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.
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.
8216383
Locating object contours in complex background using improved snakes
An active contour model, called snake, can adapt to object boundary in an image. A snake is defined as an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines or edges. The traditional snake model fails to locate object contours that appear in complex background. In this paper, we present an improved snake model associated with new regional similarity energy and a gravitation force field to attract the snake approaching the object contours efficiently. Experiment results show that our snake model works successfully for convex and concave objects in a variety of complex backgrounds.
1966273
Block-quantized kernel matrix for fast spectral embedding
Eigendecomposition of kernel matrix is an indispensable procedure in many learning and vision tasks. However, the cubic complexity <i>O</i>(<i>N</i><sup>3</sup>) is impractical for large problem, where <i>N</i> is the data size. In this paper, we propose an efficient approach to solve the eigendecomposition of the kernel matrix <i>W</i>. The idea is to approximate <i>W</i> with <i>W</i> that is composed of <i>m</i><sup>2</sup> constant blocks. The eigenvectors of <i>W</i>, which can be solved in <i>O</i>(<i>m</i><sup>3</sup>) time, is then used to recover the eigenvectors of the original kernel matrix. The complexity of our method is only <i>O</i>(<i>mN</i> + <i>m</i><sup>3</sup>), which scales more favorably than state-of-the-art low rank approximation and sampling based approaches (<i>O</i>(<i>m</i><sup>2</sup><i>N</i> + <i>m</i><sup>3</sup>)), and the approximation quality can be controlled conveniently. Our method demonstrates encouraging scaling behaviors in experiments of image segmentation (by spectral clustering) and kernel principal component analysis.
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.
15012237
A topical PageRank based algorithm for recommender systems
In this paper, we propose a Topical PageRank based algorithm for recommender systems, which aim to rank products by analyzing previous user-item relationships, and recommend top-rank items to potentially interested users. We evaluate our algorithm on MovieLens dataset and empirical experiments demonstrate that it outperforms other state-of-the-art recommending algorithms.
1087856
Towards a Model Driven Service Engineering Process
Developments in the realm of business services have opened new challenges in the business consulting world. Consequently, new procedures for services system development are being proposed. In this work, we present a model driven approach for streamlining the service engineering process. An important aspect of our work is a method for model transformation, which is essential for providing and enhancing the management and governance capabilities of the service engineering process. We then introduce a novel infrastructure which leverages Web 2.0 technologies to address the challenge of developing semantic modeling editors. Finally, we use this infrastructure as the target platform to validate our work.
31414473
Improved Learning Algorithm Based on Generalized SOM for Dynamic Non-linear System
This paper proposes an improved learning algorithm based on generalized SOM for dynamical non-linear system identification. To improve the convergent speed and the accuracy of SOM algorithm, we propose the improved self-organizing algorithm, which, at first, applies the multiple local models instead of the global model, and secondly, adjusts the weights of the computing output layers along with the weights of the competing neurons layer during the training process. We prove that the improved algorithm is convergent if the network has suitable initial weights and small positive real parameters. The simulation results using our improved generalized SOM show an improvement for non-linear system compared to traditional neural network control systems.