公开(公告)号：US07599894B2

公开(公告)日：2009-10-06

申请号：US11367755

申请日：2006-03-04

申请人： Yuri Owechko ,  Swarup Medasani

发明人： Yuri Owechko ,  Swarup Medasani

IPC分类号： G06F15/18 ,  G06K9/00 ,  G06K9/62

CPC分类号： G06K9/6229 ,  G06K9/00369 ,  G06K9/3233

摘要： An object recognition system is described that incorporates swarming classifiers with attention mechanisms. The object recognition system includes a cognitive map having a one-to-one relationship with an input image domain. The cognitive map records information that software agents utilize to focus a cooperative swarm's attention on regions likely to contain objects of interest. Multiple agents operate as a cooperative swarm to classify an object in the domain. Each agent is a classifier and is assigned a velocity vector to explore a solution space for object solutions. Each agent records its coordinates in multi-dimensional space that are an observed best solution that the agent has identified, and a global best solution that is used to store the best location among all agents. Each velocity vector thereafter changes to allow the swarm to concentrate on the vicinity of the object and classify the object when a classification level exceeds a preset threshold.

摘要翻译： 描述了包含具有注意机制的群集分类器的对象识别系统。 对象识别系统包括与输入图像域具有一对一关系的认知图。 认知地图记录软件代理人利用的信息，将合作群体的注意力集中在可能包含感兴趣对象的地区。 多个代理作为协作群来运行以对域中的对象进行分类。 每个代理是一个分类器，并分配一个速度向量来探索对象解决方案的解空间。 每个代理将其坐标记录在多维空间中，这是代理已经识别的最佳解决方案，也是用于在所有代理中存储最佳位置的全局最佳解决方案。 此后，每个速度矢量改变以允许群集集中在对象附近，并且当分类级别超过预设阈值时对对象进行分类。

公开(公告)号：US07561732B1

公开(公告)日：2009-07-14

申请号：US11051592

申请日：2005-02-04

申请人： Yuri Owechko ,  Narayan Srinivasa ,  Swarup Medasani ,  Riccardo Boscolo

发明人： Yuri Owechko ,  Narayan Srinivasa ,  Swarup Medasani ,  Riccardo Boscolo

IPC分类号： G06K9/00

CPC分类号： G06T7/593 ,  G06T2207/10012 ,  G06T2207/20076

摘要： A method, an apparatus, and a computer program product for three-dimensional shape estimation using constrained disparity propagation are presented. An act of receiving a stereoscopic pair of images of an area occupied by at least one object is performed. Next, pattern regions and non-pattern regions are detected in the images. An initial estimate of śpatial disparities between the pattern regions in the images is generated. The initial estimate is used to generate a subsequent estimate of the spatial disparities between the non-pattern regions. The subsequent estimate is used to generate further subsequent estimates of the spatial disparities using the disparity constraints until there is no change between the results of subsequent iterations, generating a final estimate of the spatial disparities. A disparity map of the area occupied by at least one object is generated from the final estimate of the three-dimensional shape.

摘要翻译： 提出了一种使用受限视差传播进行三维形状估计的方法，装置和计算机程序产品。 执行接收由至少一个对象占据的区域的立体图像对的动作。 接下来，在图像中检测图案区域和非图案区域。 生成图像中的图案区域之间的空间差异的初始估计。 初始估计用于产生非图案区域之间的空间差异的随后估计。 随后的估计用于使用差异约束来生成空间差异的进一步后续估计，直到后续迭代的结果之间没有变化，产生空间差异的最终估计。 从三维形状的最终估计中生成由至少一个对象占据的区域的视差图。

公开(公告)号：US20070183669A1

公开(公告)日：2007-08-09

申请号：US11385983

申请日：2006-03-20

申请人： Yuri Owechko ,  Swarup Medasani ,  Payam Saisan

发明人： Yuri Owechko ,  Swarup Medasani ,  Payam Saisan

IPC分类号： G06K9/62 ,  G06K9/46

CPC分类号： G06K9/6229 ,  G06K9/00369 ,  G06K9/6292

摘要： An object recognition system is described that incorporates swarming classifiers. The swarming classifiers comprise a plurality of software agents configured to operate as a cooperative swarm to classify an object in a domain as seen from multiple view points. Each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions. Each agent is configured to perform an iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents. Each velocity vector changes towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object.

