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1. 发明授权

US08983941B1 Visual content retrieval 有权
公开(公告)号：US08983941B1
公开(公告)日：2015-03-17
申请号：US13621039
申请日：2012-09-15
申请人： Erik Murphy-Chutorian , Charles J. Rosenberg , Nemanja Petrovic , Sergey Ioffe , Sean O'Malley
发明人： Erik Murphy-Chutorian , Charles J. Rosenberg , Nemanja Petrovic , Sergey Ioffe , Sean O'Malley
IPC分类号： G06F17/30
CPC分类号： G06K9/46 , G06F17/30247 , G06F17/30265
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating image search results. One of the methods includes receiving first image search results responsive to a text query, each first image search result associated with a respective first score indicating a relevance of an image represented by the first image search result to the text query. Second image search results responsive to a query image are received, each second image search result associated with a respective second score indicating a measure of similarity between an image represented by the second image search result and the query image. A set of final image search results is selected including combining first scores and second scores of the selected first image search results. The final image search results are ordered by similarity to the query image.

2. 发明授权

US09043316B1 Visual content retrieval 有权
标题翻译：视觉内容检索
公开(公告)号：US09043316B1
公开(公告)日：2015-05-26
申请号：US13433137
申请日：2012-03-28
申请人： Erik Murphy-Chutorian , Charles J. Rosenberg , Nemanja Petrovic , Sergey Ioffe , Sean O'Malley
发明人： Erik Murphy-Chutorian , Charles J. Rosenberg , Nemanja Petrovic , Sergey Ioffe , Sean O'Malley
IPC分类号： G06F17/30 , G06K9/46
CPC分类号： G06K9/46 , G06F17/30247 , G06F17/30265
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating image search results. One of the methods includes receiving first image search results responsive to a text query, each first image search result associated with a respective first score indicating a relevance of an image represented by the first image search result to the text query. Second image search results responsive to a query image are received, each second image search result associated with a respective second score indicating a measure of similarity between an image represented by the second image search result and the query image. A set of final image search results is selected including combining first scores and second scores of the selected first image search results. The final image search results are ordered by similarity to the query image.
摘要翻译：方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于产生图像搜索结果。方法之一包括响应于文本查询接收第一图像搜索结果，每个第一图像搜索结果与相应的第一分数相关联，所述第一分数指示由第一图像搜索结果表示的图像与文本查询的相关性。接收响应于查询图像的第二图像搜索结果，每个第二图像搜索结果与相应的第二分数相关联，指示由第二图像搜索结果表示的图像与查询图像之间的相似度的度量。选择一组最终图像搜索结果，包括组合所选择的第一图像搜索结果的第一分数和第二分数。最终图像搜索结果与查询图像相似。

3. 发明申请

US20120136859A1 Entity Type Assignment 审中-公开
标题翻译：实体类型分配
公开(公告)号：US20120136859A1
公开(公告)日：2012-05-31
申请号：US13171296
申请日：2011-06-28
申请人： Farhan Shamsi , Alex P. Kehlenbeck , David J. Vespe , Nemanja Petrovic
发明人： Farhan Shamsi , Alex P. Kehlenbeck , David J. Vespe , Nemanja Petrovic
IPC分类号： G06F17/30
CPC分类号： G06F16/288 , G06N5/022 , G06N7/005 , G06N20/00
摘要： A computer system creates a plurality of objects using facts derived from electronic documents, each object including one or more facts describing an entity associated with the object. The system generates a value for an object of an unknown entity type, of the plurality of objects, by using an entity type model for a known entity type. The entity type model is based on a set of features of a plurality of objects of the known entity type, and the value indicates whether the object of an unknown entity type is of the known entity type. The system assigns the known entity type to the object of an unknown entity type in response to a determination that the value indicates the object of an unknown entity type is of the known entity type, and stores the object with the assigned entity type.
摘要翻译：计算机系统使用从电子文档导出的事实来创建多个对象，每个对象包括描述与对象相关联的实体的一个或多个事实。系统通过使用已知实体类型的实体类型模型来生成多个对象中未知实体类型的对象的值。实体类型模型基于已知实体类型的多个对象的一组特征，并且该值指示未知实体类型的对象是否是已知实体类型。响应于确定该值指示未知实体类型的对象是已知实体类型，系统将已知实体类型分配给未知实体类型的对象，并且存储具有分配实体类型的对象。

