公开(公告)号：US09449259B1

公开(公告)日：2016-09-20

申请号：US13558298

申请日：2012-07-25

申请人： Shinko Y. Cheng ,  Yuri Owechko ,  Swarup Medasani

发明人： Shinko Y. Cheng ,  Yuri Owechko ,  Swarup Medasani

IPC分类号： G06K9/62 ,  G06K9/46 ,  G06K9/66 ,  G06K9/68 ,  G06K9/70 ,  G06N99/00

CPC分类号： G06K9/6257 ,  G06K9/3241 ,  G06K9/6217 ,  G06N99/005

摘要： The present invention relates to a classifier cascade object detection system. The system operates by inputting an image patch into parallel feature generation modules, each of the feature generation modules operable for extracting features from the image patch. The features are provided to an opportunistic classifier cascade, the opportunistic classifier cascade having a series of classifier stages. The opportunistic classifier cascade is executed by progressively evaluating, in each classifier in the classifier cascade, the features to produce a response, with each response progressively utilized by a decision function to generate a stage response for each classifier stage. If each stage response exceeds a stage threshold then the image patch is classified as a target object, and if the stage response from any of the decision functions does not exceed the stage threshold, then the image patch is classified as a non-target object.

摘要翻译： 本发明涉及分级器级联物体检测系统。 该系统通过将图像补丁输入到并行特征生成模块中来操作，每个特征生成模块可操作用于从图像补片提取特征。 这些特征被提供给机会分类器级联，机会分类器级联具有一系列分类器级。 机会分类器级联是通过在分类器级联中的每个分类器中逐步评估产生响应的特征来执行的，每个响应由决策函数逐渐被利用以产生每个分类器阶段的阶段响应。 如果每个阶段响应超过阶段阈值，则图像补丁被分类为目标对象，并且如果来自任何决策函数的阶段响应不超过阶段阈值，则将图像补丁分类为非目标对象。

公开(公告)号：US08818036B1

公开(公告)日：2014-08-26

申请号：US13558257

申请日：2012-07-25

申请人： Yuri Owechko ,  Shinko Y. Cheng ,  Swarup Medasani ,  Kyungnam Kim

发明人： Yuri Owechko ,  Shinko Y. Cheng ,  Swarup Medasani ,  Kyungnam Kim

IPC分类号： G06K9/00

CPC分类号： G06K9/00771 ,  G06K9/00369 ,  G06K9/209 ,  G06T7/277 ,  G06T2207/10016 ,  G06T2207/20076 ,  G06T2207/30232 ,  G06T2207/30241

摘要： Described is a system for registering a viewpoint of an imaging sensor with respect to a geospatial model or map. An image of a scene of a geospatial region comprising an object is received as input. The image of the scene is captured by a sensor having a current sensor state. Observation data related to the object's state is received, wherein the observation data comprises an object behavior of the object given the geospatial region. An estimate of the current sensor state is generated using a probability of an observation from the observation data given the current sensor state x. Finally, the image of the scene is registered with a geospatial model or map based on the estimate of the current sensor state.

摘要翻译： 描述了一种用于记录关于地理空间模型或地图的成像传感器的视点的系统。 接收包括对象的地理空间区域的场景的图像作为输入。 场景的图像由具有当前传感器状态的传感器捕获。 接收与对象状态相关的观测数据，其中观测数据包括给定地理空间区域的对象的对象行为。 使用给定当前传感器状态x的观测数据的观察概率来生成当前传感器状态的估计。 最后，基于当前传感器状态的估计，将场景的图像与地理空间模型或地图一起注册。

公开(公告)号：US07715591B2

公开(公告)日：2010-05-11

申请号：US10132875

申请日：2002-04-24

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： G06K9/6292 ,  G06K9/00201 ,  G06K9/00369 ,  G06K9/00832 ,  G06K9/209 ,  G06K9/522 ,  G06T7/97 ,  G06T2207/10021 ,  G06T2207/20064 ,  G06T2207/30268 ,  H04N2013/0081 ,  H04N2013/0092

