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    Image Organization
    1.
    发明申请
    Image Organization 有权
    图像组织

    公开(公告)号：US20080226174A1

    公开(公告)日：2008-09-18

    申请号：US11725129

    申请日：2007-03-15

    申请人： Gang Hua ,  Steven M. Drucker ,  Michael Revow ,  Paul A. Viola ,  Richard Zemel

    发明人： Gang Hua ,  Steven M. Drucker ,  Michael Revow ,  Paul A. Viola ,  Richard Zemel

    IPC分类号： G06K9/68 ,  G06K9/46

    CPC分类号： G06K9/00228 ,  G06K9/6251

    摘要： A system for organizing images includes an extraction component that extracts visual information (e.g., faces, scenes, etc.) from the images. The extracted visual information is provided to a comparison component which computes similarity confidence data between the extracted visual information. The similarity confidence data is an indication of the likelihood that items of extracted visual information are similar. The comparison component then generates a visual distribution of the extracted visual information based upon the similarity confidence data. The visual distribution can include groupings of the extracted visual information based on computed similarity confidence data. For example, the visual distribution can be a two-dimensional layout of faces organized based on the computed similarity confidence data—with faces in closer proximity faces computed to have a greater probability of representing the same person. The visual distribution can then be utilized by a user to sort, organize and/or tag images.

    摘要翻译： 用于组织图像的系统包括从图像中提取视觉信息（例如，面部，场景等）的提取组件。 提取的视觉信息被提供给计算提取的视觉信息之间的相似性置信度数据的比较部件。 相似性置信度数据是提取的视觉信息的项目相似的可能性的指示。 然后，比较组件基于相似性置信度数据生成所提取的视觉信息的视觉分布。 视觉分布可以包括基于计算的相似性置信度数据提取的视觉信息的分组。 例如，视觉分布可以是基于所计算的相似性置信度数据组织的面部的二维布局，其中更接近的面中的面被计算为具有更大的代表同一人的概率。 然后用户可以利用视觉分布来对图像进行分类，组织和/或标记。

    Image organization based on image content
    2.
    发明授权
    Image organization based on image content 有权
    基于图像内容的图像组织

    公开(公告)号：US08027541B2

    公开(公告)日：2011-09-27

    申请号：US11725129

    申请日：2007-03-15

    申请人： Gang Hua ,  Steven M. Drucker ,  Michael Revow ,  Paul A. Viola ,  Richard Zemel

    发明人： Gang Hua ,  Steven M. Drucker ,  Michael Revow ,  Paul A. Viola ,  Richard Zemel

    IPC分类号： G06K9/46

    CPC分类号： G06K9/00228 ,  G06K9/6251

    摘要： A system for organizing images includes an extraction component that extracts visual information (e.g., faces, scenes, etc.) from the images. The extracted visual information is provided to a comparison component which computes similarity confidence data between the extracted visual information. The similarity confidence data is an indication of the likelihood that items of extracted visual information are similar. The comparison component then generates a visual distribution of the extracted visual information based upon the similarity confidence data. The visual distribution can include groupings of the extracted visual information based on computed similarity confidence data. For example, the visual distribution can be a two-dimensional layout of faces organized based on the computed similarity confidence data—with faces in closer proximity faces computed to have a greater probability of representing the same person. The visual distribution can then be utilized by a user to sort, organize and/or tag images.

    摘要翻译： 用于组织图像的系统包括从图像中提取视觉信息（例如，面部，场景等）的提取组件。 提取的视觉信息被提供给计算提取的视觉信息之间的相似性置信度数据的比较部件。 相似性置信度数据是提取的视觉信息的项目相似的可能性的指示。 然后，比较组件基于相似性置信度数据生成所提取的视觉信息的视觉分布。 视觉分布可以包括基于计算的相似性置信度数据提取的视觉信息的分组。 例如，视觉分布可以是基于所计算的相似性置信度数据组织的面部的二维布局，其中更接近的面中的面被计算为具有更大的代表同一人的概率。 然后用户可以利用视觉分布来对图像进行分类，组织和/或标记。

    Face Recognition Using Discriminatively Trained Orthogonal Tensor Projections
    3.
    发明申请
    Face Recognition Using Discriminatively Trained Orthogonal Tensor Projections 有权
    使用歧视性训练正交张量投影的人脸识别

    公开(公告)号：US20080310687A1

    公开(公告)日：2008-12-18

    申请号：US11763909

    申请日：2007-06-15

    申请人： Gang Hua ,  Paul A. Viola ,  Steven M. Drucker ,  Michael Revow

    发明人： Gang Hua ,  Paul A. Viola ,  Steven M. Drucker ,  Michael Revow

    IPC分类号： G06K9/00

    CPC分类号： G06K9/00288 ,  G06K9/6232

    摘要： Systems and methods are described for face recognition using discriminatively trained orthogonal rank one tensor projections. In an exemplary system, images are treated as tensors, rather than as conventional vectors of pixels. During runtime, the system designs visual features—embodied as tensor projections—that minimize intraclass differences between instances of the same face while maximizing interclass differences between the face and faces of different people. Tensor projections are pursued sequentially over a training set of images and take the form of a rank one tensor, i.e., the outer product of a set of vectors. An exemplary technique ensures that the tensor projections are orthogonal to one another, thereby increasing ability to generalize and discriminate image features over conventional techniques. Orthogonality among tensor projections is maintained by iteratively solving an ortho-constrained eigenvalue problem in one dimension of a tensor while solving unconstrained eigenvalue problems in additional dimensions of the tensor.

