Moments constitute an important set of parameters for image analysis. The low order moments contain significant information about a simple object. They have been used in finding the location and orientation of an obje...
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Moments constitute an important set of parameters for image analysis. The low order moments contain significant information about a simple object. They have been used in finding the location and orientation of an object. Moment invariants have been used as features for pattern recognition. To compute moments of a two-dimensional image, a large number of multiplications and additions are required in a direct approach. Multiplications, which are the most time-consuming operations in simple processors, can be completely avoided in the proposed algorithms for low order moments. In this paper, parallel algorithms are proposed for efficient implementation in processor arrays. The basic idea is to decompose a 2-D moment into many vertical moments and a horizontal moment and to use the data parallelism for the vertical moments and the task parallelism for the horizontal moment.
With the rapid development of computer graphics, distributed-computing and Internet, it is possible to achieve Internet-based virtual city. This paper dwells on the method of the terrain and its feature modeling and c...
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With the rapid development of computer graphics, distributed-computing and Internet, it is possible to achieve Internet-based virtual city. This paper dwells on the method of the terrain and its feature modeling and complex entity modeling in the virtual city. Then, discusses the method for Internet-based virtual city 3D visualization and the design of the Browser/Server architecture of the system of virtual city in the network environment. Finally, Java and Java 3D are used to show an experiment example, and the related conclusion about Internet-based virtual city 3D displaying and the client-side interactive operation is given.
Genetic algorithms (GAs) are excellent approaches to solving complex problems in optimization with difficult constraints, and in high state space dimensionality problems. The classic bin-packing optimization problem h...
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ISBN:
(纸本)0819429783
Genetic algorithms (GAs) are excellent approaches to solving complex problems in optimization with difficult constraints, and in high state space dimensionality problems. The classic bin-packing optimization problem has been shown to be a NP-complete problem. There are GA applications to variations of the bin-packing problem for stock cutting, vehicle loading, air container loading, scheduling, and the knapsack problem. Mostly, these are based on a one-dimensional or two-dimensional considerations. Ikonen et. al.(1) have developed a GA for rapid prototyping called GARP, which utilizes a three-dimensional chromosome structure for the bin-packing of the Sinterstation 2000's build cylinder. GARP allows the Sinterstation to be used more productively. The GARP application was developed for a single CPU machine. Anticipating greater use of time compression technologies, this paper examines the framework necessary to reduce GARP's execution time. This framework is necessary to speed-up the bin-packing evaluation, by the use of distributed or, parallel GAs. In this paper, a framework for distribution techniques to improve the efficiency of GARP, and to improve the quality of GARPis solutions is proposed.
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