Customers usually change their purchase interests in the short product life cycle of the e-commerce environment. Therefore, recent transaction patterns should have a greater effect on the customer preferences. From the seller's point of view, an e-co

The dynamic magnetic resonance images (DMRI) is one of the major tools for distinguishing nasal tumors in recent years. The purpose of this research is to detect and enhance the tumor region in DMRI automatically by using grey prediction and Fuzzy C-means. The main detecting process is divided into two stages. At the first stage, the pixels that have specific trends and are affected by contrast agents with specific level will be filtered according to the developing coefficient and control coefficient of grey prediction. At the second stage, the tumor area would be detected by using Fuzzy C-means clustering technique to distinguish the differences between normal tissue and tumor. Owing to the work of the first stage, the second stage reduces the calculation dramatically and enhances the result of tumor detection. Six sets of DMRI images are tested by the proposed system. The average of sensitivity, accuracy, and specificity are 0.9856, 0.7857, and 0.7049, respectively. The decreasing percentages of processing pixels are 1.59%~5.59% after the first stage and 0.77%~3.52% after second stage. The detection system has already examined on different MR image sequences. The experimental results are robust and correct.

We describe a distributed robust image mosaics system that is robust to outlier and is better in computation efficiency. The system integrates a Web-based user interface, a robust image mosaic, and a distributed computing model. In robust image mosaics, we combine both feature-based and correlation-based approaches for image registration, and then incorporate a skipped mean estimator with Levenberg-Marquardt method for robust nonlinear parameter estimation. The distributed comparing model is used to speed up the computation of the global processing of image mosaics on a network of workstations. Load balancing strategies are proposed to improve the efficiency of parallel computation under uneven load situation. Experimental results show that our system is indeed robust and has significantly speedup in computation.

In this paper, we present a new stroke extraction algorithm that integrates all levels of contour information including boundary points, dominant points, corner points, segments, cross section sequence graph, character structure to extract strokes of Chinese characters. In the algorithm, first, the boundary points are extracted, then the dominant and corner points are detected. Third, the character structure including singular and regular regions are extracted by the contour information and a modified cross section sequence graph (CSSG). Finally, a Bezier curve taking dominant points and corner points as inputs is used to check the continuity of strokes. Experimental results show that the proposed algorithm can correctly extract the strokes up to 95% from a printed and handwritten test samples based on the human perception. This research provides a solid basis for the structural matching of Chinese characters.

A physically based modeling method that uses adaptive-size meshes to model surfaces of rigid and nonrigid objects is presented. The initial model uses an a priori determined mesh size. However, the mesh size increases or decreases dynamically during surface reconstruction to locate nodes near surface areas of interest (like high curvature points) and to optimize the fitting error. Further, presented with multiple 3-D data frames, the mesh size varies as the data surface undergoes nonrigid motion. This model is used to reconstruct 3-D surfaces, analyze the nonrigid motion, track the corresponding points in nonrigid motion, and create graphic animation and visualization. The method was tested on real range data, on simulated nonrigid motion, and on real data for the left ventricular motion.>

Adaptive-size physically based models suitable for nonrigid motion analysis are presented. The mesh size increases or decreases dynamically during the surface reconstruction process to locate nodes near surface areas of interests (like high curvature points) and to optimize the fitting error. A priori information about nonrigidity can be included so that the surface model deforms to fit moving data points while preserving some basic nonrigid constraints (e.g. isometry or conformality). Implementation of the proposed algorithm with and without isometric/conformal constraints is presented. Performance and accuracy of derived algorithms are demonstrated on data simulating deforming ellipsoidal and bending planar shapes. The algorithm is applied to the real range data for bending paper and to volumetric temporal left ventricular data.>