    摘要翻译： 使用区分训练的正交秩一张量投影描述用于人脸识别的系统和方法。 在示例性系统中，图像被视为张量，而不是像传统的像素矢量。 在运行期间，系统设计视觉特征 - 体现为张量投影 - 最大限度地减少不同人脸部和脸部之间的类间差异，从而最大限度地减少同一脸部实例之间的差异。 张量投影在训练图像集上顺序追溯，并采取一级张量的形式，即一组向量的外积。 示例性技术确保张量投影彼此正交，从而增加了与常规技术相比的概括和区分图像特征的能力。 通过迭代求解张量的一维中的邻域约束特征值问题，同时解决张量的附加维度中的无约束特征值问题，维持张量投影中的正交性。

    Recognition of mathematical expressions
    4.
    发明申请
    Recognition of mathematical expressions 有权
    数学表达式的识别

    公开(公告)号：US20080260251A1

    公开(公告)日：2008-10-23

    申请号：US11788190

    申请日：2007-04-19

    申请人： Goran Predovic ,  Ahmad Abdulkader ,  Bodin Dresevic ,  Paul A. Viola ,  Milan Vukosavljevic

    发明人： Goran Predovic ,  Ahmad Abdulkader ,  Bodin Dresevic ,  Paul A. Viola ,  Milan Vukosavljevic

    IPC分类号： G06K9/00

    CPC分类号： G06K9/00422 ,  G06K9/726 ,  G06K2209/01

    摘要： In embodiments consistent with the subject matter of this disclosure, a user may input strokes as digital ink to a processing device. The processing device may partition the input strokes into multiple regions of strokes. A first recognizer and a second recognizer may score grammar objects included in regions and represented by chart entries. The scores may be converted to a converted score, which may have at least a near standard normal distribution. The processing device may present a recognition result based on highest converted scores according to a recurrence formula. The processing device may receive a correction hint with respect to misrecognized strokes and may add a penalty score with respect to chart entries representing grammar objects breaking the correction hint. Incremental recognition may be performed when a pause is detected during inputting of strokes.

    摘要翻译： 在与本公开的主题相一致的实施例中，用户可以将笔画作为数字墨水输入到处理设备。 处理装置可以将输入笔划划分成多个笔画区域。 第一识别器和第二识别器可以对包括在区域中的语法对象进行评分并由图表条目表示。 得分可以转换成转换得分，其可以具有至少近标准正态分布。 处理装置可以根据递归公式提供基于最高转换分数的识别结果。 处理设备可以接收关于错误识别的笔画的校正提示，并且可以相对于表示打破校正提示的语法对象的图表条目添加惩罚分数。 当在笔画输入期间检测到暂停时，可以执行增量识别。

    Parsing of ink annotations
    5.
    发明申请
    Parsing of ink annotations 审中-公开
    解析墨迹注释

    公开(公告)号：US20080195931A1

    公开(公告)日：2008-08-14

    申请号：US11589028

    申请日：2006-10-27

    申请人： Sashi Raghupathy ,  Paul A. Viola ,  Michael Shilman ,  Xin Wang

    发明人： Sashi Raghupathy ,  Paul A. Viola ,  Michael Shilman ,  Xin Wang

    IPC分类号： G06F17/00

    CPC分类号： G06K9/00402

    摘要： Annotation recognition and parsing is accomplished by first recognizing and grouping shapes such that relationships between the annotations and the underlying text and/or images can be determined. The recognition and grouping is followed by categorization of recognized annotations according to predefined types. The classification may be according to functionality, relation to content, and the like. In a third phase, the annotations are anchored to the underlying text or images they are found to be related to.

    摘要翻译： 注释识别和解析是通过首先识别和分组形状来实现的，使得可以确定注释和底层文本和/或图像之间的关系。 识别和分组之后是根据预定义类型对所识别的注释进行分类。 分类可以根据功能，与内容的关系等。 在第三阶段，注释被锚定到被发现与之相关的底层文本或图像。

    Method for comparing two trinary logic representations in the process of customizing radio broadcasting
    6.
    发明授权
    Method for comparing two trinary logic representations in the process of customizing radio broadcasting 失效
    在定制无线电广播过程中比较两个三维逻辑表示的方法

