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    • 1. 发明授权
    • Combining error diffusion, dithering and over-modulation for smooth multilevel printing
    • 结合误差扩散,抖动和过调制来平滑多层印刷
    • US06271936B1
    • 2001-08-07
    • US09210311
    • 1998-12-11
    • Qing YuKevin E. Spaulding
    • Qing YuKevin E. Spaulding
    • H04N1405
    • H04N1/4053H04N1/40087
    • A new multitoning technique is proposed that combines error diffusion, blue-noise dithering and over-modulation in an adaptive algorithm to achieve high quality multilevel printing with smooth texture transition. A periodic dither signal is first added to an input digital image wherein the amplitude of the periodic dither signal is a function of the input pixel value for each input pixel. The amplitude of the periodic dither signal is larger for input pixel values near the N output levels, and the amplitude of the periodic dither signal is smaller for input pixel values intermediate to the N output levels to produce a modified input image. Then, a multi-level error diffusion halftoning algorithm is applied to the modified input image wherein the error diffusion halftoning algorithm uses a set of error feedback weights which are adjusted according to the original input pixel value for each input pixel. The sum of the error feedback weights is smaller for input pixel values near the N output levels, and the sum of the error feedback weights is larger for input pixel values intermediate to the N output levels to produce an output multi-level digital image.
    • 提出了一种新的多线程技术,将自适应算法中的误差扩散,蓝噪声抖动和过调制相结合,实现了平滑纹理转换的高质量多层次打印。 周期性抖动信号首先被添加到输入数字图像中,其中周期性抖动信号的幅度是每个输入像素的输入像素值的函数。 周期性抖动信号的幅度对于接近N个输出电平的输入像素值较大,并且周期性抖动信号的幅度对于在N个输出电平之间的输入像素值较小,以产生经修改的输入图像。 然后,将多级误差扩散半色调算法应用于经修改的输入图像,其中误差扩散半色调算法使用根据每个输入像素的原始输入像素值调整的一组误差反馈权重。 误差反馈权重的和对于N个输出电平附近的输入像素值较小,并且对于N个输出电平之间的输入像素值,误差反馈权重之和较大,以产生输出多电平数字图像。
    • 7. 发明授权
    • Mapping network addresses to geographical locations
    • 将网络地址映射到地理位置
    • US08364816B2
    • 2013-01-29
    • US11871810
    • 2007-10-12
    • Chuanxiong GuoJiahe H. WangQing YuYongguang ZhangYunxin Liu
    • Chuanxiong GuoJiahe H. WangQing YuYongguang ZhangYunxin Liu
    • G06F15/16
    • H04L61/20G06F17/30241G06F17/3087
    • A network address mapping system is described. The network address mapping system can identify a set of Web pages, collects information from the Web pages indicating geographical locations (“geolocations”), and correlate the geolocations with the network addresses from which the identified Web pages are served. The collected information can be weighted based on various factors, such as its relative position in a Web page. The collected information can then be used to identify a geolocation. The network mapping system can deduce geolocations for portions of ranges of network addresses based on the score, and can infer geolocations for other portions based on the deduced geolocations. This mapping can then be stored in a database and provided as a geomapping service. The network address mapping system is able to map network addresses to geographical locations. Thereafter, when a user's client computing device accesses a Web server, the Web server can easily and accurately determine a geographical location by querying the database storing the mapping or a geomapping service.
    • 描述网络地址映射系统。 网络地址映射系统可以识别一组网页,从指定地理位置(地理位置)的网页收集信息,并将地理位置与所识别的网页从其提供的网络地址相关联。 所收集的信息可以基于各种因素加权,例如其在网页中的相对位置。 然后可以使用收集的信息来识别地理位置。 网络映射系统可以基于分数推断出部分网络地址范围的地理位置,并且可以基于推导的地理位置来推断其他部分的地理位置。 然后,该映射可以存储在数据库中并作为地理服务提供。 网络地址映射系统能够将网络地址映射到地理位置。 此后,当用户的客户计算设备访问Web服务器时,Web服务器可以通过查询存储映射的数据库或地理位置服务来容易且准确地确定地理位置。
    • 8. 发明申请
    • TOPICS IN RELEVANCE RANKING MODEL FOR WEB SEARCH
    • 用于网络搜索的相关排名模式的主题
    • US20120030200A1
    • 2012-02-02
    • US13271638
    • 2011-10-12
    • Qing YuJun XuHang Li
    • Qing YuJun XuHang Li
    • G06F17/30
    • G06F17/30864
    • Described is a technology by which topics corresponding to web pages are used in relevance ranking of those pages. Topics are extracted from each web page of a set of web pages that are found via a query. For example, text such as nouns may be extracted from the title, anchor texts and URL of a page, and used as the topics. The extracted topics from a page are used to compute a relevance score for that page based on an evaluation of that page's topics against the query. The pages are then ranked relative to one another based at least in part on the relevance score computed for each page, such as by determining a matching level for each page, ranking pages by each level, and ranking pages within each level. Also described is training a model to perform the relevance scoring and/or ranking.
    • 描述了一种技术,通过该技术将与网页相对应的主题用于那些页面的相关性排名。 从通过查询找到的一组网页的每个网页中提取主题。 例如,可以从标题,锚文本和页面的URL中提取诸如名词的文本,并且用作主题。 从页面提取的主题用于根据对该页面的主题对查询的评估来计算该页面的相关性分数。 这些页面至少部分地基于针对每个页面计算的相关性分数相对于彼此进行排名,例如通过确定每个页面的匹配级别,按各级别排序页面以及在每个级别内对页面进行排序。 还描述了训练模型以执行相关性评分和/或排名。
    • 9. 发明申请
    • Topics in Relevance Ranking Model for Web Search
    • 网页搜索相关性排名模型的主题
    • US20090327264A1
    • 2009-12-31
    • US12146430
    • 2008-06-25
    • Qing YuJun XuHang Li
    • Qing YuJun XuHang Li
    • G06F17/30G06F15/18
    • G06F17/30864
    • Described is a technology by which topics corresponding to web pages are used in relevance ranking of those pages. Topics are extracted from each web page of a set of web pages that are found via a query. For example, text such as nouns may be extracted from the title, anchor texts and URL of a page, and used as the topics. The extracted topics from a page are used to compute a relevance score for that page based on an evaluation of that page's topics against the query. The pages are then ranked relative to one another based at least in part on the relevance score computed for each page, such as by determining a matching level for each page, ranking pages by each level, and ranking pages within each level. Also described is training a model to perform the relevance scoring and/or ranking.
    • 描述了一种技术,通过该技术将与网页相对应的主题用于那些页面的相关性排名。 从通过查询找到的一组网页的每个网页中提取主题。 例如,可以从标题,锚文本和页面的URL中提取诸如名词的文本,并且用作主题。 从页面提取的主题用于根据对该页面的主题对查询的评估来计算该页面的相关性分数。 这些页面至少部分地基于针对每个页面计算的相关性分数相对于彼此进行排名,例如通过确定每个页面的匹配级别,按各级别排序页面以及在每个级别内对页面进行排序。 还描述了训练模型以执行相关性评分和/或排名。