Effect of Al in Ti－microalloyed Mn－Ni－Mo welding wire on microstructure and toughness of deposited metal

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An interactive lung field segmentation scheme with automated capability

Feasible clinical application of any automated segmenting methodology demands more than just accuracy. Amendment to the automated delineation is necessary when the algorithm fails, however, integrated solution to such a scenario is largely absent in literature. Hence in this survey we devised an architecture that does both the automated and interactive lung field localizations using a single segmenting engine—random walker algorithm—so that intuitive amendment is only necessary when the automated generated delineation is unsatisfactory. The algorithm proceeds by first extracting 18 intensity profiles running horizontally, each of them equally spacing apart, and in each intensity profile three extreme points denoting the two lungs and the esophagus are determined through profile matching. This done, the algorithm removes profiles that do not intersect with the lung, and the rest of the extreme points are plugged into random walker algorithm to perform segmentation. The achieved accuracy in localization by the above was 0.8875 in terms of overlap measure (the maximum value for this parameter is 1) over 341 images. In the case where unsatisfactory delineation prompts amendment necessary, the user can interactively segment the lung by just a shift on some of the previous-determined points to the desired locations, and random walker algorithm is run again with the amended input. By such a fusion, the benefits of both the automated and interactive segmentation are shared in a single architecture.     

Learning Image-Text Associations

Web information fusion can be defined as the problem of collating and tracking information related to specific topics on the World Wide Web. Whereas most existing work on Web information fusion has focused on text-based multidocument summarization, this paper concerns the topic of image and text association, a cornerstone of cross-media Web information fusion. Specifically, we present two learning methods for discovering the underlying associations between images and texts based on small training data sets. The first method based on vague transformation measures the information similarity between the visual features and the textual features through a set of predefined domain-specific information categories. Another method uses a neural network to learn direct mapping between the visual and textual features by automatically and incrementally summarizing the associated features into a set of information templates. Despite their distinct approaches, our experimental results on a terrorist domain document set show that both methods are capable of learning associations between images and texts from a small training data set.     

基于XML中间件的分布式异构数据库的数据分片策略研究

主要研究XML中间件的分布式异构数据库的数据分片策略,以便能够并行处理XML查询。为了描述XML数据分片,提出了对象树、子树的概念,将一棵XML数据树分割成对象树和子树,对象树在各个站点上复制,子树标签化后均匀分片到各个站点上。为了实现用户数据分片查询工作量的均衡,对最大树深的子树采用减治法策略,减治法策略后的子树又分配到一个新的站点上。基于这一思想,提出并实现其算法。实验结果证明：在分布式异构数据库的检索、查询和访问中,基于对象树、子树的数据分片算法的性能要比PSPIB算法性能优越。     

基于光纤环网的隧道火灾监控系统研究

文章对基于光纤环网的隧道群火灾报警系统进行研究,分析了其优缺点,提出了解决办法,并完成改造,应用于实际.     

Layered image coding using the DCT pyramid

A block-based subband image coder that exploits the ability to perform decimation in the discrete cosine transform (DCT) domain to effect a pyramidal data structure is described. The proposed "DCT pyramid" has a distinct feature of improved image rendition properties without the associated blocking artifacts at low bit-rates.