12558325
fast Computation of 3-D Geometric Moments Using a Discrete Gauss' Theorem
A discrete Gauss' theorem is presented. Using a fast surface tracking algorithm and the discrete Gauss' theorem, we design a new method to compute the Cartesian geometric moments of 3-D objects. Compared to previous methods to compute such moments, the new method reduces the computational complexity significantly.
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.
15157401
Improving parallel data transfer times using predicted variances in shared networks
It is increasingly common to use multiple distributed storage systems as a single data store within which large datasets may be replicated. Thus, we face the problem of how to access replicated data efficiently. Multiple-source parallel transfers can reduce access times by transferring data from several replicas in parallel. However, we then face the problem of deciding which data to fetch from which replicas. We propose a Tuned Conservative scheduling technique that uses predicted means and variances for network performance to make data selection decisions. This stochastic scheduling technique adjusts the amount of data fetched on a link according to not only the link performance but the expected variance in that performance. We incorporate our technique into the striped GridFTP server from the Globus Toolkit, and demonstrate that the technique can produce data transfer times that are significantly faster and less variable than those of other techniques.
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.
5761634
High-performance operating system controlled memory compression
This article describes a new software-based on-line memory compression algorithm for embedded systems and presents a method of adaptively managing the uncompressed and compressed memory regions during application execution. The primary goal of this work is to save memory in disk-less embedded systems, resulting in greater functionality, smaller size, and lower overall cost, without modifying applications or hardware. In comparison with algorithms that are commonly used in on-line memory compression, our new algorithm has a comparable compression ratio but is twice as fast. The adaptive memory management scheme effectively responds to the predicted needs of applications and prevents on-line memory compression deadlock, permitting reliable and efficient compression for a wide range of applications. We have evaluated our technique on an embedded portable device and have found that the memory available to applications can be increased by 150%, allowing the execution of applications with larger working data sets, or allowing existing applications to run with less physical memory
3857492
Formal Representation and Transformation of DTDs to Sem-ODM Semantic Schemas
Many projects have investigated the issue of storing XML in traditional database systems and exporting data in traditional databases as XML documents. However, they paid little attention to the formalization of the data models and transformation between them. In this paper, we address this problem by providing a formal framework in which Document Type Definition (DTD) and Semantic Schema of the Semantic Binary ObjectOriented Data Model (Sem-ODM) are formally defined and the conversion from DTD to Semantic Schema is presented. Through this formalization, we achieve preciseness and conciseness in expressing both data models and converting one to the other.
11280884
A novel cache design for vector processing
This paper introduces an innovative cache design for vector computers, called prime-mapped cache. By utilizing the special properties of a Mersenne prime, the new design does not increase the critical path length of a processor, nor does it increase the cache access time as compared to a direct-mapped cache. The prime-mapped cache minimizes cache miss ratio caused by line interferences that have been shown to be critical for numerical applications by previous investigators. We show that significant performance gains are possible by adding the proposed cache memory into an existing vector computer provided that application programs can be blocked. The performance gain will increase with the increase of the speed gap between processors and memories. We develop an analytical performance model based on a generic vector computation model to study the performance of the design. Our preliminary performance analysis on various vector access patterns shows that the prime-mapped cache can provide as much as a factor of 2 to 3 performance improvement over the conventional direct-mapped cache in the vector processing environment. Moreover, the additional hardware cost introduced by the new mapping scheme is negligible.