrid algori...

引用

international workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011

作者： Li, Sijin Qi, Rongbin Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

Nodes localization plays an important role in applications of wireless sensor networks. In this paper, a localization scheme with a mobile anchor using a hybrid algorithm (ABC-GA) which combines artificial bee colony (ABC) algorithm with the advantages of genetic algorithm (GA) is proposed. The localization scheme determines location of unknown node by the mobile anchor;it has high accuracy without any additional requirements for the hardware of unknown node. The core problem of the scheme is finding the shortest path to traversal all unknown nodes. We use ABC-GA hybrid algorithm to solve this problem. Simulation results show that ABC-GA hybrid algorithm has high convergence rate and strong global search capability and the accuracy of localization scheme is satisfactory.

关键词： Genetic algorithms

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Application of particle swarm optimization and snake model hybrid on medical imaging

Application of particle swarm optimization and snake model h...

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Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 3rd international workshop on Computational Intelligence in Medical Imaging, CIMI 2011

作者： Shahamatnia, Ehsan Ebadzadeh, Mohamad Mehdi Universidade Nova de Lisboa FCT-UNINOVA Caparica 2829-516 Portugal Electrical and Computer Eng. Department Amirkabir University of Technology Tehran Iran

ISBN: (纸本)9781612843360

Active contour model has been widely used in image processing applications such as boundary delineation, image segmentation, stereo matching, shape recognition and object tracking. In this paper a novel particle swarm optimization scheme has been introduced to evolve snake over time in a way to reduce time complexity while improving quality of results. Traditional active contour models converge slowly and are prone to local minima due to their complex nature. Various evolutionary techniques including genetic algorithms, particle swarm optimization and predator prey optimization have been successfully employed to tackle this problem. Most of these methods are general problem solvers that, more or less, formulate the snake model equations as a minimization problem and try to optimize it. In contrary, our proposed approach integrates concepts from active contour model into particle swarm optimization so that each particle will represent a snaxel of the active contour. Canonical velocity update equation in particle swarm algorithm is modified to embrace the snake kinematics. This new model makes it possible to have advantages of swarm based searching strategies and active contour principles all together. Aptness of the proposed approach has been examined through several experiments on synthetic and real world images of CT and MRI images of brain and the results demonstrate its promising performance particularly in handling boundary concavities and snake initialization problems. © 2011 IEEE.

关键词： Image segmentation

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Approximation, Randomization, and Combinatorial optimization. algorithms and Techniques 2011

引用

丛书名： Lecture Notes in Computer Science

2011年

作者： Leslie Ann Goldberg Klaus Jansen R. Ravi José D.P. Rolim

ISBN: (数字)9783642229350

ISBN: (纸本)9783642229343

This book constitutes the joint refereed proceedings of the 14th international workshop on Approximation algorithms for Combinatorial optimization Problems, APPROX 2011, and the 15th international workshop on Randomization and Computation, RANDOM 2011, held in Princeton, New Jersey, USA, in August 2011. The volume presents 29 revised full papers of the APPROX 2011 workshop, selected from 66 submissions, and 29 revised full papers of the RANDOM 2011 workshop, selected from 64 submissions. They were carefully reviewed and selected for inclusion in the book. In addition two abstracts of invited talks are included. APPROX focuses on algorithmic and complexity issues surrounding the development of efficient approximate solutions to computationally difficult problems. RANDOM is concerned with applications of randomness to computational and combinatorial problems.

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Modified Artificial Bee Colony algorithms for Numerical optimization

Modified Artificial Bee Colony Algorithms for Numerical Opti...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Dongli Zhang Xinping Guan Yinggan Tang Yong Tang

The artificial bee colony algorithm is a swarm intelligence optimization algorithm inspired by the intelligent foraging behavior of honeybees. In this paper, modified ABC algorithms are proposed for numerical optimization. We have compared the performance of our ABC approach against the basic ABC algorithm, results show that the proposed methods have somewhat improved the convergence rate and global searching capability in some optimization problems.

关键词： optimization Convergence Algorithm design and analysis Clustering algorithms Particle swarm optimization Tin Recurrent neural networks

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Fingerprinting-based wireless 3D localization for motion capture applications

Fingerprinting-based wireless 3D localization for motion cap...

