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1. 发明授权

US08270671B1 Method and system for generic object detection using block features 失效
标题翻译：使用块特征的通用对象检测的方法和系统
公开(公告)号：US08270671B1
公开(公告)日：2012-09-18
申请号：US12380415
申请日：2009-02-27
申请人： Swarup Medasani , Rahul Shringarpure
发明人： Swarup Medasani , Rahul Shringarpure
IPC分类号： G06K9/00
CPC分类号： G06K9/00986 , G06K9/4642 , G06K9/6202
摘要： Disclosed is a method and system for generic object detection using block-based feature computation and, more specifically, a method and system for massively parallel computation of object features sets according to an optimized clock-cycle matrix. The method uses an array of correlators to calculate block sums for each section of the image to be analyzed. A greedy heuristic scheduling algorithm is executed to produce an optimized clock cycle matrix such that overlapping features which use the same block sum do not attempt to access the block at the same time, thereby avoiding race memory conditions. The processing system can employ any of a variety of hardwired Very Large Scale Integration (VLSI) chips such as Field Programmable Gate Arrays (FPGAs), Digital Signal Processors (DSPs) and Application Specific Integrated Circuits (ASICs).
摘要翻译：公开了一种使用基于块的特征计算的通用对象检测的方法和系统，更具体地，涉及根据优化的时钟周期矩阵对对象特征集进行大规模并行计算的方法和系统。该方法使用相关器阵列来计算待分析图像的每个部分的块和。执行贪婪启发式调度算法以产生优化的时钟周期矩阵，使得使用相同块和的重叠特征不尝试同时访问块，从而避免竞态存储器条件。处理系统可以采用各种硬连线超大规模集成（VLSI）芯片，例如现场可编程门阵列（FPGA），数字信号处理器（DSP）和专用集成电路（ASIC）。

2. 发明授权

US07761389B2 Method for anomaly prediction of battery parasitic load 失效
标题翻译：电池寄生负载异常预测方法
公开(公告)号：US07761389B2
公开(公告)日：2010-07-20
申请号：US11843876
申请日：2007-08-23
申请人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
发明人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
IPC分类号： G06F15/18 , G06N7/02
CPC分类号： G01R31/3606 , G01R31/3693
摘要： Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.
摘要翻译：电池寄生负载的异常预测包括利用机器学习算法处理与电池的充电状态有关的输入数据和持续时间因子，并产生预测的充电状态。如果预计的启动状态在运行时间内下降到阈值以下，则发出警告。

3. 发明授权

US07715591B2 High-performance sensor fusion architecture 有权
标题翻译：高性能传感器融合架构
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。

4. 发明申请

US20070183670A1 Graph-based cognitive swarms for object group recognition 有权
标题翻译：基于图的认知群体，用于对象组识别
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。

5. 发明授权

US09805301B1 Dynamic background estimation for video analysis using evolutionary optimization 有权
公开(公告)号：US09805301B1
公开(公告)日：2017-10-31
申请号：US12583519
申请日：2009-08-20
申请人： Yuri Owechko , Swarup Medasani , Payam Saisan
发明人： Yuri Owechko , Swarup Medasani , Payam Saisan
IPC分类号： G06N3/00 , G06K9/00
CPC分类号： G06N3/006 , G06K9/00335 , G06K9/00369 , G06K9/00624 , G06K9/00771 , G06K9/3233 , G06K9/6229
摘要： Described is a system for dynamic background estimation which utilizes Particle Swarm Optimization (PSO). The present invention comprises a system, method, and computer program product for accurate estimation of a background mask corresponding to a dynamically changing scene. The system is configured to construct a background template model of a scene, and then capture an image of a current view of the scene with a camera. Thereafter, the system generates an image-based template matching cost function as an optimization problem, where the objective is to identify and fit a corresponding subregion of the background template model to the current camera view. The cost function is optimized using a PSO search algorithm. Finally, the system is configured to generate the corresponding subregion of the background template model for display. The inherent efficiency of PSO makes this system conducive for use in applications requiring real-time background estimation.

