专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明申请

US20180330191A1 LOGISTIC REGRESSION GRADIENT CALCULATION METHOD AND APPARATUS 审中-公开
公开(公告)号：US20180330191A1
公开(公告)日：2018-11-15
申请号：US16043043
申请日：2018-07-23
申请人： ALIBABA GROUP HOLDING LIMITED
发明人： Jun ZHOU
IPC分类号： G06K9/62 , G06F17/18 , G06Q30/02
CPC分类号： G06K9/6221 , G06F17/18 , G06K9/6288 , G06Q30/0242 , G06Q30/0277
摘要： The present disclosure provides logistic regression gradient calculation methods and apparatuses. One exemplary calculation method comprises: acquiring training data, the training data including X-row user data and Y-row click-through data corresponding to the X-row user data; converting the X-row user data into X-column data; segmenting the X-column data and a weight vector to form N X-column data segmentation blocks and N weight vector segmentation blocks; starting N threads respectively to generate N sub-logistic regression gradients according to the N X-column data segmentation blocks, the N weight vector segmentation blocks, and the corresponding Y-row click-through data; and splicing the N sub-logistic regression gradients to form a full logistic regression gradient. With embodiments of the present disclosure, a computing machine can support training of a super-large-scale logistic regression model, which increases the calculation speed, shortens the training time, and greatly reduces the memory usage of the computing machine.

2. 发明申请

US20180253626A1 CLUSTERING IMAGES BASED ON CAMERA FINGERPRINTS 审中-公开
公开(公告)号：US20180253626A1
公开(公告)日：2018-09-06
申请号：US15757279
申请日：2016-09-05
申请人： Functional Technologies Ltd.
发明人： Xufeng Lin , Chang-Tsun Li
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/6223 , G06K9/0051 , G06K9/00577 , G06K9/40 , G06K9/6218 , G06K9/6221 , G06K2009/00583
摘要： A method of analysing a set of digital images each having been captured with a digital camera, the method comprising, using at least one processor: a) extracting a camera fingerprint from each image so as to form a set of camera fingerprints, each camera fingerprint being representative of the camera used to capture the image, and being of a first dimension; b) forming a set of dimensionally reduced camera fingerprints from each camera fingerprint, the dimensionally reduced camera fingerprint being of a second dimension smaller than the first dimension; c) forming a first subset of dimensionally reduced camera fingerprints and a second subset of dimensionally reduced camera fingerprints; d) determining a level of similarity between every pairing of the dimensionally reduced camera fingerprints of the first subset; e) determining a level of similarity between every pairing of the dimensionally reduced camera fingerprints of the second subset; f) determining a level of similarity between every pairing of, on the one hand, the dimensionally reduced fingerprints of the first set and, on the other hand, the dimensionally reduced fingerprints of the second subset; g) recording those pairings which indicate a comparatively higher level of similarity; h) substituting for the contents of the first subset those dimensionally reduced camera fingerprints of the first and second subsets which have been recorded as part of a pairing showing a comparatively higher level of similarity; i) substituting for the contents of the second subset a different subset of the set of the dimensionally reduced camera fingerprints; j) repeating steps (e) to (i), typically until all of the dimensionally reduced camera fingerprints have been processed; k) performing a clustering algorithm on all dimensionally reduced camera fingerprints based on the pairings having a comparatively higher level of similarity to produce a plurality of first clusters each comprising a set of dimensionally reduced camera fingerprints; l) for each of the first clusters, determining a level of similarity between each of the camera fingerprints corresponding to the dimensionally reduced camera fingerprints of that cluster; and m) splitting and merging the coarse clusters dependent upon the similarities between the camera fingerprints to form a plurality of second clusters.

