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91. 发明申请

US20130329049A1 MULTISENSOR EVIDENCE INTEGRATION AND OPTIMIZATION IN OBJECT INSPECTION 有权
标题翻译：多媒体证据集成和对象检查优化
公开(公告)号：US20130329049A1
公开(公告)日：2013-12-12
申请号：US13489489
申请日：2012-06-06
申请人： Norman Haas , Ying Li , Charles A. Otto , Sharathchandra U. Pankanti , Hoang Trinh
发明人： Norman Haas , Ying Li , Charles A. Otto , Sharathchandra U. Pankanti , Hoang Trinh
IPC分类号： H04N7/18
CPC分类号： B61L23/042
摘要： Video image data is acquired from synchronized cameras having overlapping views of objects moving past the cameras through a scene image in a linear array and with a determined speed. Processing units generate one or more object detections associated with confidence scores within frames of the camera video stream data. The confidence scores are modified as a function of constraint contexts including a cross-frame constraint that is defined by other confidence scores of other object detection decisions from the video data that are acquired by the same camera at different times; a cross-view constraint defined by other confidence scores of other object detections in the video data from another camera with an overlapping field-of-view; and a cross-object constraint defined by a sequential context of a linear array of the objects, spatial attributes of the objects and the determined speed of the movement of the objects relative to the cameras.
摘要翻译：视频图像数据从同步摄像机获取，该相机具有通过线性阵列中的场景图像以确定的速度移动通过相机的对象的重叠视图。处理单元产生与相机视频流数据的帧内的置信度分数相关联的一个或多个对象检测。可信度分数被修改为约束上下文的函数，包括由不同时间由同一相机获取的视频数据的其他对象检测决定的其他置信度分数定义的跨帧约束; 由具有重叠视场的另一相机的视频数据中的其他对象检测的其他置信度得分定义的横视约束; 以及由对象的线性阵列，对象的空间属性和所确定的对象相对于照相机的移动速度的顺序上下文定义的跨对象约束。

92. 发明授权

US08457355B2 Incorporating video meta-data in 3D models 有权
标题翻译：将视频元数据纳入3D模型
公开(公告)号：US08457355B2
公开(公告)日：2013-06-04
申请号：US13101401
申请日：2011-05-05
申请人： Lisa M. Brown , Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti
发明人： Lisa M. Brown , Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti
IPC分类号： G06K9/00 , H04N5/225
CPC分类号： G06T7/20 , G06K9/00208 , G06T7/251 , G06T13/20 , G06T17/00 , G06T19/006
摘要： A moving object detected and tracked within a field of view environment of a 2D data feed of a calibrated video camera is represented by a 3D model through localizing a centroid of the object and determining an intersection with a ground-plane within the field of view environment. An appropriate 3D mesh-based volumetric model for the object is initialized by using a back-projection of a corresponding 2D image as a function of the centroid and the determined ground-plane intersection. Nonlinear dynamics of a tracked motion path of the object are represented as a collection of different local linear models. A texture of the object is projected onto the 3D model, and 2D tracks of the object are upgraded to 3D motion to drive the 3D model by learning a weighted combination of the different local linear models that minimizes an image re-projection error of model movement.
摘要翻译：在校准摄像机的2D数据馈送的视野环境内检测和跟踪的移动物体由3D模型表示，其通过定位对象的质心并确定视场环境内的接地平面的交点。通过使用对应的2D图像的反投影作为质心和确定的地面交点的函数来初始化用于对象的适当的基于3D网格的体积模型。对象的跟踪运动路径的非线性动力学被表示为不同局部线性模型的集合。将对象的纹理投影到3D模型上，并且将对象的2D轨迹升级到3D运动，以通过学习不同局部线性模型的加权组合来驱动3D模型，从而最小化模型运动的图像重新投影误差。

93. 发明申请

US20120324235A1 SYSTEM AND METHOD FOR REMOTE SELF-ENROLLMENT IN BIOMETRIC DATABASES 审中-公开
标题翻译：用于生物量数据库中远程自我投入的系统和方法
公开(公告)号：US20120324235A1
公开(公告)日：2012-12-20
申请号：US13598063
申请日：2012-08-29
申请人： Rudolf M. Bolle , Sharathchandra U. Pankanti , Nalini K. Ratha , Andrew W. Senior
发明人： Rudolf M. Bolle , Sharathchandra U. Pankanti , Nalini K. Ratha , Andrew W. Senior
IPC分类号： G06F21/00
CPC分类号： G06F21/32 , G06K9/00 , G06K9/00885
摘要： Methods and systems for remotely enrolling enrollees into biometric databases are provided. The method includes acquiring biometric data from one or more biometric sensors and authenticating an enrollee associated with the biometric data. The method includes enrolling the authenticated enrollee associated with the biometric data. The acquiring occurs externally from equipment that requires an identification. The method includes verifying individual samplings of the biometric data for quality at the time of enrollment based on a pre-determined threshold and verifying whether the enrollee presenting the biometric data is authenticated at the time of enrollment. The method includes signing a request of a third party with a private key associated with the third party, the signing denoting that the biometric data is verified for a transaction between the third party and the enrollee. The method includes sending the signed third party request to the third party to complete authenticating of the transaction.
摘要翻译：提供了将参加者远程登记到生物特征数据库中的方法和系统。该方法包括从一个或多个生物测定传感器获取生物特征数据并认证与生物特征数据相关联的参与者。该方法包括登记与生物特征数据相关联的认证登记者。该采集从需要识别的设备外部进行。该方法包括基于预先确定的阈值来验证登记时的生物体数据的质量的各个采样，并且验证在登记时是否认证呈现生物特征数据的登记者。该方法包括使用与第三方相关联的私钥对第三方的请求进行签名，该签名表示生物特征数据被验证用于第三方和登记者之间的交易。该方法包括向第三方发送签名的第三方请求以完成交易的认证。