摘要： A vision-based system for automatically detecting the type of object within a specified area, such as the type of occupant within a vehicle is presented. The type of occupant can then be used to determine whether an airbag deployment system should be enabled or not. The system extracts different features, including wavelet features and/or a disparity map from images captured by image sensors. These features are then processed by classification algorithms to produce class confidences for various occupant types. The occupant class confidences are fused and processed to determine occupant type. In a preferred embodiment, image features from image edges, wavelet features, and disparity are used. Various classification algorithms may be implemented to classify the object. Use of the disparity map and/or wavelet features provides greater computational efficiency.

摘要翻译： 提出了一种基于视觉的系统，用于自动检测指定区域内物体的类型，例如车辆内乘客的类型。 然后可以使用乘客的类型来确定是否应启用安全气囊展开系统。 该系统从图像传感器捕获的图像中提取不同的特征，包括小波特征和/或视差图。 然后通过分类算法对这些特征进行处理，以便为各种乘客类型生成类别信息。 乘员班信心被融合和处理以确定乘客类型。 在优选实施例中，使用来自图像边缘的图像特征，小波特征和视差。 可以实现各种分类算法来对对象进行分类。 使用视差图和/或小波特征提供更大的计算效率。

公开(公告)号：US20070183670A1

公开(公告)日：2007-08-09

申请号：US11433159

申请日：2006-05-12

申请人： Yuri Owechko ,  Swarup Medasani

发明人： Yuri Owechko ,  Swarup Medasani

IPC分类号： G06K9/62 ,  G06K9/46

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

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 group in a domain. Each node N represents an object in the group having K object attributes. Each agent is assigned an initial velocity vector to explore a KN-dimensional solution space for solutions matching the agent's graph. Further, each agent is configured to search the solution space for an optimum solution. The agents keep track of their coordinates in the KN-dimensional solution space that are associated with an observed best solution (pbest) and a global best solution (gbest). The gbest is used to store the best solution among all agents which corresponds to a best graph among all agents. Each velocity vector thereafter changes towards pbest and gbest, allowing the cooperative swarm to classify of the object group.

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以将域中的对象组分类的多个软件代理。 每个节点N表示具有K个对象属性的组中的对象。 为每个代理分配一个初始速度向量，以探索与代理图相匹配的解决方案的KN维解决方案空间。 此外，每个代理被配置为搜索解空间以获得最佳解决方案。 代理人跟踪与观察到的最佳解决方案（pbest）和全局最佳解决方案（gbest）相关联的KN维解决方案空间中的坐标。 gbest用于在所有代理之间存储对应于最佳图形的所有代理中的最佳解决方案。 其后每个速度矢量向pbest和gbest变化，允许协作群对目标群进行分类。

公开(公告)号：US08645294B1

公开(公告)日：2014-02-04

申请号：US12583238

申请日：2009-08-17

申请人： Yuri Owechko ,  Yang Chen ,  Swarup Medasani

发明人： Yuri Owechko ,  Yang Chen ,  Swarup Medasani

IPC分类号： G06F15/18

CPC分类号： G06N5/043 ,  G06K9/6229 ,  G06N3/006 ,  G06T7/337 ,  G06T7/35

摘要： Described is a method for image registration utilizing particle swarm optimization (PSO). In order to register two images, a set of image windows is first selected from a test image and transformed. A plurality of software agents is configured to operate as a cooperative swarm to optimize an objective function, and an objective function is then evaluated at the location of each agent. The objective function represents a measure of the difference or registration quality between at least one transformed image window and a reference image. The position vectors representing the current individual best solution found and the current global best solution found by all agents are then updated according to PSO dynamics. Finally, the current global best solution is compared with a maximum pixel value which signifies a match between an image window and the reference image. A system and a computer program product are also described.