3. 发明申请

US20180189602A1 METHOD OF AND SYSTEM FOR DETERMINING AND SELECTING MEDIA REPRESENTING EVENT DIVERSITY 审中-公开
公开(公告)号：US20180189602A1
公开(公告)日：2018-07-05
申请号：US15315590
申请日：2015-06-01
申请人： THOMSON LICENSING
发明人： Pierre HELLIER , Fabrice URBAN , Patrick PEREZ
IPC分类号： G06K9/62 , G06K9/00 , G06T7/00 , G06K9/20 , G06F17/30
CPC分类号： G06K9/6221 , G06F16/51 , G06K9/00677 , G06K9/2081 , G06K9/622 , G06K2209/27 , G06T7/97
摘要： A method of reducing a large amount of media into a sub-group of high quality images in order to capture the diversity of an event. The present invention teaches a method of reducing a plurality of media into clusters in response to time and place. The clusters are further reduced in response to content in said media, including color and facial recognition to transmit still generate highlights. Near duplicate images are then removed from each highlight and then a high quality image is selected from each highlight. The high quality image is selected from each highlight and combined into an event overview to represent the diversity of an event.

4. 发明申请

US20180165542A1 TABLET DISHWASHING DETERGENT AND METHODS FOR MAKING AND USING THE SAME 审中-公开
公开(公告)号：US20180165542A1
公开(公告)日：2018-06-14
申请号：US15889641
申请日：2018-02-06
申请人： Prashant Kumar Pandey , Anant Kondiram Parte
发明人： Prashant Kumar Pandey , Anant Kondiram Parte
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/6221 , G06K9/00228 , G06K9/00288 , G06K9/00677
摘要： A tablet including a dishwashing detergent composition and a crosslinked acrylic acid polymer having a weight average molecular weight (Mw) of at least 500,000.

5. 发明授权

US09911043B2 Anomalous object interaction detection and reporting 有权
公开(公告)号：US09911043B2
公开(公告)日：2018-03-06
申请号：US13931058
申请日：2013-06-28
申请人： Omni Al, Inc.
发明人： Kishor Adinath Saitwal , Dennis G. Urech , Wesley Kenneth Cobb
IPC分类号： G06T13/00 , G06K9/00 , G06K9/62
CPC分类号： G06K9/00711 , G06K9/6221 , G06K9/6284
摘要： Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.

6. 发明申请

US20170124413A1 METHODS AND APPARATUS TO EXTRACT TEXT FROM IMAGED DOCUMENTS 有权
公开(公告)号：US20170124413A1
公开(公告)日：2017-05-04
申请号：US14927014
申请日：2015-10-29
申请人： The Nielsen Company (US), LLC
发明人： Kevin Keqiang Deng
IPC分类号： G06K9/34 , G06K9/64 , G06K9/03 , G06K9/62
CPC分类号： G06K9/344 , G06K9/033 , G06K9/6221 , G06K9/64 , G06K2209/01
摘要： Methods and apparatus to extract text from imaged documents are disclosed. Example methods include segmenting an image of a document into localized sub-images corresponding to individual characters in the document. The example methods further include grouping respective ones of the sub-images into a cluster based on a visual correlation of the respective ones of the sub-images to a reference sub-image. The visual correlation between the reference sub-image and the respective ones of the sub-images grouped into the cluster exceeding a correlation threshold. The example methods also include identifying a designated character for the cluster based on the sub-images grouped into the cluster. The example methods further include associating the designated character with locations in the image of the document associated with the respective ones of the sub-images grouped into the cluster.

7. 发明授权

US09311899B2 Detecting and describing visible features on a visualization 有权
公开(公告)号：US09311899B2
公开(公告)日：2016-04-12
申请号：US13650282
申请日：2012-10-12
申请人： International Business Machines Corporation
发明人： Eser Kandogan
IPC分类号： G09G5/377 , G06T11/20 , G06K9/62
CPC分类号： G06T11/206 , G06F17/241 , G06K9/6218 , G06K9/6221 , G06T11/60 , G09G5/377
摘要： Embodiments of the invention relate to detecting and describing visible features of a data set on a visualization. Visible features among a set of data in a view-space are detected. The visible features include potential data clusters and trends. These visual features are characterized using data-space. The characterized detected features are overlaid on visualization for supporting interaction and exploration of the data. Detected features are explored across two or more clusters for comparison of select data.