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1st ACM MobiHoc workshop on Pervasive Wireless Healthcare, MobileHealth'11 - In Conjunction with MobiHoc 2011 Conference

作者： Giuberti, Matteo Martalò, Marco Ferrari, Gianluigi WASN Laboratory Department of Information Engineering University of Parma Italy

ISBN: (纸本)9781450307802

In this paper, we consider a radio fingerprinting-based localization system for indoor motion capture applications. Fingerprinting allows target localization on the basis of radio-frequency measurements of the Received radio Signal Strength (RSS), taking into account the presence of fading by means of a training phase. Motion capture is then performed by localizing, through fingerprinting, a group of targets placed on the portion of interest of the user arm - -the approach can be easily extended to other portions of the user body. We experimentally investigate, through a SunSPOT wireless sensor network test-bed, different fingerprinting-based localization algorithms, namely deterministic and probabilistic, optimizing, in each case, the system parameters. In particular, the optimization is carried out by minimizing the localization error. © 2011 ACM.

关键词： Network architecture

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The Application of an Improved PSO Based on the Quantum Genetic Algorithm in the Submersible Path-Planning

The Application of an Improved PSO Based on the Quantum Gene...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Fei Yu Yang-lei Liu

An improved particle swarm optimization algorithm (PSO) combined with quantum genetic algorithm is proposed, to solve the problems that the PSO is difficult to converge for benchmark complex problems and it's parameters are hard to define. The new algorithm is used for submersible path planning and simulation on some standard test functions. The results show that the improved is superior to the standard PSO in optimization ability and the convergence rate, and it can find the optimal path faster.

关键词： Genetic algorithms Programming Particle swarm optimization optimization Simulation Logic gates Underwater vehicles

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Automatic Threshold Selection Based on Artificial Bee Colony Algorithm

Automatic Threshold Selection Based on Artificial Bee Colony...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Zhiwei Ye Zhengbing Hu Huamin Wang Hongwei Chen

The goal of image segmentation is to cluster pixels into salient image regions, it is the most significant step in image analysis. Thresholding is a simple but effective tool to separate objects from the background, which is one of the most popular algorithms. The artificial bee colony algorithm (ABC) is a recently presented meta-heuristic algorithm, which has been successfully applied to solve many optimization problems. As a matter of fact the classical Otsu threshold selection can be viewed as an optimization problem. Hence, this paper introduces a new method to select image threshold automatically based on ABC algorithm. In the end, the proposed method has been implemented and tested on several images. Experiments results show that proposed method performs well which is a feasible method to help select optimum threshold.

关键词： Image segmentation Algorithm design and analysis Pixel optimization Entropy Clustering algorithms Motion segmentation

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PET Image Reconstruction from Finite Linogram via Direct Fourier and Logarithmic Barrier Method

PET Image Reconstruction from Finite Linogram via Direct Fou...

引用

IEEE Nuclear Science Symposium/Medical Imaging Conference (NSS/MIC)/18th international workshop on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors

作者： Deng, Zhenzhou Xie, Qingguo Duan, Zhiwen Wuhan Natl Lab Optoelect Wuhan Hubei Peoples R China

ISBN: (纸本)9781467301206

Dual-head PET (Positron Emission Tomography) and PEM (Positron Emission Mammography) are encountered in many biological imaging and medical diagnosis applications. Since the data produced by flat-panel detectors are more closed to the linogram, and rebinning to the sinogram causes errors of the interpolation, image reconstruction based on the linogram is superior to keep the feature of the raw data for dual-head. Length of the linogram produced by actual PET or PEM systems is finite in practice. The projection views of the data are incomplete, and then the finite linogram reconstruction is a limited-view problem in mathematics. Conventional algorithms cannot achieve the exact reconstruction of the limited-view problem. In this work, we propose a half-analytic and half-iterative method named DF/LBM (Direct Fourier and Logarithmic Barrier Method) to solve the limited-view problem. The least square solution is obtained via DFM (Direct Fourier Method), and then a convex optimization method named logarithmic barrier method is employed to correct the least square solution. The substance of the convex optimization is defining the components in the null-space to satisfy some prior information, as the least square solution has no components in the null-space. Applying this method to Hoffman brain phantom, the artifact generated by the least square solution is eliminated, and PSNR is 90.2112, 94.8699 and 99.0507 when the tangent of the half allowable angle is 1.0, 1.5 and 2.0 respectively.

关键词： Barrier Contraception image reconstruction Logarithmic Finite Fourier sinogram Convex function optimization method

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