Detecting the Dominant Points by the Curvature-Based Polygonal Approximation
Detecting dominant points is an important step for object recognition. Corner detection and polygonal approximation are two major approaches for dominant point detection. In this paper, we propose the curvature-based polygonal approximation method which combines the corner detection and polygonal approximation techniques to detect the dominant points. This detection method consists of three procedures: (1) extract the break points that do not lie on a straight line, (2) detect the potential corners, and (3) perform polygonal approximation by partitioning the curves between two consecutive potential corners. Both quantitative and qualitative evaluations have been conducted. Experimental results show that the combined methods are superior to the conventional methods, and the dominant points can be properly detected by the combined methods.

title: Performance evaluation of some noise reduction methods
abstract: In this paper, a quantitative evaluation of some noise reduction methods is presented. It first classifies the existing local noise reduction techniques by the use of neighbors and statistics and proposes some new filtering techniques. Subsequently, five performance evaluation criteria including processing time, mean distortion, mean busyness, stability, and correct processing ratio are discussed. By using these performance evaluation criteria, an experiment that involves four factors (test image, noise, mask size, and iteration) was designed to evaluate the performance of the local noise reduction methods. The results indicate that some new local noise reduction techniques have performance better than that of the standard existing techniques under certain criteria.

title: A New Edge Detection Method Through Template Matching
abstract: This paper reviews some gradient edge detection methods and proposes a new detector — the template matching edge detector (TMED). This detector utilizes the concepts of pattern analysis and the template matching of 3×3 masks. A set of performance criteria was used to evaluate the gradient edge detectors as well as the template matching edge detector. The results indicate that the new method is superior to the other gradient edge detectors. In addition, the template matching edge detector has also demonstrated good performance on noisy images. It can obtain very precise edge detection of single pixel width.

title: Elliptical object detection by using its geometric properties
abstract: A simple algorithm for ellipse detection in an image is proposed. The new method consists of three steps: (1) detect the elliptical center by using its symmetrical property; (2) identify the elliptical points by using a simple geometric property; and (3) estimate the five parameters ( x c , y c , A , B , θ ) by using a least sum of squares fitting method. The new method has been tested on both the synthetic and real images. The experimental results show that the method is reliable and accurate.

title: Super line-connectivity of consecutive-d digraphs
abstract: The concept of a consecutive- d digraph was proposed by Du, Hsu and Hwang as a generalization of many digraphs, such as de Bruijn digraphs, Kautz digraphs, and Imase-Itoh digraphs, which contain many hamiltonian digraphs with near-minimum diameter and near-maximum connectivity. In this paper, we show sufficient conditions for modified consecutive- d digraphs to have super line-connectivity.

title: A Special Case for Subset Interconnection Designs
abstract: Given a set X and subsets X1, …, Xm, we consider the problem of finding a graph G with vertex set X and the minimum number of edges such that for i = 1,…,m, the subgraph G, induced by Xi is connected. We show that in the special case that every point in X appears in at most three Xi's, the problem is still MAX SNP-complete; however, there exists a polynomial-time approximation within a factor of 5 3 from optimal. As an intermediate result, we also show that the vertex cover problem in cubic graphs is MAX SNP-completc.

title: A maximal figure-of-merit learning approach to text categorization
abstract: A novel maximal figure-of-merit (MFoM) learning approach to text categorization is proposed. Different from the conventional techniques, the proposed MFoM method attempts to integrate any performance metric of interest (e.g. accuracy, recall, precision, or F1 measure) into the design of any classifier. The corresponding classifier parameters are learned by optimizing an overall objective function of interest. To solve this highly nonlinear optimization problem, we use a generalized probabilistic descent algorithm. The MFoM learning framework is evaluated on the Reuters-21578 task with LSI-based feature extraction and a binary tree classifier. Experimental results indicate that the MFoM classifier gives improved F1 and enhanced robustness over the conventional one. It also outperforms the popular SVM method in micro-averaging F1. Other extensions to design discriminative multiple-category MFoM classifiers for application scenarios with new performance metrics could be envisioned too.

title: Industrial Strength Ontology Management
abstract: Ontologies are becoming increasingly prevalent and important in a wide range of e-commerce applications. E-commerce applications are using ontologies to support parametric searches, enhanced navigation and browsing, interoperable heterogeneous information systems, supplier enablement, configuration management, and transaction discovery. Applications such as information and service discovery and autonomous agents that are built on top of the emerging Semantic Web for the WWW also require extensive use of ontologies. Ontology-enhanced commercial applications, such as these and others require ontology management that is scalable (supporting thousands of simultaneous distributed users), available (running 365×24×7), fast, and reliable. This level of ontology management is necessary not only for the initial development and maintenance of ontologies, but is essential during deployment, when scalability, availability, reliability and performance are absolutely critical. VerticalNet's Ontology Builder and Ontology Server products are specifically designed to provide the ontology management infrastructure needed for e-commerce applications. These tools bring the best ontology and knowledge representation practices together with the best enterprise solutions architecture to provide a robust and scalable ontology management solution.

title: Stability analysis on active queue management algorithms in routers
abstract: Numerous works have complained the sensitivity to network load and instability of random early detection (RED), an active queue management (AQM) scheme, under various network configurations. In this work, we focus on our investigation on the stability of AQM in routers. By modeling the close-looped congestion control systems in a differential fluid flow model, we reach the sufficient and necessary condition to stabilize the dynamical system. By exploring the convergence region and root-locus of the system and studying the parameter effects of RED in various network simulation conditions, we show the limits of RED in nature and argue against deploying RED in high-speed switches.