An adaptive method for detecting dominant points
In this paper, we propose an adaptive method for the polygonal approximation of a digitized curve. Instead of setting a fixed length of support region in advance, the new method will compute the suitable length of support region for each point to find the best approximated curvature. The dominant points are identified as the points with local maximum curvatures. In addition, the break point detection is conducted to reduce the computations. The experimental results show that the proposed method can approximate the curves effectively.

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: Dominant point detection using adaptive bending value
abstract: An efficient method for dominant point detection is proposed in this paper. The region of support for each point on curve is determined using bending value. The points with local maximum smoothing bending value can be located as the dominant points on the curve. The proposed algorithm needs no input parameter. The experimental results show that the new method is efficient and effective in detecting dominant points.

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: Detecting the Dominant Points by the Curvature-Based Polygonal Approximation
abstract: 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: 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: 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 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: Collaborative dancing in tele-immersive environment
abstract: We present a study of collaborative dancing between remote dancers in a tele-immersive environment which features 3D full and real body capturing, wide field of view, multi-display 3D rendering, and attachment free participant. We invite two professional dancers to perform collaborative dancing in the environment. The coordination requires one dancer to take the lead while the other follows by appropriate movement. Throughout the experiment, the dancers are dancing at various motion rates to evaluate how well the collaborative dancing is supported with the current technical boundary. Our important findings indicate that 1) tele-immersive environments have strong potential impact on the concept of choreography and communication of live dance performance, 2) the presence of multi-view display, real body 3D rendering, audio channel, and less intrusiveness greatly enhances the immersive and dancing experience, and 3) the level of synchronization achieved by the dancers is higher than that expected from the video rate.

title: Analysis on adjustment-based TCP-friendly congestion control: fairness and stability
abstract: In this paper, we focus on understanding the binomial congestion control algorithms (Bansal et al., 2001) and can generalize TCP-style additive-increase by increasing inversely proportional to a power k of the current window (for TCP, k=0) and generalize TCP-style multiplicative-decrease by decreasing proportional to a power l of the current window (for TCP, l=1). We discuss their global fairness and stability. We prove that such congestion control algorithms can achieve (p, k+l+1)-proportional fairness globally no matter what the network topology is and how many users there are. We also study their dynamical behavior through a control theoretical approach. The smoothness of the congestion control results in a less stable system and slower convergence to fair bandwidth allocation. The modeling and discussion in this paper are quite general and can be easily applied to equation-based TCP-friendly congestion control schemes, another category of TCP-friendly transport protocols.