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    • 4. 发明授权
    • Systems and methods for generating multi-level hypervideo summaries
    • 用于生成多级超视图摘要的系统和方法
    • US07480442B2
    • 2009-01-20
    • US10612428
    • 2003-07-02
    • Andreas GirgensohnFrank M. Shipman, IIILynn D. Wilcox
    • Andreas GirgensohnFrank M. Shipman, IIILynn D. Wilcox
    • G11B27/00H04N5/93H04N5/91H04N9/00
    • G11B27/105G11B27/034G11B27/34G11B2220/2562
    • A hypervideo summary comprised of multiple levels of related content and appropriate navigational links can be automatically generated from a media file such as a linear video. A number of algorithms and selection criteria can be used to modify how such a summary is generated. Viewers of an automatically-generated hypervideo summary can interactively select the amount of detail displayed for each portion of the summary. This selection can be done by following explicit navigational links, or by changing between media channels that are mapped to the various levels of related content.This description is not intended to be a complete description of, or limit the scope of, the invention. Other features, aspects, and objects of the invention can be obtained from a review of the specification, the figures, and the claims.
    • 可以从诸如线性视频的媒体文件自动地生成包括多级相关内容和适当导航链接的超视频摘要。 可以使用许多算法和选择标准来修改如何生成摘要。 自动生成的超视频摘要的查看者可以交互地选择为摘要的每个部分显示的详细信息量。 该选择可以通过以下显式导航链接,或者通过在映射到相关内容的各个级别的媒体频道之间进行改变来完成。 本说明书不是对本发明的完整描述或限制本发明的范围。 本发明的其它特征,方面和目的可以通过对说明书,附图和权利要求的评述来获得。
    • 7. 发明授权
    • Word spotting in bitmap images using context-sensitive character models
without baselines
    • 使用没有基线的上下文相关的角色模型,在位图图像中发现文字
    • US5592568A
    • 1997-01-07
    • US387958
    • 1995-02-13
    • Lynn D. WilcoxFrancine R. Chen
    • Lynn D. WilcoxFrancine R. Chen
    • G06K9/70G06F17/30G06K9/32G06K9/46G06K9/50G06K9/62G06K9/72G06T7/00G06K9/68
    • G06K9/72G06K9/32G06K9/6297G06K2209/01
    • Font-independent spotting of user-defined keywords in a scanned image. Word identification is based on features of the entire word without the need for segmentation or OCR, and without the need to recognize non-keywords. Font-independent character models are created using hidden Markov models (HMMs) and arbitrary keyword models are built from the character HMM components. Word or text line bounding boxes are extracted from the image, a set of features based on the word shape, (and preferably also the word internal structure) within each bounding box is extracted, this set of features is applied to a network that includes one or more keyword HMMs, and a determination is made. The identification of word bounding boxes for potential keywords includes the steps of reducing the image (say by 2x) and subjecting the reduced image to vertical and horizontal morphological closing operations. The bounding boxes of connected components in the resulting image are then used to hypothesize word or text line bounding boxes, and the original bitmaps within the boxes are used to hypothesize words. In a particular embodiment, a range of structuring elements is used for the closing operations to accommodate the variation of inter- and intra-character spacing with font and font size.
    • 在扫描图像中用户定义的关键字的字体独立检测。 词识别基于整个词的特征,而不需要分割或OCR,并且不需要识别非关键词。 使用隐马尔可夫模型(HMM)创建字体无关的字符模型,并且使用字符HMM组件构建任意关键字模型。 从图像中提取词或文本行边界框,提取基于每个边界框内的单词形状(并且优选地也称为单词内部结构)的一组特征,该特征集合被应用于包括一个 或更多关键字HMM,并进行确定。 用于潜在关键词的单词界限框的识别包括以下步骤:减少图像(例如,2x),并使缩小后的图像进行垂直和水平形态关闭操作。 然后,使用所得到的图像中的连接分量的边界框来假设单词或文本行界限框,并且使用框内的原始位图来假设单词。 在特定实施例中,结构化元素的范围用于关闭操作以适应字体和字体间距与字体间距的变化。
    • 8. 发明授权
    • Word spotting in bitmap images using word bounding boxes and hidden
Markov models
    • 使用字边界框和隐马尔可夫模型在位图图像中发现字
    • US5438630A
    • 1995-08-01
    • US991913
    • 1992-12-17
    • Francine R. ChenLynn D. WilcoxDan S. Bloomberg
    • Francine R. ChenLynn D. WilcoxDan S. Bloomberg
    • G06K9/36G06F17/15G06F17/30G06K9/20G06K9/46G06K9/50G06K9/62G06K9/70G06K9/72G06T7/00
    • G06K9/00463G06K9/6297G06K9/72G06K2209/01
    • Font-independent spotting of user-defined keywords in a scanned image. Word identification is based on features of the entire word without the need for segmentation or OCR, and without the need to recognize non-keywords. Font-independent character models are created using hidden Markov models (HMMs) and arbitrary keyword models are built from the character HMM components. Word or text line bounding boxes are extracted from the image, a set of features based on the word shape, (and preferably also the word internal structure) within each bounding box is extracted, this set of features is applied to a network that includes one or more keyword HMMs, and a determination is made. The identification of word bounding boxes for potential keywords includes the steps of reducing the image (say by 2.times.) and subjecting the reduced image to vertical and horizontal morphological closing operations. The bounding boxes of connected components in the resulting image are then used to hypothesize word or text line bounding boxes, and the original bitmaps within the boxes are used to hypothesize words. In a particular embodiment, a range of structuring elements is used for the closing operations to accommodate the variation of inter- and intra-character spacing with font and font size.
    • 在扫描图像中用户定义的关键字的字体独立检测。 词识别基于整个词的特征,而不需要分割或OCR,并且不需要识别非关键字。 使用隐马尔可夫模型(HMM)创建字体无关的字符模型,并且使用字符HMM组件构建任意关键字模型。 从图像中提取词或文本行边界框,提取基于每个边界框内的单词形状(并且优选地也称为单词内部结构)的一组特征,该特征集合被应用于包括一个 或更多关键字HMM,并进行确定。 用于潜在关键词的单词界限框的识别包括以下步骤:减少图像(例如,2x),并使缩小后的图像进行垂直和水平形态关闭操作。 然后,使用所得到的图像中的连接分量的边界框来假设单词或文本行界限框,并且使用框内的原始位图来假设单词。 在特定实施例中,结构化元素的范围用于关闭操作以适应字体和字体间距与字体间距的变化。