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19 条记录 (耗时 0.015 秒)

    Face Annotation In Streaming Video
    1.
    发明申请
    Face Annotation In Streaming Video 审中-公开
    流媒体视频中的脸部注释

    公开(公告)号：US20080235724A1

    公开(公告)日：2008-09-25

    申请号：US12088001

    申请日：2006-09-19

    申请人： Frank Sassenscheidt ,  Christian Benien ,  Reinhard Kneser

    发明人： Frank Sassenscheidt ,  Christian Benien ,  Reinhard Kneser

    IPC分类号： H04N9/81

    CPC分类号： G06F16/784

    摘要： The invention relates to a system (5, 15) and a method for detecting and annotating faces on-the-fly in video data. The annotation (29) is performed by modifying the pixel content of the video and is thereby independent of file types, protocols and standards. The invention can also perform real-time face-recognition by comparing detected faces with known faces from storage, so that the annotation can contain personal information (38) relating to the face. The invention can be applied at either end of a transmission channel and is particularly applicable in videoconferences, Internet classrooms, etc.

    摘要翻译： 本发明涉及一种用于在视频数据中动态检测和注释面部的系统（5,15）和方法。 通过修改视频的像素内容来执行注释（29），由此独立于文件类型，协议和标准。 本发明还可以通过将检测到的面部与存储器中的已知面部进行比较来执行实时面部识别，使得注释可以包含与脸部有关的个人信息（38）。 本发明可应用于传输通道的任一端，特别适用于视频会议，互联网教室等。

    Retrieving and viewing medical images
    3.
    发明授权
    Retrieving and viewing medical images 有权
    检索和查看医学图像

    公开(公告)号：US09390236B2

    公开(公告)日：2016-07-12

    申请号：US13320956

    申请日：2010-05-17

    申请人： Dieter Geller ,  Reinhard Kneser ,  Yuechen Qian

    发明人： Dieter Geller ,  Reinhard Kneser ,  Yuechen Qian

    IPC分类号： G06F17/30 ,  G06F19/00

    CPC分类号： G06F19/3487 ,  G06F19/00 ,  G06F19/321 ,  G06F19/324 ,  G16H15/00

    摘要： As medical imaging becomes more affordable, and the diversity of diagnostic modalities and therapeutic treatments increase, the amount of data being stored increases, and the problem becomes even more critical. One approach to improve retrieval efficiency of images is to employ semantics to establish a defined set of search and classification terms. However, such semantic systems still require the user to make a selection of the most appropriate term or terms to classify a report or image, and the accuracy of the results are thus dependent on the skill and knowledge of the classifier. According to a first aspect of the invention, a retriever is provided for retrieving a medical image having a searchable attribute, the retriever being configured to interface with a semantic database and an image database, and wherein the searchable attribute is determined by segmenting the medical image, using the anatomical model.

    摘要翻译： 随着医学成像变得更加实惠，并且诊断模式和治疗方法的多样性增加，存储的数据量增加，并且问题变得更加重要。 提高图像检索效率的一种方法是采用语义来建立一组定义的搜索和分类术语。 然而，这样的语义系统仍然需要用户选择最合适的术语或术语来分类报告或图像，并且结果的准确性因此取决于分类器的技能和知识。 根据本发明的第一方面，提供了一种用于检索具有可搜索属性的医学图像的检索器，所述检索器被配置为与语义数据库和图像数据库进行接口，并且其中通过分割医学图像来确定可搜索属性 ，使用解剖模型。

    RETRIEVING AND VIEWING MEDICAL IMAGES
    6.
    发明申请
    RETRIEVING AND VIEWING MEDICAL IMAGES 有权
    检索和查看医学图像

    公开(公告)号：US20120066241A1

    公开(公告)日：2012-03-15

    申请号：US13320956

    申请日：2010-05-17

    申请人： Dieter Geller ,  Reinhard Kneser ,  Yuechen Qian

    发明人： Dieter Geller ,  Reinhard Kneser ,  Yuechen Qian

    IPC分类号： G06F17/30

    CPC分类号： G06F19/3487 ,  G06F19/00 ,  G06F19/321 ,  G06F19/324 ,  G16H15/00

    摘要： As medical imaging becomes more affordable, and the diversity of diagnostic modalities and therapeutic treatments increase, the amount of data being stored increases, and the problem becomes even more critical. One approach to improve retrieval efficiency of images is to employ semantics to establish a defined set of search and classification terms. However, such semantic systems still require the user to make a selection of the most appropriate term or terms to classify a report or image, and the accuracy of the results are thus dependent on the skill and knowledge of the classifier. According to a first aspect of the invention, a retriever is provided for retrieving a medical image having a searchable attribute, the retriever being configured to interface with a semantic database and an image database, and wherein the searchable attribute is determined by segmenting the medical image, using the anatomical model.