4. 发明授权

US07889898B2 System and method for semantic indexing and navigation of volumetric images 有权
标题翻译：体积图像语义索引和导航的系统和方法
公开(公告)号：US07889898B2
公开(公告)日：2011-02-15
申请号：US11532556
申请日：2006-09-18
申请人： Amit Chakraborty , Bogdan Georgescu , Nemanja Petrovic , Paolo Favaro , Dorin Comaniciu
发明人： Amit Chakraborty , Bogdan Georgescu , Nemanja Petrovic , Paolo Favaro , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06F17/30265 , G06F17/30014 , G06F19/00 , G06F19/321
摘要： A method for navigating digital medical images includes providing a digitized patient medical image of a structure of interest in a patient, using a pre-trained classifier to segment the structure of interest from the image, creating links from the structure of interest to a corresponding structure in an anatomical atlas, receiving a query to view the structure of interest, parsing the query to identify one or more keywords from noun phrases in the query, mapping a keyword to a corresponding structure in the anatomical atlas, wherein the anatomical atlas structure is associated with a link to the corresponding structure in the patient image, and following the link to display said patient structure of interest.
摘要翻译：用于导航数字医学图像的方法包括使用预训练的分类器从图像中分割感兴趣的结构，从感兴趣的结构到相应的结构建立链接来提供患者的感兴趣的结构的数字化的患者医学图像在解剖图谱中，接收查询以查看感兴趣的结构，解析查询以从查询中的名词短语识别一个或多个关键词，将关键字映射到解剖图谱中的对应结构，其中解剖图谱结构相关联具有到患者图像中的对应结构的链接，并且跟随链接以显示感兴趣的所述患者结构。

5. 发明授权

US07657102B2 System and method for fast on-line learning of transformed hidden Markov models 有权
标题翻译：用于快速在线学习变换隐马尔科夫模型的系统和方法
公开(公告)号：US07657102B2
公开(公告)日：2010-02-02
申请号：US10649382
申请日：2003-08-27
申请人： Nebojsa Jojic , Nemanja Petrovic
发明人： Nebojsa Jojic , Nemanja Petrovic
IPC分类号： G06K9/62 , G10L15/06
CPC分类号： G11B27/28 , G06K9/00711 , G06K9/6297
摘要： A fast variational on-line learning technique for training a transformed hidden Markov model. A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, once the model has been initialized, an expectation-maximization (“EM”) algorithm is used to learn the one or more object class models, so that the video sequence has high marginal probability under the model. In the expectation step (the “E-Step”), the model parameters are assumed to be correct, and for an input image, probabilistic inference is used to fill in the values of the unobserved or hidden variables, e.g., the object class and appearance. In one embodiment of the invention, a Viterbi algorithm and a latent image is employed for this purpose. In the maximization step (the “M-Step”), the model parameters are adjusted using the values of the unobserved variables calculated in the previous E-step.
摘要翻译：一种快速变化的在线学习技术，用于训练变换后的隐马尔可夫模型。提供了简化的一般模型和相关联的估计算法用于对诸如视频序列的视觉数据进行建模。具体来说，一旦模型被初始化，使用期望最大化（“EM”）算法来学习一个或多个对象类模型，使得视频序列在该模型下具有高边际概率。在期望步骤（“E步骤”）中，假设模型参数是正确的，对于输入图像，使用概率推断来填充未观察或隐藏变量的值，例如对象类和出现。在本发明的一个实施例中，为此目的采用维特比算法和潜像。在最大化步骤（“M步骤”）中，使用在先前E步骤中计算的未观察到的变量的值来调整模型参数。

