13407923
The Application of Two-Dimensional Image Reconstruction for Relics' Preservation and Administration
The purpose of this paper is to build a relics database using for preservation and administration for relics. The paper research on the two-dimensional reconstruction images store in the relics database. The reconstruction images of relics can be used to observe and analyze the antique inside that the eye has no way to distinguish without disruption. This paper have make experiments with six relic samples. By using the industrial computed-tomography (CT) obtain the CT scan data of the six relics samples of different materials and after handling the real data, the experiment and analysis of CT image reconstruction have been carried out. The reconstruction images by SIRT algorithm can clearly indicate the internal structure of the relics. This paper accomplished the comparison of image reconstruction by SIRT algorithm with different iterative relaxation coefficient λ values. This job can provide the CT images data of relics for the relics database. The experiments can provide support for preservation and administration of relics and provide information for teaching and learning activities of cultural studies.
17637228
Research on the Application of Industrial CT for Relics Image Reconstruction
The purpose of this paper is to visualizing the internal structure of historic relics by using the industrial computed-tomography (CT). The image reconstruction of CT scan data can be used to observe and analyze the antique inside that the eye has no way to distinguish without disruption. This paper have built the simulated testing model, produced the data of simulating projection, and accomplished the comparison of image reconstruction between of ART(SIRT) and FDK algorithm. After handling the real data of different materials, the experiment and analysis of CT image reconstruction have been carried out. This job has provided the effective technology support in the research of the heritage computed tomography scan platform. This paper also has provided the information for teaching and learning activities of cultural studies.
18328733
Target Detecting Based on Improved WVD-HT for Radio Frequency Interference to UWB-SAR
Ultra-wideband synthetic aperture radar is interfered by ground clutter and artificial jamming. In many cases the targetpsilas scatter signal can be approximated as linear frequency modulated signals. Time-frequency Distribution, such as Winger-Ville distribution, provides a direct and effective analysis method of the time-frequency structures of linear frequency modulated signals. Meanwhile, the cross terms of the Winger-Ville distribution can be smoothed through the smoothed pseudo Winger-Ville distribution. Combine Hough transform the time-frequency concentration is improved and better precision of parameter estimation will be obtained. Target detecting and parameter estimation by the smoothed pseudo Winger-Ville distribution and Hough transform for frequency-modulated jamming are simulated in this paper. The method proved to be efficient for target detecting and parameter estimation of ultra-wideband synthetic aperture radar.
207113548
A Hybrid Two-Phase Algorithm For Face Recognition
Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.
16395477
Extending Metadata with Scenarios in Adaptive Distributed System
The traditional metadata cannot provide enough support for the adaptive distributed systems. In this paper, we propose a metadata extension that uses the scenarios as semantic aspect, describing the expected interaction sequence based on XML schema. With such constraints, resource waste and further damages can be avoided by preventing the illegal interactions at runtime. This extension is more valid and user-friendly than the majority of the existing proposals, avoiding the potential inconsistency. A prototype has been developed to illustrate the applicability of the extended metadata. The experiments demonstrate that the prototype introduces an average overhead ratio of 6.4% in the inter-process interactions and only 0.27% in the remote interactions. The approach presented in this paper is suitable for the adaptive distributed systems and affords more guarantee of reliability
1936453
A hierarchical refinement algorithm for fully automatic gridding in spotted DNA microarray image processing
Gridding, the first step in spotted DNA microarray image processing, usually requires human intervention to achieve acceptable accuracy. We present a new algorithm for automatic gridding based on hierarchical refinement to improve the efficiency, robustness and reproducibility of microarray data analysis. This algorithm employs morphological reconstruction along with global and local rotation detection, non-parametric optimal thresholding and local fine-tuning without any human intervention. Using synthetic data and real microarray images of different sizes and with different degrees of rotation of subarrays, we demonstrate that this algorithm can detect and compensate for alignment and rotation problems to obtain reliable and robust results.
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.