Embedding HMMs-based models in a Euclidean space: the topological hidden Markov models

Current extensions of hidden Markov models such as structural, hierarchical, coupled, and others have the power to classify complex and highly organized patterns. However, one of their major limitations is the inability to cope with topology: When applied to a visible observation (VO) sequence, the traditional HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named “topological hidden Markov models” (THMMs) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. This is achieved by modeling the noise embedded in the shape generated by the VO sequence. We cover the first and second level topological HMMs. We describe five basic problems that are assigned to a second level topological hidden Markov model: (1) sequence probability evaluation, (2) statistical decoding, (3) structural decoding, (4) topological decoding, and (5) learning. To show the significance of this research, we have applied the concept of THMMs to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map protein primary structures to their 3D folds. The results show that the second level THMMs outperform the SHMMs and the multi-class SVM classifiers significantly.     

Dynamical information fusion of heterogeneous sensors for 3D tracking using particle swarm optimization

This paper presents a new method for three dimensional object tracking by fusing information from stereo vision and stereo audio. From the audio data, directional information about an object is extracted by the Generalized Cross Correlation (GCC) and the object’s position in the video data is detected using the Continuously Adaptive Mean shift (CAMshift) method. The obtained localization estimates combined with confidence measurements are then fused to track an object utilizing Particle Swarm Optimization (PSO). In our approach the particles move in the 3D space and iteratively evaluate their current position with regard to the localization estimates of the audio and video module and their confidences, which facilitates the direct determination of the object’s three dimensional position. This technique has low computational complexity and its tracking performance is independent of any kind of model, statistics, or assumptions, contrary to classical methods. The introduction of confidence measurements further increases the robustness and reliability of the entire tracking system and allows an adaptive and dynamical information fusion of heterogenous sensor information.     

Multi-spectral fusion for surveillance systems

Surveillance systems such as object tracking and abandoned object detection systems typically rely on a single modality of colour video for their input. These systems work well in controlled conditions but often fail when low lighting, shadowing, smoke, dust or unstable backgrounds are present, or when the objects of interest are a similar colour to the background. Thermal images are not affected by lighting changes or shadowing, and are not overtly affected by smoke, dust or unstable backgrounds. However, thermal images lack colour information which makes distinguishing between different people or objects of interest within the same scene difficult.By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using either modality individually. We evaluate four approaches for fusing visual and thermal images for use in a person tracking system (two early fusion methods, one mid fusion and one late fusion method), in order to determine the most appropriate method for fusing multiple modalities. We also evaluate two of these approaches for use in abandoned object detection, and propose an abandoned object detection routine that utilises multiple modalities. To aid in the tracking and fusion of the modalities we propose a modified condensation filter that can dynamically change the particle count and features used according to the needs of the system.We compare tracking and abandoned object detection performance for the proposed fusion schemes and the visual and thermal domains on their own. Testing is conducted using the OTCBVS database to evaluate object tracking, and data captured in-house to evaluate the abandoned object detection. Our results show that significant improvement can be achieved, and that a middle fusion scheme is most effective.     

基于SOAP消息的过度加密攻击检测算法

分析Web服务中的过度加密攻击场景、攻击特点以及SOAP消息特征,提出一种基于简单对象访问协议消息(SOAP)消息的过度加密攻击检测算法。通过检测标签ReferenceList的属性个数统计SOAP消息的加密次数,并将统计出的加密次数与预先设定的阈值进行比较,从而判断是否存在过度加密攻击。在.net WSE安全平台下验证了该检测算法的有效性。     

村镇土地审批流程的动态构建

针对村镇土地流转业务复杂、审批处理流程标准不统一的问题,基于对象交换模型(OEM)提出一种动态数据管理方法,建立村镇土地审批流程数据结构的个性化动态定制,将审批流程数据的OEM表达映射为XML文档,开发村镇土地审批处理系统。应用结果表明,该系统实现的土地审批数字化处理和规划能辅助决策分析。     

一种类间测试顺序改进算法

确定类的测试顺序是面向对象程序测试中的关键步骤。描述对象关系图模型,提出一种基于对象关系图的类间集成测试顺序改进算法,与Tai和Traon等人提出的算法相比,该算法需要的测试桩较少、效率较高。在Redflag4系统下用Kylix3(C++)对算法进行实现。理论分析和初步实验证明了该算法的可行性和有效性。     

一种优化的混合型A缓冲碰撞检测算法

在分析碰撞检测算法图像空间法与对象空间法各有优劣的基础上,以提高碰撞检测的完全性、唯一性与实时性为目标,提出一种基于图像空间法与对象空间法相结合的A缓冲碰撞检测算法。利用CULLIDE算法的实时性及AABB算法精确性相结合的优点,以A缓冲单元控制精度,可根据不同场景的需要控制A缓冲单元的尺寸。因不需要预处理,避免了不必要的重叠检测,能简单地被硬件加速,其速度比普通的方法约快8倍的速度。不仅适应于刚体与软体模型,而且能够检测自我冲突。     

基于单目视觉的移动机器人导航算法研究进展

基于单目视觉的移动机器人导航的研究,涵盖了机器视觉、模式识别和多目标跟踪多个领域．其算法框架不仅成功应用于移动机器人导航,还为目标检测、识别与跟踪领域的研究提供了可供参考的模型．该综述将以算法发展历史为脉络,结合一些典型系统,通过对关键技术和算法结构的分析比较,总结算法本身的发展前景和由此发展起来的可供相关研究参考的算法框架．     

Topology‐based Smoothing of 2D Scalar Fields with C1‐Continuity

Data sets coming from simulations or sampling of real‐world phenomena often contain noise that hinders their processing and analysis. Automatic filtering and denoising can be challenging: when the nature of the noise is unknown, it is difficult to distinguish between noise and actual data features; in addition, the filtering process itself may introduce “artificial” features into the data set that were not originally present. In this paper, we propose a smoothing method for 2D scalar fields that gives the user explicit control over the data features. We define features as critical points of the given scalar function, and the topological structure they induce (i.e., the Morse‐Smale complex). Feature significance is rated according to topological persistence. Our method allows filtering out spurious features that arise due to noise by means of topological simplification, providing the user with a simple interface that defines the significance threshold, coupled with immediate visual feedback of the remaining data features. In contrast to previous work, our smoothing method guarantees a C¹‐continuous output scalar field with the exact specified features and topological structures.     

Image‐Based Edge Bundles: Simplified Visualization of Large Graphs

We present a new approach aimed at understanding the structure of connections in edge‐bundling layouts. We combine the advantages of edge bundles with a bundle‐centric simplified visual representation of a graph's structure. For this, we first compute a hierarchical edge clustering of a given graph layout which groups similar edges together. Next, we render clusters at a user‐selected level of detail using a new image‐based technique that combines distance‐based splatting and shape skeletonization. The overall result displays a given graph as a small set of overlapping shaded edge bundles. Luminance, saturation, hue, and shading encode edge density, edge types, and edge similarity. Finally, we add brushing and a new type of semantic lens to help navigation where local structures overlap. We illustrate the proposed method on several real‐world graph datasets.