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    Optimal multi-class classifier threshold-offset estimation with particle swarm optimization for visual object recognition
    1.
    发明授权
    Optimal multi-class classifier threshold-offset estimation with particle swarm optimization for visual object recognition 有权
    用于视觉对象识别的粒子群优化的最优多类分类器阈值偏移估计

    公开(公告)号：US08768868B1

    公开(公告)日：2014-07-01

    申请号：US13440881

    申请日：2012-04-05

    申请人： Shinko Y. Cheng ,  Yang Chen ,  Deepak Khosla ,  Kyungnam Kim

    发明人： Shinko Y. Cheng ,  Yang Chen ,  Deepak Khosla ,  Kyungnam Kim

    IPC分类号： G06N5/00

    CPC分类号： G06N5/00

    摘要： Described is a system for multi-class classifier threshold-offset estimation for visual object recognition. The system receives an input image with input features for classifying. A pair-wise classifier is trained for each pair of a plurality of object classes. A set of classification responses is generated, and a multi-class receiver-operating-characteristics (ROC) curve is computed for a set of threshold-offsets. An objective function of classification performance is computed from the ROC curve and optimized using particle swarm optimization (PSO) to generate a set of optimized threshold-offsets. The optimized threshold-offsets are then applied to the classification responses. The resulting classification responses are compared to a predetermined value to classify each input feature as belonging to one object class or another. The tuning of the threshold-offsets with (PSO) improves classification performance in a visual object recognition system.

    摘要翻译： 描述了用于视觉对象识别的多类分类器阈值偏移估计的系统。 系统接收具有输入特征进行分类的输入图像。 针对多对象类的每一对训练一对成对的分类器。 生成一组分类响应，并计算一组阈值偏移量的多类接收器操作特性（ROC）曲线。 从ROC曲线计算分类性能的目标函数，并使用粒子群优化（PSO）进行优化，以生成一组优化的阈值偏移。 然后将优化的阈值偏移应用于分类响应。 将所得分类响应与预定值进行比较，以将每个输入特征分类为属于一个对象类或另一对象类。 使用（PSO）调整阈值偏移可提高视觉对象识别系统中的分类性能。

    Contextual behavior state filter for sensor registration
    2.
    发明授权
    Contextual behavior state filter for sensor registration 有权
    用于传感器注册的上下文行为状态过滤器

    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。

    Opportunistic cascade and cascade training, evaluation, and execution for vision-based object detection
    5.
    发明授权
    Opportunistic cascade and cascade training, evaluation, and execution for vision-based object detection 有权
    机会级联和级联培训，评估和执行基于视觉的对象检测

    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。

    Method and system for embedding visual intelligence
    6.
    发明授权
    Method and system for embedding visual intelligence 有权
    嵌入视觉智能的方法和系统

    公开(公告)号：US09129158B1

    公开(公告)日：2015-09-08

    申请号：US13412527

    申请日：2012-03-05

    申请人： Swarup Medasani ,  Suhas E. Chelian ,  Shinko Y. Cheng ,  Rashmi N. Sundareswara ,  Howard Neely, III

    发明人： Swarup Medasani ,  Suhas E. Chelian ,  Shinko Y. Cheng ,  Rashmi N. Sundareswara ,  Howard Neely, III

    IPC分类号： G06K9/62 ,  G06K9/00 ,  G06T7/20

    CPC分类号： G06K9/00718 ,  G06K9/00335 ,  G06K9/00342 ,  G06K9/6219 ,  G06K9/6263 ,  G06K9/6296 ,  G06T2207/10016

    摘要： Described is a method and system for embedding unsupervised learning into three critical processing stages of the spatio-temporal visual stream. The system first receives input video comprising input video pixels representing at least one action and at least one object having a location. Microactions are generated from the input image using a set of motion sensitive filters. A relationship between the input video pixels and the microactions is then learned, and a set of spatio-temporal concepts is learned from the microactions. The system then learns to acquire new knowledge from the spatio-temporal concepts using mental imagery processes. Finally, a visual output is presented to a user based on the learned set of spatio-temporal concepts and the new knowledge to aid the user in visually comprehending the at least one action in the input video.

    摘要翻译： 描述了将无监督学习嵌入时空视觉流的三个关键处理阶段的方法和系统。 系统首先接收包括表示至少一个动作的输入视频像素和至少一个具有位置的对象的输入视频。 使用一组运动敏感滤波器从输入图像生成微反应。 然后学习输入视频像素和微动作之间的关系，并从微动态学习一组时空概念。 该系统然后学习从使用心理图像过程的时空概念中获取新知识。 最后，基于学习的时空概念和新知识，向用户呈现视觉输出，以帮助用户直观地理解输入视频中的至少一个动作。