Catálogo de publicaciones - libros

It is important to perform the clustering task on XML documents. However, it is difficult to select the appropriate parameters’ value for the clustering algorithms. Meanwhile, current clustering algorithms lack the effective mechanism to detect outliers while treating outliers as “noise”. By integrating outlier detection with clustering, the paper takes a new approach for analyzing the XML documents by structure. After stating the concept of common neighbor based outlier, the paper proposes a new clustering algorithm, which stops clustering automatically by utilizing the outlier information and needs only one parameter, whose appropriate value range is decided in the outlier mining process. After discussing some features of the proposed algorithm, the paper adopts the XML dataset with different structure and other real-life datasets to compare it with other clustering algorithms.

In this paper, we proposed a novel hierarchical algorithm to recognize English calling cards. The algorithm processes multiresolution images of calling cards hierarchically to extract characters and recognize the characters by using an enhanced neural network method. Each processing step functions at lower overhead and results improved output. That is, first, horizontal smearing is applied to a 1/3 resolution image in order to extract the areas that only include characters from the calling card image. Second vertical smearing and the contour tracking masking, is applied to a 1/2 resolution image in order to extract individual characters from the character string areas. And last, the original image is used in the recognition step, because the image accurately includes the morphological information of the characters accurately. To recognize characters with diverse font types and sizes, the enhanced RBF network that improves the middle layer based on the ART1 was used. The results of experiments on a large number of calling card images showed that the proposed algorithm greatly improves the character extraction and recognition compared with traditional recognition algorithms.

This paper proposes a rate-distortion (RD) based seeded region growing (SRG) for extracting an object such as breast tumors in ultrasound volumes which contain speckle noise and indistinct edges. In the proposed algorithm, region growing proceeds in such a way that the growing cost is minimized which is represented as the combination of rate measuring the roughness of a region contour and distortion measuring the inhomogeneity of pixels in a region. An input image is first segmented into an initial seed region and atomic homogeneous regions. The seed is next merged with one of adjacent regions which makes the RD cost minimum at each step. Such a merging is repeated until the RD cost averaged over the entire seed contour reaches the maximum. As a result, the final seed holds region homogeneity and has a smooth contour while maximizing inhomogeneity against its adjacent regions. Experiments of extracting breast tumors in four real ultrasound volumes show the proposed method yields the average 40% improvement in error rate with respect to the results extracted manually over some conventional methods.

A method to estimate long distance navigation of a mobile robot is proposed. The method uses the dead reckoning,sonar and infrared sensors to detect the landmarks. A corridor environment with equal spaced convex edges is applied as the mobile robot’s moving space and the convex edges are used as landmarks for the robot mounted with the combined sensor system to estimate its position. The robot detects the convex edges using combined sensor system, and navigates in this corridor by using the information obtained from dead reckoning and combined sensor system based on the Extended Kalman. Experiment result show the effectiveness of the method.

There are two important factors to visualize ultrasound datasets for volume ray casting method. The first is an efficient method to skip over empty space and the second is an adequate noise filtering method. We propose a noise-insensitive hierarchical min-max octree. In preprocessing stage, we generate a filtered dataset and make a hierarchical min-max octree from the dataset. In rendering step, we exploit the hierarchical min-max octree only when rays skip over transparent region. If rays reach meaningful object, color and opacity values are computed from the original volume dataset. By adaptively using two datasets, our method increases image quality while reducing rendering time.

An Intelligent Particle Swarm Optimization (IPSO) for MO problems is proposed based on AER (Agent-Environment-Rules) model, in which Competition and Clonal Selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. Simulations and comparison with NSGA-II and MOPSO indicate that IPSO is highly competitive.

Key frame extraction has been recognized as one of the important research issues in video retrieval. Key Frame Extraction based on Evolutionary Artificial Immune Network (KFE-EAIN) is proposed in this paper. To describe the distribution of video frame data, an artificial immune network is first evolved by video frame data. Then, key frame can be selected by minimal spanning tree of the network. KFE-EAIN does not require the number of clusters to be known beforehand. Otherwise, it can apply to both single shot and video sequence. Experimental results show that KFE-EAIN can effectively summarize content of a video with acceptable complexity.

In this paper, we propose a topology control algorithm for constructing an energy-efficient spanning subgraph for a wireless multi-hop network. The constructed topology has the following properties: (1) it preserves a minimum-energy path between every pair of nodes; (2) it is biconnected, i.e., it can tolerate any one node failure and avoid network partition. Simulation results show that the constructed topology has a small average node degree, a small average transmission range and a constant power stretch factor.

This paper expatiated on the process of numerical computing of brain electric field in electroencephalogram (EEG). Based on boundary element method (BEM), a 3D reconstruction of computing model is presented first, which is the premise of BEM computing in EEG. A simple but efficient triangular mesh generation method with constrained points is developed, furthermore, a mesh subdivision method is also put forward. Forward computation of EEG is investigated and acceptable results are obtained with the simulated experiments by using these methods.

In this paper, a new approach on image registration is presented. We introduce a novel conception- normal vector information (NVI) – to evaluate the similarity between two images. NVI method takes advantage of the relationship between voxels in the image to extract the normal vector (NV) information of each voxel. Firstly, NVI criterion is presented. Then, based on the criterion, we find that NVI related metric has a quite perfect global optimal value on transformation parameter ranges. Finally, registration examples which are based on NVI criterion are provided. The result implies that the feature of smooth value distribution and one global optimal value that NVI metric has makes the optimization procedure much easier to be implemented in image registration.