8. 发明申请

US20150324630A1 GROUPING FACE IMAGES USING STATISTIC DISTRIBUTION ESTIMATE 有权
标题翻译：使用统计分布估计对脸部图像进行分组
公开(公告)号：US20150324630A1
公开(公告)日：2015-11-12
申请号：US14272809
申请日：2014-05-08
申请人： Shutterfly, Inc.
发明人： Roman Sandler , Alexander M. Kenis
IPC分类号： G06K9/00 , G06K9/52 , G06K9/62
CPC分类号： G06K9/00228 , G06K9/00288 , G06K9/52 , G06K9/6215 , G06K9/6221 , G06K9/6267 , G06K9/66
摘要： A computer-implemented method for sorting face images of different individuals into different groups includes obtaining face images comprising faces of unknown individuals by a computer processor; calculating similarity functions between pairs of face images by the computer processor; joining face images that have values of the similarity functions above a predetermined threshold into a hypothetical face group, wherein the face images in the hypothetical face group hypothetically belong to a same person; conducting non-negative matrix factorization on values of the similarity functions in the hypothetical face group to test truthfulness of the hypothetical face group; and identifying the hypothetical face group as a true face group if a percentage of the associated similarity functions being true is above a threshold based on the non-negative matrix factorization.
摘要翻译：用于将不同个体的面部图像分类为不同组的计算机实现的方法包括：通过计算机处理器获取包括未知个体的面部的面部图像; 通过计算机处理器计算人脸图像之间的相似度函数; 将具有高于预定阈值的相似度函数的面部图像连接到假想面部组中，其中假设面部组中的面部图像假设属于同一个人; 对假想面部组中相似度函数的值进行非负矩阵分解，以测试假想面部群体的真实性; 并且如果所述相关联的相似度函数的百分比为真，则将所述假想面部群体识别为真面团，基于所述非负矩阵因子分解，高于阈值。

9. 发明申请

US20150234954A1 System, Method and Computer Program Product For Wellbore Event Modeling Using Rimlier Data 审中-公开
标题翻译：系统，方法和计算机程序产品对于使用边界数据的井眼事件建模
公开(公告)号：US20150234954A1
公开(公告)日：2015-08-20
申请号：US14435404
申请日：2012-11-05
申请人： Robello SAMUEL , Olivier Roger GERMAIN , LANDMARK GRAPHICS CORPORATION
发明人： Robello Samuel , Olivier Roger Germain
IPC分类号： G06F17/50 , G06F17/18
CPC分类号： G06F17/5009 , E21B44/00 , G06F17/18 , G06K9/6221 , G06K9/6284
摘要： A data mining and analysis system which analyzes clusters of outlier data (i.e., rimliers) to detect and/or predict downhole events.
摘要翻译：数据挖掘和分析系统，其分析离群数据群集（即边界）以检测和/或预测井下事件。

10. 发明申请

US20150193656A1 PERFORMING HAND GESTURE RECOGNITION USING 2D IMAGE DATA 有权
标题翻译：使用2D图像数据执行手势识别
公开(公告)号：US20150193656A1
公开(公告)日：2015-07-09
申请号：US14125154
申请日：2013-06-10
申请人： INTEL CORPORATION
发明人： Michael Kounavis , Rajendra Yavatkar , Ioannis Schoinas , Carlos Abad Vazquez
IPC分类号： G06K9/00 , G06K9/46 , G06K9/62 , G06F3/01
CPC分类号： G06K9/00355 , G06F3/017 , G06F3/0304 , G06K9/00389 , G06K9/00993 , G06K9/3241 , G06K9/342 , G06K9/44 , G06K9/4619 , G06K9/4638 , G06K9/4652 , G06K9/6221 , G06K9/6223 , G06K2209/21 , G06T7/246
摘要： Systems and methods may provide for determining a skin tone distribution for a plurality of pixels in a video signal and using the skin tone distribution to conduct one or more blob-based hand gesture determinations with respect to the video signal. In one example, the video signal includes two-dimensional (2D) image data, and the skin tone distribution has an execution time budget that is greater than an execution time budget of the blob-based hand gesture determinations.
摘要翻译：系统和方法可以提供用于确定视频信号中的多个像素的肤色分布，并且使用肤色分布来针对视频信号进行一个或多个基于blob的手势确定。在一个示例中，视频信号包括二维（2D）图像数据，并且肤色分布具有大于基于blob的手势确定的执行时间预算的执行时间预算。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式