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公开(公告)号：US09600767B1
公开(公告)日：2017-03-21
申请号：US12317884
申请日：2008-12-30
申请人： Aleksey Nogin , Deepak Khosla , Yang Chen
发明人： Aleksey Nogin , Deepak Khosla , Yang Chen
CPC分类号： G06N5/02 , G06N3/08 , G06N5/04 , G06N99/005
摘要： The present invention relates to a system, method, and computer program product for generating a single software code based on a description of a distributed architecture. The present invention introduces a BICA-SMART development framework that implements a distributed architecture in a flexible, parallel and scalable implementation that is embodied by a single software code. Thus, using the present invention, a user can input a description of the architecture into the system, with the system automatically generating the software code to implement the architectural description.
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2.发明授权Method for recognition and pose estimation of multiple occurrences of multiple objects in visual images 有权用于识别和姿态估计多个对象在视觉图像中的方法公开(公告)号：US08837839B1
公开(公告)日：2014-09-16
申请号：US12938722
申请日：2010-11-03
申请人： David J. Huber , Deepak Khosla
发明人： David J. Huber , Deepak Khosla
CPC分类号： G06K9/6218 , G06K9/4671
摘要： Described is a system for multiple-object recognition in visual images. The system is configured to receive an input test image comprising at least one object. Keypoints representing the object are extracted using a local feature algorithm. The keypoints from the input test image are matched with keypoints from at least one training image stored in a training database, resulting in a set of matching keypoints. A clustering algorithm is applied to the set of matching keypoints to detect inliers among the set of matching keypoints. The inliers and neighboring keypoints in a vicinity of the inliers are removed from the input test image. An object label and an object boundary for the object are generated, and the object in the input test image is identified and segmented. Also described is a method and computer program product for multiple-object recognition in visual images.
摘要翻译： 描述了一种在视觉图像中进行多目标识别的系统。 该系统被配置为接收包括至少一个对象的输入测试图像。 使用局部特征算法提取表示对象的关键点。 来自输入测试图像的关键点与来自存储在训练数据库中的至少一个训练图像的关键点匹配，从而产生一组匹配的关键点。 将聚类算法应用于匹配关键点集合，以检测匹配关键点集合之间的内联。 从入口测试图像中删除内部值附近的内联和相邻关键点。 生成对象标签和对象边界，并且识别和分割输入测试图像中的对象。 还描述了一种用于视觉图像中的多目标识别的方法和计算机程序产品。
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公开(公告)号：US08774517B1
公开(公告)日：2014-07-08
申请号：US12982713
申请日：2010-12-30
IPC分类号： G06K9/00
CPC分类号： G06K9/00771 , G06K9/00993 , G06K9/4623 , G06K9/629
摘要： The present invention relates to a system for identifying regions of interest in visual imagery. The system is configured to receive a series of consecutive frames representing a scene as captured from N sensors. The frames include at least a current frame and a previous frame. A surprise map can be generated based on features found in the current frame and the previous frame. The surprise map having a plurality of values corresponding to spatial locations within the scene. Based on the values, a surprise in the scene can be identified if a value in the surprise map exceeds a predetermined threshold.
摘要翻译： 本发明涉及用于识别视觉图像中的感兴趣区域的系统。 该系统被配置为从N个传感器接收一系列表示场景的连续帧。 帧至少包括当前帧和前一帧。 可以基于当前帧和前一帧中发现的特征生成惊喜图。 惊奇地图具有对应于场景内的空间位置的多个值。 基于这些值，如果惊奇图中的值超过预定阈值，则可以识别场景中的惊喜。
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公开(公告)号：US08285052B1
公开(公告)日：2012-10-09
申请号：US12653561
申请日：2009-12-15
CPC分类号： G06F17/30247
摘要： Described is a system for ordering images. The system receives a plurality of images. Image features are extracted from each image. A set of all possible image pairs are generated for all images. A similarity metric with weights is generated between the images in each image pair in the set, with a net similarity metric thereafter generated by combining the similarity metrics. The images are then ordered according to the net similarity metrics to generate a computer-ordered set of images. The computer-ordered set of images is then displayed to the user, which allows the user to re-order the images to generate a user-ordered set of images. The weights are then optimized to minimize the distance between the computer-ordered set of images and the user-ordered set of images. The similarity metrics are then re-weighted, with the images thereafter being re-ordered according to the new metrics.