摘要翻译： 描述了包含群组分类器的对象识别系统。 群集分类器包括被配置为作为协作群进行操作的多个软件代理，用于对从多个视点看到的域中的对象进行分类。 每个代理是一个完整的分类器，并分配一个初始速度向量来探索对象解决方案的解空间。 每个代理被配置为执行迭代，迭代是针对潜在解决方案空间的解决方案空间中的搜索，其中每个代理跟踪其在与所观察到的最佳解（pbest）相关联的多维空间中的坐标，代理 已经确定了全球最佳解决方案（gbest），其中gbest用于存储所有代理商中的最佳位置。 每个速度向量向pbest和gbest变化，允许合作群集集中在对象附近并对对象进行分类。

公开(公告)号：US09361523B1

公开(公告)日：2016-06-07

申请号：US12841078

申请日：2010-07-21

申请人： Yang Chen ,  Swarup Medasani ,  Qin Jiang ,  David L. Allen ,  Tsai-Ching Lu

发明人： Yang Chen ,  Swarup Medasani ,  Qin Jiang ,  David L. Allen ,  Tsai-Ching Lu

IPC分类号： G06K9/03 ,  G06K9/00

CPC分类号： G06K9/00758 ,  G06K9/00711 ,  G06K9/00718 ,  G06K9/00744 ,  G06K9/4676 ,  G06T2207/10016 ,  G06T2210/00

摘要： A method and system for video-content based retrieval is described. A query video depicting an activity is processed using interest point selection to find locations in the video that are relevant to that activity. A set of spatio-temporal descriptors such as self-similarity and 3-D SIFT are calculated within a local neighborhood of the set of interest points. An indexed video database containing videos similar to the query video is searched using the set of descriptors to obtain a set of candidate videos. The videos in the video database are indexed hierarchically using a vocabulary tree or other hierarchical indexing mechanism.

摘要翻译： 描述了基于视频内容的检索的方法和系统。 使用兴趣点选择来处理描绘活动的查询视频，以查找视频中与该活动相关的位置。 在一组感兴趣点的本地邻域内计算一组空间 - 时间描述符，例如自相似性和3-D SIFT。 使用该组描述符搜索包含与查询视频类似的视频的索引视频数据库，以获得一组候选视频。 视频数据库中的视频使用词汇树或其他分层索引机制分层索引。

公开(公告)号：US08948501B1

公开(公告)日：2015-02-03

申请号：US13535286

申请日：2012-06-27

申请人： Kyungnam Kim ,  Shankar R. Rao ,  Yuri Owechko ,  Swarup Medasani ,  Michael Cao ,  Jiejun Xu

发明人： Kyungnam Kim ,  Shankar R. Rao ,  Yuri Owechko ,  Swarup Medasani ,  Michael Cao ,  Jiejun Xu

IPC分类号： G06K9/62

CPC分类号： G01S7/4808 ,  G01S17/89 ,  G06K9/00201 ,  G06K9/00335 ,  G06K9/00711 ,  G06T7/11 ,  G06T7/136 ,  G06T7/187 ,  G06T2207/10044 ,  G06T2207/20021

摘要： The present invention relates to an object detection and behavior recognition system using three-dimensional motion data. The system receives three-dimensional (3D) motion data of a scene from at least one sensor, such as a LIDAR sensor. An object is identified in the 3D motion data. Thereafter, an object track is extracted, the object track being indicative of object motion in the scene over time. Through Dynamic Time Warping (DTW) or other comparison techniques, the object track is compared to a database to identify the behavior of the object based on its object track.

