基于Mean Shift算法和灰度模板匹配的运动目标主动跟踪

该文提出了一种综合Mean Shift算法和灰度模板匹配的主动跟踪算法。该算法利用灰度模板匹配与运动目标在图像的位置无关的特点,在视角和焦距发生变化后用灰度模板进行穷尽搜索,再用匹配结果更新Mean Shift搜索窗口,解决了Mean Shift算法要已知目标区域才能正确跟踪的问题。该算法能在视角和焦距发生变化的情况下能正确的跟踪运动目标并能使被跟踪的运动目标始终保持在图像的中心区域。实验表明,该算法具有较好的可行性。     

基于混合高斯模型的运动目标检测

提出了一种新的基于HSV颜色空间的阴影检测和误判检测的自适应背景模型运动目标检测算法,并将其应用于运动物体的分割。该算法较好地解决了背景模型的提取、更新、背景扰动、外界光照变化等问题。实验结果表明了该算法的实时性、可靠性和准确性较好。     

主动对象模式在NMS适配器中的应用研究

国际电信联盟将电信管理网分为五层:网元功能层、网元管理层、网络管理层、业务管理层、商业管理层,其中网络管理层的网管属于高层网管,管理着大量网元层网管,因此,网络层网管南向适配器一般采用分布式部署。但是在分布式网络层网管的南向适配器的登录过程中,如果是同步调用过程,就会花费大量时间。针对这种情况,本文提出了采用ACE提供的主动对象模式,这种模式可以构建线程池以实现多线程调用,以异步调用的方式登录网络层网管的南向适配器,从而解决了登录时间过长的问题。     

多模板多格式论文校验与自动排版系统的研究

按照目前学术界对论文格式的规范要求,针对依靠人工排版和校验的方法的费时、低效等缺点,研究并设计了一个多模板多格式的论文校验与自动排版系统,采用对象链接与嵌入(OLE)技术实现C#与Word相融合的方法,实现了模板文件导入、标准格式导出、论文格式校验、自动排版输出等功能。该系统已经投入实际应用,结果表明,使文档的格式校验和排版的质量和效率得到很大提高。     

Mesh‐Free Discrete Laplace–Beltrami Operator

In this work we propose a new discretization method for the Laplace–Beltrami operator defined on point‐based surfaces. In contrast to the existing point‐based discretization techniques, our approach does not rely on any triangle mesh structure, turning out truly mesh‐free. Based on a combination of Smoothed Particle Hydrodynamics and an optimization procedure to estimate area elements, our discretization method results in accurate solutions while still being robust when facing abrupt changes in the density of points. Moreover, the proposed scheme results in numerically stable discrete operators. The effectiveness of the proposed technique is brought to bear in many practical applications. In particular, we use the eigenstructure of the discrete operator for filtering and shape segmentation. Point‐based surface deformation is another application that can be easily carried out from the proposed discretization method.     

Patch‐Collaborative Spectral Point‐Cloud Denoising

We present a new framework for point cloud denoising by patch‐collaborative spectral analysis. A collaborative generalization of each surface patch is defined, combining similar patches from the denoised surface. The Laplace–Beltrami operator of the collaborative patch is then used to selectively smooth the surface in a robust manner that can gracefully handle high levels of noise, yet preserves sharp surface features. The resulting denoising algorithm competes favourably with state‐of‐the‐art approaches, and extends patch‐based algorithms from the image processing domain to point clouds of arbitrary sampling. We demonstrate the accuracy and noise‐robustness of the proposed algorithm on standard benchmark models as well as range scans, and compare it to existing methods for point cloud denoising.     

Animation‐Aware Quadrangulation

Geometric meshes that model animated characters must be designed while taking into account the deformations that the shape will undergo during animation. We analyze an input sequence of meshes with point‐to‐point correspondence, and we automatically produce a quadrangular mesh that fits well the input animation. We first analyze the local deformation that the surface undergoes at each point, and we initialize a cross field that remains as aligned as possible to the principal directions of deformation throughout the sequence. We then smooth this cross field based on an energy that uses a weighted combination of the initial field and the local amount of stretch. Finally, we compute a field‐aligned quadrangulation with an off‐the‐shelf method. Our technique is fast and very simple to implement, and it significantly improves the quality of the output quad mesh and its suitability for character animation, compared to creating the quad mesh based on a single pose. We present experimental results and comparisons with a state‐of‐the‐art quadrangulation method, on both sequences from 3D scanning and synthetic sequences obtained by a rough animation of a triangulated model.     

Spatially aware feature selection and weighting for object retrieval

Many recent image retrieval methods are based on the “bag-of-words” (BoW) model with some additional spatial consistency checking. This paper proposes a more accurate similarity measurement that takes into account spatial layout of visual words in an offline manner. The similarity measurement is embedded in the standard pipeline of the BoW model, and improves two features of the model: i) latent visual words are added to a query based on spatial co-occurrence, to improve query recall; and ii) weights of reliable visual words are increased to improve the precision. The combination of these methods leads to a more accurate measurement of image similarity. This is similar in concept to the combination of query expansion and spatial verification, but does not require query time processing, which is too expensive to apply to full list of ranked results. Experimental results demonstrate the effectiveness of our proposed method on three public datasets.     

Learning structured visual dictionary for object tracking

In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.