摘要翻译： 描述了一种用于排序图像的系统。 系统接收多个图像。 从每个图像中提取图像特征。 为所有图像生成一组所有可能的图像对。 在集合中的每个图像对中的图像之间生成具有权重的相似性度量，其后通过组合相似度度量而生成净相似度度量。 然后根据网络相似度度量对图像进行排序以生成计算机有序的图像集合。 然后将计算机有序的图像集合显示给用户，这允许用户重新排序图像以生成用户有序的图像集合。 然后优化权重以最小化计算机有序的图像集与用户有序的图像集之间的距离。 然后相似性度量被重新加权，其后的图像根据新的度量重新排序。
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公开(公告)号：US20080250875A1
公开(公告)日：2008-10-16
申请号：US11787135
申请日：2007-04-13
申请人： Deepak Khosla , James Guillochon
发明人： Deepak Khosla , James Guillochon
IPC分类号： G01N1/00
CPC分类号： G01N35/0092 , G01N35/00871
摘要： A method and system of a sparse sampling planner uses a finite number of measurements to determine a track's expected intermediate kinematic and classification state for a specific sensor action. It uses the expected track state to compute a reward function. The expected states are further propagated for actions at the next time step to determine the next states and so on. The sampling becomes sparse and the reward function is discounted as one propagates further in time. This produces a state-action tree that is more top-heavy while providing greater accuracy at times closer to the decision point. By doing so, the planner creates a plan comprising a sequence of actions that result in the highest reward. By employing various heuristics to further prune the tree gives highly accurate results with significant savings in computational processor time.
摘要翻译： 稀疏采样计划器的方法和系统使用有限数量的测量来确定特定传感器动作的轨道的预期中间运动学和分类状态。 它使用预期轨迹状态计算奖励功能。 在下一个时间步骤中进一步传播预期状态以确定下一个状态等等。 抽样变得稀疏，奖励功能随着时间的推移而被打折扣。 这产生了一个状态动作树，它在更接近决策点的同时提供更高的精度，更重要。 通过这样做，计划员创建一个计划，其中包含一系列可以获得最高回报的动作。 通过采用各种启发式方式进一步修剪树，可以显着节省计算处理器时间，从而获得高精度的结果。
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6.发明授权公开(公告)号：US06965900B2
公开(公告)日：2005-11-15
申请号：US10026065
申请日：2001-12-19
CPC分类号： G06F17/30699 , Y10S707/99933 , Y10S707/99935 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945
摘要： An apparatus and method provides application specific multidimensional information to an application running on a user computing device from a plurality of member documents electronically extracted from a library of electronically searchable documents. An information extractor is adapted to extract occurrences of prospective representations of dimensions of application specific multidimensional information and occurrences of non-application specific multidimensional information from the member documents. Also, an encoder is adapted to encode the occurrences of prospective dimensions of application specific and non-application specific multidimensional information contained in member documents. A member document identifier determines document formatting and decides whether to proceed with further processing. An information verification unit optionally verifies the extraction of application specific multidimensional information from the member documents. A database optionally stores and provides access to the application specific multidimensional information, which may for example be scheduled events having dimensions of time, location, identity.
摘要翻译： 一种装置和方法从用户计算装置上运行的应用程序向从电子可搜索文件库电子提取的多个成员文件提供应用特定的多维信息。 信息提取器适于从成员文档中提取应用程序特定多维信息的维度的预期表示以及非应用程序特定多维信息的出现。 此外，编码器适于对包含在成员文档中的应用特定和非应用特定多维信息的预期维度的出现进行编码。 成员文件标识符确定文档格式，并决定是否进行进一步处理。 信息验证单元可选地验证从成员文档中提取特定于应用的多维信息。 数据库可选地存储并提供对应用程序特定的多维信息的访问，其可以例如是具有时间，位置，身份的维度的调度事件。
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7.发明授权公开(公告)号：US06497169B1
公开(公告)日：2002-12-24
申请号：US09834425
申请日：2001-04-13
申请人： Deepak Khosla
发明人： Deepak Khosla
IPC分类号： B64D104
摘要： A system and method for automatic weapon allocation and scheduling of the present invention. The inventive method includes the steps of providing data with respect to threats, weapons, weapon allocation options; weapon allocation rules; and temporally dependent constraints with respect thereto; evaluating the data; and temporally allocating the weapons to the threats automatically in accordance with the evaluation. The invention computes the optimal pairing and the best time to deploy each weapon system against threat(s) it is paired with in arriving at the pairing. This results in an optimal assignment where weapon resource constraints are not exceeded and therefore guarantee availability of sufficient resources for engagement of every threat that is paired with a weapon system.
