Dominant point detection using adaptive bending value
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: 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: A dynamic method for dominant point detection
abstract: Detecting dominant points is an important step for shape representation. Most of dominant point detection methods attend to preset or find the region of support of each point. In this paper, we demonstrate an improved method for determining the region of support in the dominant point detection. Instead of setting the regions of support for points independently, the region of support dynamically depending on the previous region of support. The experimental results show that the proposed method is effective in detecting dominant points.

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: 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: 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: 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.

title: Collision Attack and Pseudorandomness of Reduced-Round Camellia

title: A greedy approximation for minimum connected dominating sets
abstract: Given a graph, a connected dominating set is a subset of vertices such that every vertex is either in the subset or adjacent to a vertex in the subset and the subgraph induced by the subset is connected. A minimum connected dominating set is such a vertex subset with minimum cardinality. In this paper, we present a new one-step greedy approximation with performance ratio ln δ + 2 where δ is the maximum degree in the input graph. The interesting aspect is that the greedy potential function of this algorithm is not supmodular while all previously known one-step greedy algorithms with similar performance have supmodular potential functions.