    摘要翻译： 随着医学成像变得更加实惠，并且诊断模式和治疗方法的多样性增加，存储的数据量增加，并且问题变得更加重要。 提高图像检索效率的一种方法是采用语义来建立一组定义的搜索和分类术语。 然而，这样的语义系统仍然需要用户选择最合适的术语或术语来分类报告或图像，并且结果的准确性因此取决于分类器的技能和知识。 根据本发明的第一方面，提供了一种用于检索具有可搜索属性的医学图像的检索器，所述检索器被配置为与语义数据库和图像数据库进行接口，并且其中通过分割医学图像来确定可搜索属性 ，使用解剖模型。

    Single-count backing-off method of determining N-gram language model values
    7.
    发明授权
    Single-count backing-off method of determining N-gram language model values 失效
    确定N-gram语言模型值的单次备份方法

    公开(公告)号：US5745876A

    公开(公告)日：1998-04-28

    申请号：US642012

    申请日：1996-05-02

    申请人： Reinhard Kneser ,  Hermann Ney

    发明人： Reinhard Kneser ,  Hermann Ney

    IPC分类号： G10L15/197 ,  G10L5/06

    CPC分类号： G10L15/197

    摘要： For the recognition of coherently spoken speech with a large vocabulary, language model values which take into account the probability of word sequences are considered at word transitions. Prior to the recognition, these language model values are derived on the basis of training speech signals. If the amount of training data is kept within sensible limits, not all word sequences will actually occur, so that the language model values for, for example an N-gram language model must be determined from word sequences of N-1 words actually occurring. In accordance with the invention, these reduced word sequences from each different, complete word sequence are counted only once, irrespective of the actual frequency of occurrence of the complete word sequence or only reduced training sequences which occur exactly once in the training data are taken into account.

    摘要翻译： 为了识别具有较大词汇量的相干语音，考虑到字序列的概率的语言模型值在词转换中被考虑。 在识别之前，这些语言模型值是基于训练语音信号导出的。 如果训练数据的数量保持在明显的限度内，并不是所有的字序列实际上都会发生，因此，例如N-gram语言模型的语言模型值必须从实际出现的N-1个字的单词序列中确定。 根据本发明，来自每个不同的完整字序列的这些缩减的字序列仅被计数一次，而不考虑完整字序列的实际发生频率，或者仅将训练数据中正好出现一次的训练序列减少 帐户。

    Speech recognition method with language model adaptation
    9.
    发明授权
    Speech recognition method with language model adaptation 失效
    语言识别方法与语言模型适应

    公开(公告)号：US6157912A

    公开(公告)日：2000-12-05

    申请号：US033202

    申请日：1998-03-02

    申请人： Reinhard Kneser ,  Jochen Peters ,  Dietrich Klakow

    发明人： Reinhard Kneser ,  Jochen Peters ,  Dietrich Klakow

    IPC分类号： G10L15/065 ,  G10L15/183 ,  G10L9/00

    CPC分类号： G10L15/065 ,  G10L15/183

    摘要： Language models which take into account the probabilities of word sequences are used in speech recognition, in particular in the recognition of fluently spoken language with a wide vocabulary, in order to increase the recognition reliability. These models are obtained from comparatively large quantities of text and accordingly represent values which were averaged over several texts. This means, however, that the language model is not well adapted to peculiarities of a special text. To achieve such an adaptation of a given language model to a special text on the basis of only a short text fragment, according to the invention, it is suggested that first the unigram language model is adapted with the short text and, in dependence thereon, the M-gram language model is subsequently adapted. A method is described for adapting the unigram language model values which automatically carries out a subdivision of the words into semantic classes.

    摘要翻译： 考虑到词序概率的语言模型用于语音识别，特别是在识别具有较宽词汇的流畅语言的情况下，以增加识别的可靠性。 这些模型是从相对大量的文本中获得的，因此代表在几个文本中平均的值。 然而，这意味着语言模型不能很好地适应特殊文本的特殊性。 为了根据本发明，将特定语言模型的特定语言模型适应于特殊文本，根据本发明，建议首先将单词语言模型与短文本进行匹配，并且依赖于该文本， 随后调整了M-gram语言模型。 描述了一种用于适应单字语言模型值的方法，其自动地将单词的细分实现为语义类。