摘要翻译： 本发明的自动武器分配和调度的系统和方法。 本发明的方法包括提供关于威胁，武器，武器分配选项的数据的步骤; 武器分配规则; 和时间依赖的约束; 评估数据; 并根据评估自动将武器临时分配给威胁。 本发明计算出最佳配对和最佳时间来部署每个武器系统以抵御与之配对的威胁。 这导致了不超过武器资源限制的最佳分配，因此保证了与武器系统配对的每个威胁的参与的足够资源的可用性。
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公开(公告)号：US08369652B1
公开(公告)日：2013-02-05
申请号：US12584744
申请日：2009-09-11
申请人： Deepak Khosla , David J. Huber
发明人： Deepak Khosla , David J. Huber
IPC分类号： G06K9/36
CPC分类号： G06K9/4623 , G06K9/3241
摘要： Described is a system for finding salient regions in imagery. The system improves upon the prior art by receiving an input image of a scene and dividing the image into a plurality of image sub-regions. Each sub-region is assigned a coordinate position within the image such that the sub-regions collectively form the input image. A plurality of local saliency maps are generated, where each local saliency map is based on a corresponding sub-region and a coordinate position representative of the corresponding sub-region. Finally, the plurality of local saliency maps is combined according to their coordinate positions to generate a single global saliency map of the input image of the scene.
摘要翻译： 描述了一种用于在图像中查找显着区域的系统。 该系统通过接收场景的输入图像并将图像划分为多个图像子区域来改进现有技术。 每个子区域被分配在图像内的坐标位置，使得子区域共同形成输入图像。 生成多个局部显着图，其中每个局部显着图基于对应的子区域和表示相应子区域的坐标位置。 最后，根据它们的坐标位置组合多个局部显着图，以生成场景的输入图像的单个全局显着图。
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9.发明授权System for intelligent goal-directed search in large volume imagery and video using a cognitive-neural subsystem 有权使用认知神经子系统的大容量图像和视频中的智能目标导向搜索系统公开(公告)号：US08335751B1
公开(公告)日：2012-12-18
申请号：US12589052
申请日：2009-10-15
申请人： Michael J. Daily , Deepak Khosla , Ronald T. Azuma
发明人： Michael J. Daily , Deepak Khosla , Ronald T. Azuma
CPC分类号： G06N3/0427 , G06K9/00973 , G06K9/6263 , G06N3/08
摘要： A system for intelligent goal-directed search in large volume visual imagery using a cognitive-neural subsystem is disclosed. The system comprises an imager, a display, a display processor, a cognitive-neural subsystem, a system controller, and operator controls. The cognitive-neural subsystem comprises a cognitive module, a neural module, and an adaptation module. The cognitive module is configured to extract a set of regions of interest from the image using a cognitive algorithm. The neural module is configured to refine the set of regions of interest using a neural processing algorithm. The adaptation module is configured to bias the cognitive algorithm with information gained from the neural module to improve future searches. The system functions in a plurality of operating modes, including: batch mode, semi-interactive mode, real-time mode, and roaming mode.
摘要翻译： 公开了一种使用认知神经子系统进行大容量视觉图像智能目标导向搜索的系统。 该系统包括成像器，显示器，显示处理器，认知神经子系统，系统控制器和操作者控制。 认知神经子系统包括认知模块，神经模块和适配模块。 认知模块被配置为使用认知算法从图像中提取一组感兴趣的区域。 神经模块被配置为使用神经处理算法来细化感兴趣区域集合。 适配模块被配置为利用从神经模块获得的信息来偏差认知算法以改进未来搜索。 该系统以多种操作模式运行，包括批量模式，半交互模式，实时模式和漫游模式。
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公开(公告)号：US08214309B1
公开(公告)日：2012-07-03
申请号：US12316779
申请日：2008-12-16
申请人： Deepak Khosla , Michael J. Daily
发明人： Deepak Khosla , Michael J. Daily
CPC分类号： G06N3/0427 , G06K9/4628 , G06K9/6264 , G06K9/6293
摘要： A self adapting cognitive-neural method and system for image analysis is disclosed. The method comprises detecting a set of potential items of interest in an image using a cognitive algorithm, refining the set of potential items of interest using a neural analysis, and adapting the cognitive algorithm based on information gained from the results of the neural analysis. The method may further comprise fusing the results of the cognitive and neural algorithms with a fusion classifier. The neural analysis can include eye tracked EEG, RSVP, or presentation to an operator for visual inspection.
摘要翻译： 公开了一种自适应认知神经方法和图像分析系统。 该方法包括使用认知算法检测图像中的一组潜在的兴趣项目，使用神经分析来对感兴趣的潜在项目进行细化，以及基于从神经分析结果获得的信息来适应认知算法。 该方法还可以包括将认知和神经算法的结果与融合分类器融合。 神经分析可以包括眼睛跟踪的EEG，RSVP或呈现给操作者进行目视检查。
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