6. 发明授权

US08793200B1 Method for particle swarm optimization with random walk 有权
标题翻译：随机散乱的粒子群优化方法
公开(公告)号：US08793200B1
公开(公告)日：2014-07-29
申请号：US12586505
申请日：2009-09-22
申请人： Yang Chen , Yuri Owechko , Swarup Medasani
发明人： Yang Chen , Yuri Owechko , Swarup Medasani
IPC分类号： G06N5/00
CPC分类号： G06N5/003 , G06N3/006
摘要： Described is a method for particle swarm optimization (PSO) utilizing a random walk process. A plurality of software agents is configured to operate as a cooperative swarm to locate an optimum of an objective function. The method described herein comprises two phases. In a first phase, the plurality of software agents randomly explores the multi-dimensional solution space by undergoing a Brownian motion style random walk process. In a second phase, the velocity and position vectors for each particle are updated probabilistically according to a PSO algorithm. By allowing the particles to undergo a random walk phase, the particles have an increased opportunity to explore their neighborhood, land in the neighborhood of a true optimum, and avoid prematurely converging on a sub-optimum. The present invention improves on what is currently known by increasing the success rate of the PSO algorithm in addition to reducing the required computation.
摘要翻译：描述了利用随机游走过程的粒子群优化（PSO）的方法。多个软件代理被配置为作为协作群操作以定位目标函数的最优。本文描述的方法包括两个阶段。在第一阶段，多个软件代理人通过经历布朗运动风格随机游走过程随机探索多维解决方案空间。在第二阶段，根据PSO算法概率地更新每个粒子的速度和位置向量。通过允许颗粒经历随机游走阶段，颗粒具有增加的机会来探索它们的邻域，附近的真实最优值，并避免过早地收敛于次优。除了减少所需的计算之外，本发明通过增加PSO算法的成功率来改进当前所知道的内容。

7. 发明授权

US08515184B1 System for visual object recognition using heterogeneous classifier cascades 有权
标题翻译：使用异类分类器级联的视觉对象识别系统
公开(公告)号：US08515184B1
公开(公告)日：2013-08-20
申请号：US12462017
申请日：2009-07-28
申请人： Swarup Medasani , Yuri Owechko
发明人： Swarup Medasani , Yuri Owechko
IPC分类号： G06K9/62
CPC分类号： G06K9/6257
摘要： Described is a system for visual object recognition using heterogeneous classifier cascades. Visual object recognition is one of the most critical tasks for video and image analysis applications. The present invention utilizes a cascade of classifiers, wherein each stage is dedicated to a certain task such as achieving high accuracy or reducing false alarms. The stages are then appropriately trained using either the training data or false alarm datasets, respectively. Additionally, the features that are employed by the classifier cascades are heterogeneous and complementary in that several types of features may be used. The system described herein has multiple applications in a variety of fields including automotive safety, factory automation, surveillance, force protection, and automatic target recognition.
摘要翻译：描述了使用异类分类器级联的视觉对象识别系统。视觉对象识别是视频和图像分析应用程序中最关键的任务之一。本发明利用了级联的分类器，其中每个阶段专用于一些任务，例如实现高精度或减少假警报。然后分别使用训练数据或假警报数据集适当训练各个阶段。此外，分类器级联采用的特征是异构和互补的，因为可以使用几种类型的特征。本文描述的系统在各种领域中具有多种应用，包括汽车安全，工厂自动化，监视，力保护和自动目标识别。

