Region-Based Hierarchical Image Matching

This paper presents an approach to region-based hierarchical image matching, where, given two images, the goal is to identify the largest part in image 1 and its match in image 2 having the maximum similarity measure defined in terms of geometric and photometric properties of regions (e.g., area, boundary shape, and color), as well as region topology (e.g., recursive embedding of regions). To this end, each image is represented by a tree of recursively embedded regions, obtained by a multiscale segmentation algorithm. This allows us to pose image matching as the tree matching problem. To overcome imaging noise, one-to-one, many-to-one, and many-to-many node correspondences are allowed. The trees are first augmented with new nodes generated by merging adjacent sibling nodes, which produces directed acyclic graphs (DAGs). Then, transitive closures of the DAGs are constructed, and the tree matching problem reformulated as finding a bijection between the two transitive closures on DAGs, while preserving the connectivity and ancestor-descendant relationships of the original trees. The proposed approach is validated on real images showing similar objects, captured under different types of noise, including differences in lighting conditions, scales, or viewpoints, amidst limited occlusion and clutter.     

Study of Silicon Cantilevers by the Photoacoustic Elastic Bending Method

Rectangular silicon cantilevers are studied by the photoacoustic (PA) elastic bending method. Experimental signals versus modulation frequency of the excitation optical beam are measured and analyzed in a frequency range from 20 Hz to 50 000 Hz. The procedure for experimental signal correction to eliminate the frequency characteristics of the measuring system is given. The corrected experimental signal shows a good correlation with theoretically calculated PA signal at frequencies below 32 000 Hz. The corrected experimental PA elastic bending signals for cantilevers with different thicknesses are analyzed. The experimental results allow identifying the resonant frequency (the first resonant mode) of the cantilever vibrations. These values are in good agreement with the theoretically computed values. A theoretical model of the optically excited Si cantilever is derived, taking into account plasmaelastic, thermoelastic, and thermodiffusion mechanisms. Dynamic relations for the amplitude and phase of electronic and thermal elastic vibrations in optically excited cantilevers are derived. The theoretical model is compared to the experimental results.     

北京奥运安全倒计时

奥运会网络会运行三大系统：计时计分系统（Timing＆Scoring）、信息发布系统（IDS）以及运动会管理系统（GMS）。     

Increasing the Robustness of Boosting Algorithms within the Linear-programming Framework

AdaBoost has been successfully used in many signal classification systems. However, it has been observed that on highly noisy data AdaBoost easily leads to overfitting, which seriously constrains its applicability. In this paper, we address this problem by proposing a new regularized boosting algorithm LP_norm2-AdaBoost (LPNA). This algorithm arises from a close connection between AdaBoost and linear programming. In the algorithm, skewness of the data distribution is controlled during the training to prevent outliers from spoiling decision boundaries. To this end, a smooth convex penalty function (l ₂ norm) is introduced in the objective function of a minimax problem. A stabilized column generation technique is used to transform the optimization problem into a simple linear programming problem. The effectiveness of the proposed algorithm is demonstrated through experiments on many diverse datasets.     

Estimation of FAO Blaney-Criddle b Factor by RBF Network

This technical note presents the application of a Radial Basis Function (RBF) network to estimate the FAO Blaney-Criddle b factor. Tabular b values are given in the United Nations Food and Agriculture Organization Irrigation and Drainage Paper Number 24. The b values obtained by the RBF network are compared to the appropriate b values produced using regression equations. The RBF network predicted b values better than the regression equations. An example is given to illustrate the simplicity and accuracy of the RBF network for b factor estimation.