摘要翻译： 本发明涉及使用三维运动数据的物体检测和行为识别系统。 该系统从诸如LIDAR传感器的至少一个传感器接收场景的三维（3D）运动数据。 在3D运动数据中识别对象。 此后，提取对象轨道，物体轨迹指示场景中的物体运动随着时间的推移。 通过动态时间扭曲（DTW）或其他比较技术，将对象轨迹与数据库进行比较，以基于其对象轨迹来识别对象的行为。

公开(公告)号：US08798372B1

公开(公告)日：2014-08-05

申请号：US13414391

申请日：2012-03-07

申请人： Dmitriy Korchev ,  Swarup Medasani ,  Yuri Owechko

发明人： Dmitriy Korchev ,  Swarup Medasani ,  Yuri Owechko

IPC分类号： G06K9/46

CPC分类号： G06K9/00637 ,  G06K9/00651 ,  G06K9/6211

摘要： Described is a system and method for detecting elevated structures, such as bridges and overpasses, in point cloud data. A set of data from a three-dimensional point cloud of a landscape is received by the system. The set of data points comprises inlier data points and outlier data points. The inlier data points in the three-dimensional point cloud data are identified and combined into at least one segment. The segment is converted into an image comprising at least one image level. Each image level is processed with an edge detection algorithm to detect elevated edges. The elevated edges are vectorized to identify an elevated structure of interest in the landscape. The present invention is useful in applications that require three-dimensional sensing systems, such as autonomous navigation and surveillance applications.

摘要翻译： 描述了用于在点云数据中检测诸如桥梁和立交桥的升高结构的系统和方法。 由系统接收来自景观的三维点云的一组数据。 数据点集合包括异常数据点和异常值数据点。 三维点云数据中的上位数据点被识别并组合成至少一个段。 该片段被转换成包括至少一个图像级别的图像。 每个图像级别都使用边缘检测算法进行处理，以检测提升的边缘。 提升的边缘被矢量化以识别景观中兴趣兴高的结构。 本发明在需要诸如自主导航和监视应用的三维感测系统的应用中是有用的。

公开(公告)号：US08744200B1

公开(公告)日：2014-06-03

申请号：US13464887

申请日：2012-05-04

申请人： Yuri Owechko ,  Swarup Medasani

发明人： Yuri Owechko ,  Swarup Medasani

IPC分类号： G06K9/54 ,  G06K9/60

CPC分类号： H04N5/23245 ,  G06K9/00496 ,  H04N5/23229 ,  H04N19/136 ,  H04N19/194

摘要： Described is a knowledge-enhanced compressive imaging system. The system first initializes a compressive measurement basis set and a measurement matrix using task- and scene-specific prior knowledge. An image captured using the imaging mode of the dual-mode sensor is then sampled to extract context knowledge. The compressive measurement basis set and the measurement matrix are adapted using the extracted context knowledge and the prior knowledge. Task-relevant compressive measurements of the image are performed using the compressive measurement mode of the dual-mode sensor, and compressive reconstruction of the image is performed. Finally, a task and context optimized signal representation of the image is generated.

摘要翻译： 描述了一种知识增强的压缩成像系统。 系统首先使用任务和场景特定的先验知识初始化压缩测量基础集和测量矩阵。 然后对使用双模传感器的成像模式捕获的图像进行采样以提取上下文知识。 使用提取的上下文知识和现有知识来调整压缩测量基础集和测量矩阵。 使用双模传感器的压缩测量模式执行图像的任务相关的压缩测量，并且执行图像的压缩重建。 最后，生成图像的任务和上下文优化的信号表示。

公开(公告)号：US08589315B2

公开(公告)日：2013-11-19

申请号：US11800264

申请日：2007-05-03

申请人： Swarup Medasani ,  Yuri Owechko

发明人： Swarup Medasani ,  Yuri Owechko

IPC分类号： G06F15/18 ,  G06F15/00

CPC分类号： G06K9/6292 ,  G06K9/00369 ,  G06K9/6229

摘要： Described is a behavior recognition system for detecting the behavior of objects in a scene. The system comprises a semantic object stream module for receiving a video stream having at least two frames and detecting objects in the video stream. Also included is a group organization module for utilizing the detected objects from the video stream to detect a behavior of the detected objects. The group organization module further comprises an object group stream module for spatially organizing the detected objects to have relative spatial relationships. The group organization module also comprises a group action stream module for modeling a temporal structure of the detected objects. The temporal structure is an action of the detected objects between the two frames, whereby through detecting, organizing and modeling actions of objects, a user can detect the behavior of the objects.

