1256891
A Supervised Approach to the Evaluation of Image Segmentation Methods
Evaluation is an important step in developing a segmentation algorithm for an image analysis system. We first give a review of segmentation evaluation methods, and then demonstrate how a supervised evaluation method based on shape features is used in the development of a segmentation algorithm for fluorescence images of white blood cells.
20306091
Fast and exact computation of Cartesian geometric moments using discrete Green's theorem
Abstract Green's theorem evaluates a double integral over a planar object by a simple integration along the boundary of the object. It can be used in fast computation of Cartesian geometric moments of two-dimensional binary objects, since the object shape is totally determined by the boundary. Li and Shen [Pattern Recognition 24, 807–813 (1991)] used an approximation of Green's theorem to deal with discrete objects. This method is fast, but not accurate. In this paper, we present a discrete version of Green's theorem, which evaluates a double sum over a two-dimensional discrete object by a simple summation along the discrete boundary of the object. By using the discrete Green's theorem, we propose a new algorithm for fast computation of the geometric moments. The new algorithm is faster than the previous methods, and gives exactly the same results as if a double sum were used. The importance of the exact computation is discussed by examining the accuracy of Hu's moment invariants and some other moment based shape features, which are frequently used in various shape description and pattern recognition tasks. A fast method for computing moments of regions in gray level images, using the discrete Green's theorem, is also presented. We show that the moments of this type are useful in structural texture analysis.
21010264
Fast Computation of Three-Dimensional Geometric Moments Using a Discrete Divergence Theorem and a Generalization to Higher Dimensions
Abstract The three-dimensional Cartesian geometric moments (for short 3-D moments) are important features for 3-D object recognition and shape description. To calculate the moments of objects in a 3-D image by a straightforward method requires a large number of computing operations. Some authors have proposed fast algorithms to compute the 3-D moments. However, the problem of computation has not been well solved since all known methods require computations of order N 3 , assuming that the object is represented by an N × N × N voxel image. In this paper, we present a discrete divergence theorem which can be used to compute the sum of a function over an n -dimensional discrete region by a summation over the discrete surface enclosing the region. As its corollaries, we give a discrete Gauss's theorem for 3-D discrete objects and a discrete Green's theorem for 2-D discrete objects. Using a fast surface tracking algorithm and the discrete Gauss's theorem, we design a new method to compute the geometric moments of 3-D binary objects as observed in 3-D discrete images. This method reduces the computational complexity significantly, requiring computation of O ( N 2 ). This reduction is demonstrated experimentally on two 3-D objects. We also generalize the method to deal with high-dimensional images. Some 3-D moment invariants and shape features are also discussed.
6650910
A predictive, decentralized load balancing approach
The growth of load balancing system raises the issue of scalability, and decentralized load balancing architecture has been proposed to address this issue. In this paper, we investigate how a load balancing architecture can be built on decentralized policies based on CORBA and enhanced by predictive algorithm. The L/sub 2/E predictive filtering model was used to supply workstations with robust cluster load information, which allows them to make more accurate independent allocation decisions. Experimental results showed that our decentralized load balancing approach was able to suppress thrashing and oscillations compared to other load monitoring and prediction techniques, and it was able to achieve a highly balanced system than Sun Grid Engine.
2104066
Design and evaluation of a resource selection framework for Grid applications
While distributed, heterogeneous collections of computers ("Grids") can in principle be used as a computing platform, in practice the problems of first discovering and then organizing resources to meet application requirements are difficult. We present a general-purpose resource selection framework that addresses these problems by defining a resource selection service for locating Grid resources that match application requirements. At the heart of this framework is a simple, but powerful, declarative language based on a technique called set matching, which extends the Condor matchmaking framework to support both single-resource and multiple-resource selection. This framework also provides an open interface for loading application-specific mapping modules to personalize the resource selector. We present results obtained when this framework is applied in the context of a computational astrophysics application, Cactus. These results demonstrate the effectiveness of our technique.
3410538
A Flexible Context-Aware Authorization Framework for Mediation Systems
Security is a critical concern for mediator-based data integration among heterogeneous data sources. This paper provides a modeling and architectural solution to the problem of mediation security that addresses the security challenges including context-awareness, semantic heterogeneity, and multiple security policy specification. A generic, extensible modeling method for the security policies in mediation systems is presented. A series of authorization constraints are identified based on the relationship on the different security components in the mediation systems. Moreover, we enforce the flexible access control to mediation systems while providing uniform access for heterogeneous data sources.
207735443
A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms
Abstract Location of fire stations is an important factor in its fire protection capability. This paper aims to determine the optimal location of fire station facilities. The proposed method is the combination of a fuzzy multi-objective programming and a genetic algorithm. The original fuzzy multiple objectives are appropriately converted to a single unified ‘min–max’ goal, which makes it easy to apply a genetic algorithm for the problem solving. Compared with the existing methods of fire station location our approach has three distinguish features: (1) considering fuzzy nature of a decision maker (DM) in the location optimization model; (2) fully considering the demands for the facilities from the areas with various fire risk categories; (3) being more understandable and practical to DM. The case study was based on the data collected from the Derbyshire fire and rescue service and used to illustrate the application of the method for the optimization of fire station locations.
26409739
Guest Editors' Introduction