6. 发明授权

US07970766B1 Entity type assignment 有权
标题翻译：实体类型分配
公开(公告)号：US07970766B1
公开(公告)日：2011-06-28
申请号：US11781891
申请日：2007-07-23
申请人： Farhan Shamsi , Alex Kehlenbeck , David Vespe , Nemanja Petrovic
发明人： Farhan Shamsi , Alex Kehlenbeck , David Vespe , Nemanja Petrovic
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06N99/005 , G06F17/30604
摘要： A repository contains objects including facts about entities. Objects may be of known or unknown entity type. An entity type assignment engine assigns entity types to objects of unknown entity type. A feature generation module generates a set of features describing the facts included with each object in the repository. An entity type model module generates an entity type model based on the sets of features generated for a subset of objects. An entity type model module generates entity type models, such as a classifier or generative models, based on the sets of features associated with objects of known entity type. An entity type assignment module generates a value based on the sets of features associated with an object of unknown entity type and the entity type model. This value indicates whether the object of unknown entity type is of a known entity type. An object update module stores the object to which the known entity type was assigned in the repository in association with the assigned entity type.
摘要翻译：存储库包含包含实体事实的对象。对象可能是已知或未知的实体类型。实体类型分配引擎将实体类型分配给未知实体类型的对象。特征生成模块生成描述包含在存储库中的每个对象的事实的一组特征。实体类型模型模块基于为对象子集生成的特征集合生成实体类型模型。基于与已知实体类型的对象相关联的特征集合，实体类型模型模块生成实体类型模型，例如分类器或生成模型。实体类型分配模块基于与未知实体类型的对象和实体类型模型相关联的特征集合生成值。该值指示未知实体类型的对象是否为已知实体类型。对象更新模块与所分配的实体类型相关联地存储在库中分配了已知实体类型的对象。

7. 发明授权

US07966291B1 Fact-based object merging 有权
标题翻译：基于事实的对象合并
公开(公告)号：US07966291B1
公开(公告)日：2011-06-21
申请号：US11768877
申请日：2007-06-26
申请人： Nemanja Petrovic , David Vespe , Alexander Kehlenbeck , Farhan Shamsi
发明人： Nemanja Petrovic , David Vespe , Alexander Kehlenbeck , Farhan Shamsi
IPC分类号： G06F17/00 , G06F7/00 , G09G5/00
CPC分类号： G06F17/30864
摘要： A repository contains objects including facts about entities. Some objects might be associated with the same entity. An object merge engine identifies a set of merge candidate objects. A grouping module groups the merge candidate objects based on the values of facts included in the objects. An object comparison module compares pairs of objects in each group to identify evidence for and/or against merging the pair. Evidence for merging the pair exists if, e.g., the objects have a type in common or share an uncommon fact. Evidence against merging the pair exists if, e.g., the objects have differing singleton attributes. A graph generation module generates graphs describing the evidence for and/or against merging the pair. A merging module analyzes the graphs and merges objects associated with the same entity. The merged objects are stored in the repository.
摘要翻译：存储库包含包含实体事实的对象。一些对象可能与同一实体相关联。对象合并引擎识别一组合并候选对象。分组模块根据包含在对象中的事实的值对合并候选对象进行分组。对象比较模块比较每组中的对象对以识别和/或不合并该对的证据。如果对象具有共同的类型或共享不常见的事实，则存在合并对的证据。如果对象具有不同的单例属性，则存在对合并对的证据。图形生成模块生成描述和/或反对合并该对的证据的图形。合并模块分析图形并合并与同一实体相关联的对象。合并的对象存储在存储库中。

8. 发明授权

US07152209B2 User interface for adaptive video fast forward 有权
标题翻译：自适应视频用户界面快进
公开(公告)号：US07152209B2
公开(公告)日：2006-12-19
申请号：US10401371
申请日：2003-03-28
申请人： Nebojsa Jojic , Nemanja Petrovic
发明人： Nebojsa Jojic , Nemanja Petrovic
IPC分类号： G11B27/00
CPC分类号： G06F17/30825 , G06F17/30852 , G11B27/005 , G11B27/105 , G11B27/28 , G11B27/34 , Y10S707/99933
摘要： A user interface (UI) for adaptive video fast forward provides a novel fully adaptive content-based UI for allowing user interaction with an image sequence or video relative to a user identified query sample. This query sample is drawn either from an image sequence being searched or from another image sequence entirely. The user interaction offered by the UI includes providing a user with computationally efficient searching, browsing and retrieval of one or more objects, frames or sequences of interest in video or image sequences, as well as automatic content-based variable-speed playback based on a computed similarity to the query sample. In addition, the UI also provides the capability to search for image frames or sequences that are dissimilar to the query sample, thereby allowing the user to quickly locate unusual or different activity within an image sequence.
摘要翻译：用于自适应视频快进的用户界面（UI）提供了一种新颖的完全自适应的基于内容的UI，用于允许用户与图像序列或视频相对于用户识别的查询样本进行交互。该查询样本是从正在搜索的图像序列或完全从另一个图像序列绘制的。 UI提供的用户交互包括向用户提供计算上有效的搜索，浏览和检索视频或图像序列中的一个或多个对象，感兴趣的帧或序列，以及基于基于内容的可变速度回放计算出与查询样本的相似度。此外，UI还提供搜索与查询样本不相似的图像帧或序列的能力，从而允许用户快速定位图像序列内的异常或不同的活动。

