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.
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.
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.
52801820
Shape Reconstruction from 3D and 2D Data Using PDE-Based Deformable Surfaces
In this paper, we propose a new PDE-based methodology for deformable surfaces that is capable of automatically evolving its shape to capture the geometric boundary of the data and simultaneously discover its underlying topological structure. Our model can handle multiple types of data (such as volumetric data, 3D point clouds and 2D image data), using a common mathematical framework. The deformation behavior of the model is governed by partial differential equations (e.g. the weighted minimal surface flow). Unlike the level-set approach, our model always has an explicit representation of geometry and topology. The regularity of the model and the stability of the numerical integration process are ensured by a powerful Laplacian tangential smoothing operator. By allowing local adaptive refinement of the mesh, the model can accurately represent sharp features. We have applied our model for shape reconstruction from volumetric data, unorganized 3D point clouds and multiple view images. The versatility and robustness of our model allow its application to the challenging problem of multiple view reconstruction. Our approach is unique in its combination of simultaneous use of a high number of arbitrary camera views with an explicit mesh that is intuitive and easy-to-interact-with. Our model-based approach automatically selects the best views for reconstruction, allows for visibility checking and progressive refinement of the model as more images become available. The results of our extensive experiments on synthetic and real data demonstrate robustness, high reconstruction accuracy and visual quality.
3869043
XQuery Translation to Sem-SQL
XML query translation is an inevitable step involved in using non-XML databases storing XML data. In this paper, we address the XQuery to Sem-SQL translation issue, part of the XML storage and retrieval using the Semantic Binary Object-Oriented Database System (Sem-ODB) project, by providing a high-level description of the translation scheme between XQuery and Sem-SQL. Our translation scheme utilizes the navigation-oriented paradigm of Sem-SQL in translating XQuery path expressions to avoid excessive joins. Our approach can deal with all recursive path expressions without requiring the underlying database system to support recursive queries. We also extend the sorted outer union approach with consideration on user-defined orderings.
1548147
Level-of-detail based modeling of object movement in tracking and surveillance system
This paper proposes a level-of-detail (LOD) based method for modeling object movement in wireless sensor network tracking and surveillance applications. This method addresses the dilemma of achieving maximum information with limited power and bandwidth in wireless sensor network. LOD provides the researchers with various fidelity levels of data to perform such tasks as regional traffic statistics analysis, object classification and clustering, and at the highest level, object behavior analysis. A simulation test bed extracted from the sensor network hardware platform is being built to allow users to easily generate objects with various behavioral models, design three-dimensional sensor deployment layouts and related surveillance environments, test the accuracy and efficiency of tracking, and calculate lifetime of system and power consumption. An illustrative experiment with preliminary results is provided at the end of this paper.