摘要翻译： 描述了使用粒子群优化（PSO）的图像配准的方法。 为了注册两个图像，首先从测试图像中选择一组图像窗口并进行变换。 多个软件代理被配置为作为协作群来操作以优化目标函数，然后在每个代理的位置处评估目标函数。 目标函数表示至少一个变换的图像窗口和参考图像之间的差异或注册质量的度量。 然后根据PSO动态更新表示当前找到的最佳解决方案的位置向量和所有代理发现的当前全局最佳解。 最后，将当前全局最佳解决方案与表示图像窗口和参考图像之间的匹配的最大像素值进行比较。 还描述了系统和计算机程序产品。

公开(公告)号：US08553989B1

公开(公告)日：2013-10-08

申请号：US12799618

申请日：2010-04-27

申请人： Yuri Owechko ,  Swarup Medasani ,  Jim Nelson

发明人： Yuri Owechko ,  Swarup Medasani ,  Jim Nelson

IPC分类号： G06K9/00

CPC分类号： G06K9/00201 ,  G06K9/3233 ,  G06K9/468

摘要： The present invention relates to a method for three-dimensional (3D) object recognition using region of interest geometric features. The method includes acts of receiving an implicit geometry representation regarding a three-dimensional (3D) object of interest. A region of interest (ROI) is centered on the implicit geometry representation such that there is at least one intersection area between the ROI and the implicit geometry representation. Object shape features are calculated that reflect a location of the ROI with respect to the implicit geometry representation. The object shape features are assembled into a feature vector. A classification confidence value is generated with respect to a particular object classification. Finally, the 3D object of interest is classified as a particular object upon the output of a statistical classifier reaching a predetermined threshold.

摘要翻译： 本发明涉及使用感兴趣区域几何特征的三维（3D）物体识别方法。 该方法包括接收关于感兴趣的三维（3D）对象的隐式几何表示的动作。 感兴趣区域（ROI）以隐式几何表示为中心，使得ROI和隐式几何表示之间存在至少一个交叉区域。 计算反映相对于隐式几何表示的ROI的位置的对象形状特征。 对象形状特征被组合成特征向量。 相对于特定对象分类产生分类置信度值。 最后，感兴趣的3D对象在统计分类器的输出达到预定阈值时被分类为特定对象。

公开(公告)号：US08406522B1

公开(公告)日：2013-03-26

申请号：US12583239

申请日：2009-08-17

申请人： Yuri Owechko ,  Swarup Medasani

发明人： Yuri Owechko ,  Swarup Medasani

IPC分类号： G06K9/00

CPC分类号： G06K9/6228 ,  G06K9/6229

摘要： Described is a method for flexible feature adaptation and matching for object recognition in visual systems which incorporates evolutionary optimization. In the present invention, an analysis window is provided to select a portion of an input image to be analyzed for the presence or absence of an object. The analysis window is then divided into spatial regions, and a feature kernel function for each spatial region is selected and optimized. A feature value for each spatial region is calculated by finding a suitable location that generates the best matching features to a stored set using an optimization algorithm. The feature values are concatenated for the spatial regions to comprise a feature vector. Finally, the feature vector is processed by a classification algorithm, and a determination is made whether the object is present in the analysis window.

摘要翻译： 描述了一种用于可视化系统中的物体识别的灵活特征适应和匹配的方法，其包括进化优化。 在本发明中，提供了一个分析窗口，用于选择要分析的输入图像的一部分是否存在对象。 然后将分析窗口分为空间区域，并选择并优化每个空间区域的特征核函数。 通过使用优化算法找到对存储集合生成最佳匹配特征的合适位置来计算每个空间区域的特征值。 特征值被连接以使空间区域包括特征向量。 最后，通过分类算法处理特征向量，并确定对象是否存在于分析窗口中。

公开(公告)号：US08253792B2

公开(公告)日：2012-08-28

申请号：US12549425

申请日：2009-08-28

申请人： James W. Wells ,  Roland J. Menassa ,  Charles W. Wampler, II ,  Swarup Medasani ,  Yuri Owechko ,  Kyungnam Kim ,  Yang Chen