摘要翻译： 描述了用于检测场景中的对象的行为的行为识别系统。 该系统包括语义对象流模块，用于接收具有至少两个帧的视频流并检测视频流中的对象。 还包括用于利用来自视频流的检测对象的组织组织模块来检测检测到的对象的行为。 组织模块还包括用于空间组织所检测到的对象以具有相对空间关系的对象组流模块。 组织模块还包括用于对所检测到的对象的时间结构建模的组动作流模块。 时间结构是两帧之间检测到的对象的动作，由此通过检测，组织和建模对象的动作，用户可以检测对象的行为。

公开(公告)号：US08488877B1

公开(公告)日：2013-07-16

申请号：US12592836

申请日：2009-12-02

申请人： Yuri Owechko ,  Swarup Medasani ,  Ronald T. Azuma ,  Jim Nelson

发明人： Yuri Owechko ,  Swarup Medasani ,  Ronald T. Azuma ,  Jim Nelson

IPC分类号： G06K9/00

CPC分类号： G06K9/00201 ,  G06K9/00704

摘要： Described is a system for object recognition in colorized point clouds. The system includes an implicit geometry engine that is configured to receive three-dimensional (3D) colorized cloud point data regarding a 3D object of interest and to convert the cloud point data into implicit representations. The engine also generates geometric features. A geometric grammar block is included to generate object cues and recognize geometric objects using geometric tokens and grammars based on object taxonomy. A visual attention cueing block is included to generate object cues based on 3D geometric properties. Finally, an object recognition block is included to perform a local search for objects using cues from the cueing block and the geometric grammar block and to classify the 3D object of interest as a particular object upon a classifier reaching a predetermined threshold.

摘要翻译： 描述了在彩色点云中的对象识别系统。 该系统包括隐式几何引擎，其被配置为接收关于感兴趣的3D对象的三维（3D）着色浊点数据，并将该浊点数据转换为隐含表示。 引擎还生成几何特征。 包含几何语法块以生成对象提示，并使用基于对象分类法的几何令牌和语法来识别几何对象。 包括视觉注意提示块，以根据3D几何属性生成对象提示。 最后，包括对象识别块，以使用来自提示块和几何语法块的提示来执行对象的本地搜索，并且在分类器达到预定阈值时将感兴趣的3D对象分类为特定对象。

公开(公告)号：US08213709B1

公开(公告)日：2012-07-03

申请号：US12590110

申请日：2009-11-03

申请人： Swarup Medasani ,  Yuri Owechko ,  Tsai-Ching Lu ,  Deepak Khosla ,  David L. Allen

发明人： Swarup Medasani ,  Yuri Owechko ,  Tsai-Ching Lu ,  Deepak Khosla ,  David L. Allen

IPC分类号： G06K9/62

CPC分类号： G06K9/6278 ,  G06K9/00671 ,  G06K9/6263 ,  G06N7/005

摘要： A method and system for a directed area search using cognitive swarm vision and cognitive Bayesian reasoning is disclosed. The system comprises a domain knowledge database, a top-down reasoning module, and a bottom-up module. The domain knowledge database is configured to store Bayesian network models comprising visual features and observables associated with various sets of entities. The top-down module is configured to receive a search goal, generate a plan of action using Bayesian network models, and partition the plan into a set of tasks/observables to be located in the imagery. The bottom-up module is configured to select relevant feature/attention models for the observables, and search the visual imagery using a cognitive swarm for the at least one observable. The system further provides for operator feedback and updating of the domain knowledge database to perform better future searches.

摘要翻译： 公开了一种使用认知群体视觉和认知贝叶斯推理的定向区域搜索的方法和系统。 该系统包括域知识数据库，自上而下推理模块和自下而上模块。 域知识数据库被配置为存储包括与各组实体相关联的视觉特征和可观察性的贝叶斯网络模型。 自顶向下模块被配置为接收搜索目标，使用贝叶斯网络模型生成行动计划，并将计划分成一组要在图像中的任务/可观察值。 自下而上模块被配置为选择可观察的相关特征/关注模型，并且使用用于至少一个可观察的认知群搜索视觉图像。 该系统进一步提供操作者反馈和更新领域知识数据库以执行更好的未来搜索。