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1. 发明授权

US08804005B2 Video concept detection using multi-layer multi-instance learning 有权
标题翻译：使用多层多实例学习的视频概念检测
公开(公告)号：US08804005B2
公开(公告)日：2014-08-12
申请号：US12111202
申请日：2008-04-29
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Zhiwei Gu
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Zhiwei Gu
IPC分类号： G06K9/62 , G06K9/34
CPC分类号： G11B27/28 , G06K9/00711 , G06K9/6282
摘要： Visual concepts contained within a video clip are classified based upon a set of target concepts. The clip is segmented into shots and a multi-layer multi-instance (MLMI) structured metadata representation of each shot is constructed. A set of pre-generated trained models of the target concepts is validated using a set of training shots. An MLMI kernel is recursively generated which models the MLMI structured metadata representation of each shot by comparing prescribed pairs of shots. The MLMI kernel is subsequently utilized to generate a learned objective decision function which learns a classifier for determining if a particular shot (that is not in the set of training shots) contains instances of the target concepts. A regularization framework can also be utilized in conjunction with the MLMI kernel to generate modified learned objective decision functions. The regularization framework introduces explicit constraints which serve to maximize the precision of the classifier.
摘要翻译：视频剪辑中包含的视觉概念基于一组目标概念进行分类。剪辑被分割成镜头，并且构建每个镜头的多层多实例（MLMI）结构化元数据表示。使用一组训练镜头验证了一组预先生成的目标概念训练模型。通过比较规定的拍摄对，递归地生成MLMI内核，以对每个镜头的MLMI结构化元数据表示进行建模。 MLMI内核随后被用于生成学习的客观决策函数，该函数学习用于确定特定镜头（不在该组训练镜头中）是否包含目标概念的实例的分类器。正则化框架也可以与MLMI内核一起使用，以生成修改后的学习目标决策函数。正则化框架引入明确的约束，用于最大化分类器的精度。

2. 发明申请

US20110290882A1 QR CODE DETECTION 审中-公开
标题翻译： QR码检测
公开(公告)号：US20110290882A1
公开(公告)日：2011-12-01
申请号：US12790125
申请日：2010-05-28
申请人： Zhiwei Gu , Matthew R. Scott , Gang Chen , Jonathan Y. Tien
发明人： Zhiwei Gu , Matthew R. Scott , Gang Chen , Jonathan Y. Tien
IPC分类号： G06K7/10
CPC分类号： G06K7/1456
摘要： One or more techniques and/or systems are disclosed for detecting a quick response (QR) code. An area of an image comprising a QR code is localized by combining pixel dynamic scale (DS), black-cell ratio (BR), and edge intensity sum (EIS) criteria determination to identify the QR code. A pattern for the QR code is detected, comprising determining if a position detection pattern (PDP) is located in respective grid areas of a first grid that comprises the QR code, and identifying an alignment pattern (AP), if present. To identify the AP, an AP region is estimated using the PDPs, and a center area of the AP is found by examining respective areas of a second grid comprising the estimated AP region.
摘要翻译：公开了用于检测快速响应（QR）代码的一个或多个技术和/或系统。包括QR码的图像的区域通过组合像素动态尺度（DS），黑色单元比（BR）和边缘强度和（EIS）标准确定来识别QR码来定位。检测QR码的图案，包括确定位置检测图案（PDP）是否位于包括QR码的第一格栅的相应网格区域中，以及如果存在则识别对准图案（AP）。为了识别AP，使用PDP估计AP区域，并且通过检查包括估计的AP区域的第二网格的相应区域来找到AP的中心区域。