94. 发明申请

US20120308121A1 IMAGE RANKING BASED ON ATTRIBUTE CORRELATION 有权
标题翻译：基于属性关联的图像排序
公开(公告)号：US20120308121A1
公开(公告)日：2012-12-06
申请号：US13152615
申请日：2011-06-03
申请人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Behjat Siddiquie
发明人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Behjat Siddiquie
IPC分类号： G06K9/62
CPC分类号： G06F17/30256 , G06F17/30277 , G06F17/3028 , G06F17/3053 , G06K9/00268 , G06K9/481 , G06K9/6263 , G06K9/66 , G06N99/005
摘要： Images are retrieved and ranked according to relevance to attributes of a multi-attribute query through training image attribute detectors for different attributes annotated in a training dataset. Pair-wise correlations are learned between pairs of the annotated attributes from the training dataset of images. Image datasets may then be searched via the trained attribute detectors for images comprising attributes in a multi-attribute query, wherein images are retrieved from the searching that each comprise one or more of the query attributes and also in response to information from the trained attribute detectors corresponding to attributes that are not a part of the query but are relevant to the query attributes as a function of the learned plurality of pair-wise correlations. The retrieved images are ranked as a function of respective total numbers of attributes within the query subset attributes.
摘要翻译：根据与训练数据集中注释的不同属性的训练图像属性检测器，根据与多属性查询的属性的相关性来检索和排列图像。在图像训练数据集的注释属性对之间学习成对相关。然后可以经由经训练的属性检测器搜索包括多属性查询中的属性的图像的图像数据集，其中从搜索中检索每个包括一个或多个查询属性的图像，并且还响应于来自经训练的属性检测器的信息对应于不是查询的一部分但与所学习的多个成对相关性的函数的查询属性相关的属性。检索到的图像根据查询子集属性内的各个属性总数的顺序排列。

95. 发明申请

US20120284211A1 IDENTIFYING ABNORMALITIES IN RESOURCE USAGE 失效
标题翻译：识别资源使用异常
公开(公告)号：US20120284211A1
公开(公告)日：2012-11-08
申请号：US13100868
申请日：2011-05-04
申请人： Ankur Datta , Charles A. Otto , Sharathchandra U. Pankanti
发明人： Ankur Datta , Charles A. Otto , Sharathchandra U. Pankanti
IPC分类号： G06F15/18 , G06F17/00 , G06N5/04
CPC分类号： G06F11/0751 , G06F11/3058 , G06F11/3082
摘要： A method, data processing system, and computer program product for identifying abnormalities in data. A model representing a plurality of modes for an activity generated from training data is retrieved. The training data includes a first plurality of measurements of a first performance of the activity over a period of time. Each of the plurality of modes is identified as one of normal and abnormal. Activity data including a second plurality of measurements of a second performance of the activity is received. A portion of the activity data is compared with the plurality of modes in the model. A notification of an abnormality in the second performance of the activity is generated in response to an identification that the portion of the activity data matches a mode in the plurality of modes identified as abnormal. Confirmation of the abnormality is requested via a user interface.
摘要翻译：一种用于识别数据异常的方法，数据处理系统和计算机程序产品。检索表示从训练数据生成的活动的多个模式的模型。训练数据包括在一段时间内第一次执行活动的测量。多个模式中的每一个被标识为正常和异常之一。接收包括活动的第二次执行的第二多个测量的活动数据。将活动数据的一部分与模型中的多个模式进行比较。响应于识别出活动数据的一部分与被识别为异常的多个模式中的模式相匹配的标识来生成第二次活动的异常的通知。通过用户界面要求确认异常。