8. 发明授权

US08515126B1 Multi-stage method for object detection using cognitive swarms and system for automated response to detected objects 有权
标题翻译：使用认知群体的对象检测的多阶段方法和用于对检测到的对象的自动响应的系统
公开(公告)号：US08515126B1
公开(公告)日：2013-08-20
申请号：US12456558
申请日：2009-06-18
申请人： Swarup Medasani , Yuri Owechko , Michael Daily , Ronald Azuma
发明人： Swarup Medasani , Yuri Owechko , Michael Daily , Ronald Azuma
IPC分类号： G06K9/00
CPC分类号： G06K9/00369 , G06K9/6229 , G06K9/6807
摘要： A multi-stage method of visual object detection is disclosed. The method was originally designed to detect humans in specific poses, but is applicable to generic detection of any object. A first stage comprises acts of searching for members of a predetermined general-class of objects (such as humans) in an image using a cognitive swarm, detecting members of the general-class of objects in the image, and selecting regions of the image containing detected members of the general-class of objects. A second stage comprises acts of searching for members of a predetermined specific-class of objects (such as humans in a certain pose) within the selected regions of the image using a cognitive swarm, detecting members of the specific-class of objects within the selected regions of the image, and outputting the locations of detected objects to an operator display and optionally to an automatic response system.
摘要翻译：公开了一种视觉对象检测的多级方法。该方法最初设计用于以特定姿势检测人类，但适用于任何物体的通用检测。第一阶段包括使用认知群搜索图像中预定的一般类别的对象（诸如人）的成员的动作，检测图像中的一般对象的成员，以及选择包含检测到一般类对象的成员。第二阶段包括使用认知群体搜索图像的所选区域内的预定特定类别的对象（例如人类在某种姿势中）的成员的动作，检测所选择的特定类别的对象的成员并且将检测到的对象的位置输出到操作者显示器，并且可选地输出到自动响应系统。

9. 发明授权

US08285655B1 Method for object recongnition using multi-layered swarm sweep algorithms 有权
标题翻译：使用多层群扫描算法的对象识别方法
公开(公告)号：US08285655B1
公开(公告)日：2012-10-09
申请号：US12587925
申请日：2009-10-13
申请人： Swarup Medasani , Yuri Owechko
发明人： Swarup Medasani , Yuri Owechko
IPC分类号： G06F15/18
CPC分类号： G06K9/00664 , G06K9/00973
摘要： Described is a system for multi-layered object detection which presents a unified way of processing an entire field-of-view (FOV) using cognitive swarms of software agents and classifier cascades by partitioning the FOV into layers and processing the closest layer first. A plurality of software agents operate as a cooperative swarm to search the first layer of the field-of-view to locate an objective function optima according to particle swarm optimization dynamics, wherein the objective function optima corresponds to a location of an object in the image in a layer of the field-of-view. The other layers are then sequentially swept to detect other objects in the FOV. In another aspect, the layers correspond to layers of increasing resolution in a hierarchical image pyramid. By using the cooperative swarm to search the coarser resolution layers first, objects can be detected more rapidly. A method and computer program product are also described.
摘要翻译：描述了一种用于多层物体检测的系统，其提供了使用认知群的软件代理和分类器级联来处理整个视场（FOV）的统一方式，通过将FOV分成层并且首先处理最近的层。多个软件代理作为协作群操作以搜索视场的第一层以根据粒子群优化动态来定位目标函数最优，其中目标函数最优对应于图像中对象的位置在视野的一层。然后依次扫描其他层以检测FOV中的其他物体。在另一方面，这些层对应于分层图像金字塔中分辨率增加的层。首先通过使用合作群搜索较粗的分辨率层，可以更快速地检测对象。还描述了一种方法和计算机程序产品。

10. 发明授权

US07672911B2 Graph-based cognitive swarms for object group recognition in a 3N or greater-dimensional solution space 有权
标题翻译：基于图的认知群体，用于3N或更大维解决方案空间中的对象组识别
公开(公告)号：US07672911B2
公开(公告)日：2010-03-02
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： G06K9/00 , G06K9/62 , G06F15/18
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。

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