A Novel Approach Using Edge Detection Information for Texture Based Image Retrieval
Most texture-based image retrieval system just consider an original image of coarseness, contrast and roughness, actually there are many texture information in the edge image. In this paper, a method combining both edge information and gray level co-occurrence matrix properties is proposed to improve the retrieval performance. The proposed method gives encouraging results when comparing its retrieval performance to that of the Yao's method, in the same image database.

title: ROI Based Natural Image Retrieval Using Color and Texture Feature
abstract: Region of interest(ROI) plays an important role in image analysis. In this paper, an efficient approach for content based image retrieval combining both color and texture features using three ROIs is proposed. Firstly, segment image to three parts using K-means algorithm. Secondly, select three ROIs from the three parts and then extract color features and texture features of ROIs. The similarity of two images will be determined by the similarities between pairs of ROIs. The experimental results demonstrate that the proposed method is encouraging with a successful retrieval rate.

title: The nature of knowledge and its influence on knowledge sharing practice: experiences from building the MACROS system
abstract: This study investigates how the interactive influences of the nature of knowledge and multiple organizational and technological factors - trust, leadership, incentives and issues, group size and variety, implementation strategy, and technology - facilitated and/or impeded the knowledge sharing processes. Using a case study approach, the research focuses on the modifying effects of four characteristics of knowledge- codifiability, context-embeddedness, practice-embeddedness, and dynamics- on the processes and outcomes of knowledge sharing in a case of building the multi-purpose access for customer relations & operational support (MACROS) system involving multiple organizations, divisions, and geographically separated offices. The case results suggest that modifying effects occurred along at least three dimensions- codifiability, context-embeddedness, and practice-embeddedness. The levels of codifiability appeared to dictate the implementation strategy; reduced context-embeddedness allowed for more effective group coordination; reduced context-embeddedness also enhanced trust; codified knowledge is more effective than uncodified knowledge in demonstrating concrete incentives; and technology interacts with context-embeddedness and practice-embeddedness. The results of this study have theoretical and practical implications for a larger set of problems encountered in sizable organizations. More specifically, even though the relevance of the nature of knowledge has been widely acknowledged by I. Nonaka, et. al. (2001), it is not always clear how it comes into play. This study conceptualized the nature of knowledge as a modifying variable, and the results provide a new and more comprehensive framework for investigating the relevance of the nature of knowledge in knowledge management research.

title: Real-time Competition Simulation System
abstract: In tennis competition, there are some reasons leading to the competition results inaccurately. So I put forward a real-time competition simulation system to solve the tennis problem. It can overcome the limitations and the blind spots that occur in human observation. The system includes four parts: image collection, moving object detection and tracking, static scene simulation, competition simulation. This paper presents an algorithm for the detection and tracking of moving objects in sequence images that can be applied in simulations of tennis competition. The approach differs from most other methods that solve the problems of object occlusion and object deformation. The experimental results proved that our method is feasibility and usefulness.

title: Multimodal Registration using the Discrete Wavelet Frame Transform
abstract: Image registration is a critical step in medical image analysis. In this paper, a novel image registration method based on the discrete wavelet frame transform (DWFT) and the sum of absolute distance (SAD) method is proposed. First, the multimodal images are decomposed by DWFT, which is shift-invariant compared to traditional dyadic wavelet transforms. Then the energy maps are computed from the details sub-band images. Finally, genetic algorithm (GA) is adopted to obtain the minimum SAD between the two energy maps. The proposed method is tested on 50 pairs of two-dimensional T1-weighted and T2-weighted modal images. Experimental results demonstrated that the proposed method can achieve high accuracy in the rigid transformation case

title: An Improved Fuzzy Image Enhancement Algorithm
abstract: An effective method for fuzzy image enhancement was presented by Russo, which was controlled by tuning of one parameter. The fixed parameter was used for the entire image resulting in over blurring or sharpening of image features in some parts of the image. On the basis of these, in this paper, we applied Russo's algorithm on different kinds of noise and proposed an efficient method to obtain the parameter values adaptively. Firstly, each pixel went through a phase of smoothing and then followed by a phase of combining smoothing and sharpening; Secondly, to effectively enhance image, each pixel local feature was adaptively assigned different parameter values by evaluating pixel local features through a fuzzy membership function; Finally, compared with Russopsilas method and other methods, experimental results indicated that the proposed method could achieve better performance than traditional methods for the enhancement of images corrupted with impulse and Gaussian noise.

title: Clustering in Dynamic Spatial Databases
abstract: Efficient clustering in dynamic spatial databases is currently an open problem with many potential applications. Most traditional spatial clustering algorithms are inadequate because they do not have an efficient support for incremental clustering.In this paper, we propose DClust, a novel clustering technique for dynamic spatial databases. DClust is able to provide multi-resolution view of the clusters, generate arbitrary shapes clusters in the presence of noise, generate clusters that are insensitive to ordering of input data and support incremental clustering efficiently. DClust utilizes the density criterion that captures arbitrary cluster shapes and sizes to select a number of representative points, and builds the Minimum Spanning Tree (MST) of these representative points, called R-MST. After the initial clustering, a summary of the cluster structure is built. This summary enables quick localization of the effect of data updates on the current set of clusters. Our experimental results show that DClust outperforms existing spatial clustering methods such as DBSCAN, C2P, DENCLUE, Incremental DBSCAN and BIRCH in terms of clustering time and accuracy of clusters found.