    公开(公告)号：US07058694B1

    公开(公告)日：2006-06-06

    申请号：US09656950

    申请日：2000-09-07

    申请人： Jeremy S. De Bonet ,  Paul A. Viola

    发明人： Jeremy S. De Bonet ,  Paul A. Viola

    IPC分类号： G06F15/16

    CPC分类号： H04L29/06027 ,  H04L65/4092 ,  H04L65/602 ,  H04L67/30 ,  H04L67/306 ,  H04L69/329 ,  Y10S707/99933

    摘要： A method for efficiently comparing two trinary logic representations, including the steps of creating a first data structure (a VALUE data structure) representative of a first set of properties; creating a second data structure (a KNOWN data structure) representative of whether the first set of properties is known; creating a third data structure (a TARGET data structure) representative of a target set of properties; creating a fourth data structure (a WANT data structure) representative of whether the target set of properties is wanted; and comparing the first, second, third, and fourth data structures using bit-wise binary operations to determine whether the first set of known properties are wanted as a target set of properties. In exemplary embodiments, the bit-wise binary operations are performed according to the Boolean equation: (not WANT) or (KNOWN and ((TARGET xor VALUE))). Alternatively, the bit-wise binary operation are performed according to the Boolean equation: (not WANT) or (KNOWN and ((TARGET and VALUE) or ((not TARGET) and (not (VALUE))). These data structures may be any size computer word, including 16 and 32-bit words.

    摘要翻译： 一种用于有效地比较两个二进制逻辑表示的方法，包括创建代表第一组属性的第一数据结构（VALUE数据结构）的步骤; 创建代表第一组属性是否已知的第二数据结构（KNOWN数据结构）; 创建代表目标特性集的第三数据结构（TARGET数据结构）; 创建代表目标属性集是否需要的第四数据结构（WANT数据结构）; 以及使用逐位二进制运算来比较第一，第二，第三和第四数据结构，以确定是否需要将第一组已知属性作为目标属性集合。 在示例性实施例中，根据布尔方程式执行逐位二进制运算：（不是WANT）或（KNOWN和（（TARGET XOR VALUE）））。 或者，根据布尔方程（不是WANT）或（KNOWN和（（TARGET和VALUE）或（（不是TARGET）和（不是（VALUE）））来执行逐位二进制运算。这些数据结构可以是 任何大小的计算机字，包括16位和32位字。

    System and method for detecting objects in images
    7.
    发明授权
    System and method for detecting objects in images 有权
    用于检测图像中物体的系统和方法

    公开(公告)号：US07020337B2

    公开(公告)日：2006-03-28

    申请号：US10200464

    申请日：2002-07-22

    申请人： Paul A. Viola ,  Michael J. Jones

    发明人： Paul A. Viola ,  Michael J. Jones

    IPC分类号： G06K9/62 ,  G06K9/00

    CPC分类号： G06K9/00228

    摘要： A method detects an object, such a face, in an image. The image is first partitioned into patches of various sizes using either an integral image or a Gaussian pyramid. Features in each patch are evaluated to determine a cumulative score. The evaluating is repeated while the cumulative score is within a range of an acceptance threshold and a rejection threshold, and otherwise the image is rejected when the accumulated score is less than the rejection threshold and accepted as including the object when the cumulative score is greater than the acceptance threshold.

    摘要翻译： 方法检测图像中的物体（例如脸部）。 首先使用积分图像或高斯金字塔将图像分割成各种尺寸的斑块。 评估每个补丁中的特征以确定累积分数。 当累积分数在接受阈值和拒绝阈值的范围内时，重复评估，否则当累积分数小于拒绝阈值时拒绝图像，并且当累积分数大于接受阈值时被接受为包括对象 验收门槛。

    Grammatical parsing of document visual structures
    9.
    发明授权
    Grammatical parsing of document visual structures 有权
    文字视觉结构的语法解析

    公开(公告)号：US08249344B2

    公开(公告)日：2012-08-21

    申请号：US11173280

    申请日：2005-07-01

    申请人： Paul A. Viola ,  Michael Shilman

    发明人： Paul A. Viola ,  Michael Shilman

    IPC分类号： G06K9/34 ,  G06K9/72

    CPC分类号： G06K9/726 ,  G06F17/271 ,  G06K2209/01

    摘要： A two-dimensional representation of a document is leveraged to extract a hierarchical structure that facilitates recognition of the document. The visual structure is grammatically parsed utilizing two-dimensional adaptations of statistical parsing algorithms. This allows recognition of layout structures (e.g., columns, authors, titles, footnotes, etc.) and the like such that structural components of the document can be accurately interpreted. Additional techniques can also be employed to facilitate document layout recognition. For example, grammatical parsing techniques that utilize machine learning, parse scoring based on image representations, boosting techniques, and/or “fast features” and the like can be employed to facilitate in document recognition.

    摘要翻译： 利用文档的二维表示来提取便于识别文档的层次结构。 使用统计解析算法的二维适应来语法解析视觉结构。 这允许识别布局结构（例如，列，作者，标题，脚注等）等，使得可以准确地解释文档的结构组件。 还可以采用附加技术来促进文档布局识别。 例如，可以采用利用机器学习，基于图像表示的分析评分，增强技术和/或“快速特征”等的语法解析技术，以促进文档识别。