3. 发明授权

US09223861B2 Method and system for automatic assignment of identifiers to a graph of entities 有权
标题翻译：将标识符自动分配给实体图形的方法和系统
公开(公告)号：US09223861B2
公开(公告)日：2015-12-29
申请号：US13468320
申请日：2012-05-10
申请人： Balaji Kannan , Aamod Sane , Zhiwei Gu
发明人： Balaji Kannan , Aamod Sane , Zhiwei Gu
IPC分类号： G06F17/30
CPC分类号： G06F17/30734 , G06F17/3089
摘要： Method, system, and programs for providing identifiers to objects. Input data representing a plurality of objects is received and categorized into a plurality of entity categories. A first graph of entities is generated using the plurality of entity categories. The first graph of entities are matched with a second graph of entities. A comparison of object pairs is then made, in which each object pair includes a first object from the first graph of entities and a corresponding second object from the second graph of entities. Identifiers are assigned to each object based on comparing the object pairs.
摘要翻译：用于向对象提供标识符的方法，系统和程序。接收表示多个对象的输入数据并将其分类为多个实体类别。使用多个实体类别生成实体的第一图。实体的第一个图与实体的第二个图匹配。然后进行对象对的比较，其中每个对象对包括来自实体的第一个图形的第一个对象和来自实体的第二个图形的对应的第二个对象。基于比较对象对，将标识符分配给每个对象。

4. 发明申请

US20130304741A1 METHOD AND SYSTEM FOR AUTOMATIC ASSIGNMENT OF IDENTIFIERS TO A GRAPH OF ENTITIES 有权
标题翻译：标识符自动分配到实体图的方法和系统
公开(公告)号：US20130304741A1
公开(公告)日：2013-11-14
申请号：US13468320
申请日：2012-05-10
申请人： Balaji Kannan , Aamod Sane , Zhiwei Gu
发明人： Balaji Kannan , Aamod Sane , Zhiwei Gu
IPC分类号： G06F17/30
CPC分类号： G06F17/30734 , G06F17/3089
摘要： Method, system, and programs for providing identifiers to objects. Input data representing a plurality of objects is received and categorized into a plurality of entity categories. A first graph of entities is generated using the plurality of entity categories. The first graph of entities are matched with a second graph of entities. A comparison of object pairs is then made, in which each object pair includes a first object from the first graph of entities and a corresponding second object from the second graph of entities. Identifiers are assigned to each object based on comparing the object pairs.
摘要翻译：用于向对象提供标识符的方法，系统和程序。接收表示多个对象的输入数据并将其分类为多个实体类别。使用多个实体类别生成实体的第一图。实体的第一个图与实体的第二个图匹配。然后进行对象对的比较，其中每个对象对包括来自实体的第一个图形的第一个对象和来自实体的第二个图形的对应的第二个对象。基于比较对象对，将标识符分配给每个对象。

5. 发明申请

US20090274434A1 VIDEO CONCEPT DETECTION USING MULTI-LAYER MULTI-INSTANCE LEARNING 有权
标题翻译：使用多层次多实例学习的视频概念检测
公开(公告)号：US20090274434A1
公开(公告)日：2009-11-05
申请号：US12111202
申请日：2008-04-29
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Zhiwei Gu
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Zhiwei Gu
IPC分类号： G11B27/00
CPC分类号： G11B27/28 , G06K9/00711 , G06K9/6282
摘要： Visual concepts contained within a video clip are classified based upon a set of target concepts. The clip is segmented into shots and a multi-layer multi-instance (MLMI) structured metadata representation of each shot is constructed. A set of pre-generated trained models of the target concepts is validated using a set of training shots. An MLMI kernel is recursively generated which models the MLMI structured metadata representation of each shot by comparing prescribed pairs of shots. The MLMI kernel is subsequently utilized to generate a learned objective decision function which learns a classifier for determining if a particular shot (that is not in the set of training shots) contains instances of the target concepts. A regularization framework can also be utilized in conjunction with the MLMI kernel to generate modified learned objective decision functions. The regularization framework introduces explicit constraints which serve to maximize the precision of the classifier.
摘要翻译：视频剪辑中包含的视觉概念基于一组目标概念进行分类。剪辑被分割成镜头，并且构建每个镜头的多层多实例（MLMI）结构化元数据表示。使用一组训练镜头验证了一组预先生成的目标概念训练模型。通过比较规定的拍摄对，递归地生成MLMI内核，以对每个镜头的MLMI结构化元数据表示进行建模。 MLMI内核随后被用于生成学习的客观决策函数，该函数学习用于确定特定镜头（不在该组训练镜头中）是否包含目标概念的实例的分类器。正则化框架也可以与MLMI内核一起使用，以生成修改后的学习目标决策函数。正则化框架引入明确的约束，用于最大化分类器的精度。

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