9. 发明授权

US07127127B2 System and method for adaptive video fast forward using scene generative models 有权
标题翻译：使用场景生成模型的自适应视频快进的系统和方法
公开(公告)号：US07127127B2
公开(公告)日：2006-10-24
申请号：US10378773
申请日：2003-03-04
申请人： Nebojsa Jojic , Nemanja Petrovic
发明人： Nebojsa Jojic , Nemanja Petrovic
IPC分类号： G06K9/60 , G06K9/00 , G06K9/68
CPC分类号： G11B27/28 , G06F17/30259 , G06F17/30802 , G06K9/00711 , G11B27/105
摘要： Computationally efficient searching, browsing and retrieval of one or more objects in a video sequence are accomplished using learned generative models. The generative model is trained on an automatically or manually selected query sequence from a sequence of image frames. The resulting generative model is then used in searching, browsing or retrieval of one or more similar or dissimilar image frames or sequences within the image sequence by determining the likelihood of each frame under the learned generative model. Further, this method allows for automatic separation and balancing of various causes of variability while analyzing the image sequence. The generative models are based on appearances of multiple, possibly occluding objects in an image sequence. Further, the search strategies used include clustering and intelligent fast forward through the image sequence. Additionally, in one embodiment, a fast forward speed is relative to the current frame likelihood under the learned generative model.
摘要翻译：使用学习的生成模型来实现视频序列中的一个或多个对象的计算有效的搜索，浏览和检索。生成模型通过一系列图像帧自动或手动选择的查询序列进行训练。然后，所得到的生成模型用于通过确定学习生成模型下的每个帧的可能性来搜索，浏览或检索图像序列内的一个或多个相似或不相似的图像帧或序列。此外，该方法允许在分析图像序列的同时自动分离和平衡各种变异原因。生成模型基于图像序列中多个可能闭塞的对象的出现。此外，使用的搜索策略包括通过图像序列的聚类和智能快进。另外，在一个实施例中，在学习的生成模型下，快进速度相对于当前帧可能性。

10. 发明申请

US20070127798A1 SYSTEM AND METHOD FOR SEMANTIC INDEXING AND NAVIGATION OF VOLUMETRIC IMAGES 有权
标题翻译：体积图像的语义索引和导航的系统和方法
公开(公告)号：US20070127798A1
公开(公告)日：2007-06-07
申请号：US11532556
申请日：2006-09-18
申请人： Amit Chakraborty , Bogdan Georgescu , Nemanja Petrovic , Paolo Favaro , Dorin Comaniciu
发明人： Amit Chakraborty , Bogdan Georgescu , Nemanja Petrovic , Paolo Favaro , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06F17/30265 , G06F17/30014 , G06F19/00 , G06F19/321
摘要： A method for navigating digital medical images includes providing a digitized patient medical image of a structure of interest in a patient, using a pre-trained classifier to segment the structure of interest from the image, creating links from the structure of interest to a corresponding structure in an anatomical atlas, receiving a query to view the structure of interest, parsing the query to identify one or more keywords from noun phrases in the query, mapping a keyword to a corresponding structure in the anatomical atlas, wherein the anatomical atlas structure is associated with a link to the corresponding structure in the patient image, and following the link to display said patient structure of interest.
摘要翻译：用于导航数字医学图像的方法包括使用预训练的分类器从图像中分割感兴趣的结构，从感兴趣的结构到相应的结构建立链接来提供患者的感兴趣的结构的数字化的患者医学图像在解剖图谱中，接收查询以查看感兴趣的结构，解析查询以从查询中的名词短语识别一个或多个关键词，将关键字映射到解剖图谱中的对应结构，其中解剖图谱结构相关联具有到患者图像中的对应结构的链接，并且跟随链接以显示感兴趣的所述患者结构。

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