发明人： James W. Wells ,  Roland J. Menassa ,  Charles W. Wampler, II ,  Swarup Medasani ,  Yuri Owechko ,  Kyungnam Kim ,  Yang Chen

IPC分类号： H04N9/47

CPC分类号： H04N7/181

摘要： A safety monitoring system for a workspace area. The workspace area related to a region having automated moveable equipment. A plurality of vision-based imaging devices capturing time-synchronized image data of the workspace area. Each vision-based imaging device repeatedly capturing a time synchronized image of the workspace area from a respective viewpoint that is substantially different from the other respective vision-based imaging devices. A visual processing unit for analyzing the time-synchronized image data. The visual processing unit processes the captured image data for identifying a human from a non-human object within the workspace area. The visual processing unit further determining potential interactions between a human and the automated moveable equipment. The visual processing unit further generating control signals for enabling dynamic reconfiguration of the automated moveable equipment based on the potential interactions between the human and the automated moveable equipment in the workspace area.

摘要翻译： 用于工作区的安全监控系统。 与具有自动移动设备的区域相关的工作空间区域。 多个基于视觉的成像设备捕获工作区域的时间同步图像数据。 每个基于视觉的成像设备从与其他各自的基于视觉的成像设备基本上不同的相应视点重复地捕获工作区域的时间同步图像。 一种用于分析时间同步图像数据的可视处理单元。 视觉处理单元从工作区域内的非人物对象处理用于识别人的拍摄图像数据。 视觉处理单元进一步确定人与自动移动设备之间的潜在交互作用。 视觉处理单元还基于人与工作空间区域中的自动移动设备之间的潜在交互，进一步产生用于实现自动移动设备的动态重新配置的控制信号。

公开(公告)号：US07778466B1

公开(公告)日：2010-08-17

申请号：US11004501

申请日：2004-12-02

申请人： Swarup Medasani ,  Yuri Owechko

发明人： Swarup Medasani ,  Yuri Owechko

IPC分类号： G06K9/48

CPC分类号： G06K9/4647 ,  G06K9/00744 ,  G06K9/00805

摘要： A method, computer program product, and system for processing imagery is presented. The imagery is processed by receiving data regarding a scene (such as from a sensor monitoring a scene). The scene includes an object having a dimension. Flow vectors are computed from the data, while a flow histogram space is generated from the flow vectors. A line segment (with a length) is found within the flow histogram space. An object in the scene is associated with the length segment, and the dimensions of the object are estimated based on the length of the line segment.

摘要翻译： 提出了一种处理图像的方法，计算机程序产品和系统。 通过接收关于场景的数据（例如从监​​视场景的传感器）处理图像。 场景包括具有维度的对象。 从数据计算流向量，而从流向量生成流直方图空间。 在流直方图空间内找到一个线段（长度）。 场景中的对象与长度段相关联，并且基于线段的长度来估计对象的尺寸。

公开(公告)号：US20070065014A1

公开(公告)日：2007-03-22

申请号：US11231327

申请日：2005-09-20

申请人： Yuri Owechko ,  Swarup Medasani

发明人： Yuri Owechko ,  Swarup Medasani

IPC分类号： G06K9/46 ,  G06K9/32

CPC分类号： G06K9/46 ,  G06K9/42

摘要： A computer vision system includes distorting optics producing distorted or warped input images. The system includes an integral feature object classifier trained using an undistorted image space. Undistorted integral feature values are calculated directly from distorted input images without undistorting or dewarping the distorted input image.

摘要翻译： 计算机视觉系统包括产生失真或翘曲输入图像的变形光学器件。 该系统包括使用未失真的图像空间训练的整体特征对象分类器。 非失真积分特征值直接从失真的输入图像计算，而不会使失真输入图像失真或杜绝。