96. 发明申请

US20120233159A1 HIERARCHICAL RANKING OF FACIAL ATTRIBUTES 失效
标题翻译：物理属性的分级排序
公开(公告)号：US20120233159A1
公开(公告)日：2012-09-13
申请号：US13045092
申请日：2011-03-10
申请人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Daniel A. Vaquero
发明人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Daniel A. Vaquero
IPC分类号： G06F17/30
CPC分类号： G06F17/30277 , G06F17/16 , G06F17/30256 , G06F17/30265 , G06F17/30271 , G06F17/30528 , G06F17/3053 , G06F17/3069 , G06K9/00268 , G06K9/6228 , Y10S707/915
摘要： In response to a query of discernible facial attributes, the locations of distinct and different facial regions are estimated from face image data, each relevant to different attributes. Different features are extracted from the estimated facial regions from database facial images, which are ranked in base layer rankings by matching feature vectors in a bipartite graph to a base layer ranking sequence as a function of edge weights parameterized by an associated base layer parameter vector. Second-layer rankings define second-layer attribute vectors as bilinear combinations of the base-layer feature vectors and associated base layer parameter vectors for common attributes, which are matched in a bipartite graph to a second-layer ranking sequence as a function of edge weights parameterized by associated second-layer parameter vectors. The images are thus ranked for relevance to the query as a function of fusing the second-layer rankings.
摘要翻译：响应于可辨别的面部属性的查询，根据与不同属性相关的面部图像数据来估计不同和不同面部区域的位置。从数据库面部图像从估计的面部区域提取不同的特征，其通过将二分图中的特征向量与基本层排序序列相匹配来排列在基本层排名中，作为由相关联的基本层参数向量参数化的边缘权重的函数。第二层次排列将第二层属性向量定义为用于共同属性的基层特征向量和相关联的基层参数向量的双线性组合，其在二分图中与作为边权的函数的第二层排序序列匹配由相关的第二层参数向量参数化。因此，这些图像作为将第二层排名融合的功能被排列为与查询相关。

97. 发明申请

US20090212102A1 SECURE SELF-CHECKOUT 有权
标题翻译：安全自检
公开(公告)号：US20090212102A1
公开(公告)日：2009-08-27
申请号：US12037266
申请日：2008-02-26
申请人： Jonathan H. Connell, II , Norman Haas , Sharathchandra U. Pankanti
发明人： Jonathan H. Connell, II , Norman Haas , Sharathchandra U. Pankanti
IPC分类号： G06F17/00
CPC分类号： A47F9/047 , G06K9/6215 , G06Q30/00 , G07G1/0063 , G07G3/003
摘要： Under the present invention, item verification is automated and expedited. Specifically, items to be purchased can be scanned by the shopper using a barcode reader (e.g., a scanner) attached to or positioned near the shopping receptacle. As items are scanned, they are identified based on their barcode and added to an item list. Item verification can then performed at checkout using imaging technology. For example, the shopping cart or shopping basket can be brought into the field of view of a computer-connected camera. The camera and computer can, working from the customer's item list developed when the items are scanned, observe each product in the receptacle and “ring it up”. If all products can be accounted for, the customer is free to leave; otherwise the customer is denied egress, informed of the problem, etc. A store employee can also be signaled to investigate. The total time required to make the decision is the time to take a picture and process it, which by human standards is very fast; faster than existing verification methods.
摘要翻译：在本发明中，项目验证是自动化和加速的。特别地，购物者可以使用附接到购物容器附近或位于购物容器附近的条形码读取器（例如，扫描器）来扫描要购买的物品。当项目被扫描时，它们根据其条形码被识别并被添加到项目列表中。然后可以使用成像技术在结帐时执行项目验证。例如，购物车或购物篮可以被带入计算机连接的相机的视野中。照相机和计算机可以在扫描物品时开发的客户项目列表中进行操作，观察插座中的每个产品并“振铃”。如果所有产品都可以核算，客户可以自由离开; 否则客户被拒绝出境，通知问题等。店员也可以被告知调查。作出决定所需的总时间是拍摄照片和处理时间，人体标准非常快; 比现有的验证方法快。

98. 发明授权

US06266433B1 System and method for determining ridge counts in fingerprint image processing 失效
标题翻译：用于确定指纹图像处理中的脊数的系统和方法
公开(公告)号：US06266433B1
公开(公告)日：2001-07-24
申请号：US09302109
申请日：1999-04-29
申请人： Rudolf Maarten Bolle , Scott Eric Colville , Sharathchandra U. Pankanti
发明人： Rudolf Maarten Bolle , Scott Eric Colville , Sharathchandra U. Pankanti
IPC分类号： G06K900
CPC分类号： G06K9/00067
摘要： A computer based image processing system uses an extraction process to include a pressure invariant feature for measuring distances between minutiae. The feature extraction process identifies one or more of the following features of the fingerprint: an orthogonal image contrast, a parallel image contrast, and a feature confidence. A ridge counter process, executing on the computer system, determines the number of ridges (ridge count) running across two given points and further qualifies (invalidates) this count if the confidence value of the pixels in the region adjoining the region is not reliable. The ridge count feature between minutiae is used for determining reliable features when matching fingerprints.
摘要翻译：基于计算机的图像处理系统使用提取过程来包括用于测量细节之间的距离的压力不变特征。特征提取过程识别指纹的以下特征中的一个或多个：正交图像对比度，平行图像对比度和特征置信度。在计算机系统上执行的脊计数器处理确定在两个给定点上运行的脊数（脊数），并且如果与该区域相邻的区域中的像素的置信度值不可靠，则进一步限定（无效）该计数。细节之间的脊数特征用于在匹配指纹时确定可靠特征。

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