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公开(公告)号：US09020870B1
公开(公告)日：2015-04-28
申请号：US13160406
申请日：2011-06-14
摘要： Described is a recall system that uses spiking neuron networks to identify an unknown external stimulus. The system operates by receiving a first input signal (having spatial-temporal data) that originates from a known external stimulus. The spatial-temporal data is converted into a first spike train. A first set of polychronous groups (PCGs) are generated as a result of the first spike train. Thereafter, a second input signal originating from an unknown external stimulus is received. The spatial-temporal data of the second input signal is converted into a second spike train. A second set of PCGs are then generated as a result of the second spike train. Finally, the second set of PCGs is recognized as being sufficiently similar to the first set of PCGs to identify the unknown external stimulus as the known external stimulus.
摘要翻译： 描述的是使用尖峰神经元网络来识别未知的外部刺激的召回系统。 系统通过接收源自已知外部刺激的第一输入信号（具有空间 - 时间数据）来操作。 空间 - 时间数据被转换成第一尖峰列。 作为第一尖峰列车的结果产生第一组多组同步组（PCG）。 此后，接收源自未知外部刺激的第二输入信号。 第二输入信号的空间 - 时间数据被转换成第二尖峰列。 作为第二个尖峰列车的结果，然后产生第二组PCG。 最后，第二组PCG被识别为与第一组PCG相当，以识别未知的外部刺激作为已知的外部刺激。
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2.发明申请System and Method for Predicting Political Instability using Bayesian Networks 审中-公开使用贝叶斯网络预测政治不稳定的系统和方法公开(公告)号：US20120323826A1
公开(公告)日：2012-12-20
申请号：US13159759
申请日：2011-06-14
CPC分类号： G06N7/005
摘要： Disclosed is a system and method for predicting political instability. This instability is predicted for specific countries or geographic regions. In one embodiment, the prediction is carried out on a basis of a probabilistic model, such as a Bayesian-network. The model is comprised of various notes corresponding to dependent and independent variables. The independent variables, in turn, correspond to factors relating to historical political instability. The dependent variable corresponds to the prediction of instability. By populating the independent variables with current data, future political instability can be predicted.
摘要翻译： 披露了一种预测政治不稳定的制度和方法。 这种不稳定性是针对特定国家或地理区域预测的。 在一个实施例中，基于诸如贝叶斯网络的概率模型来执行预测。 该模型由对应于依赖和独立变量的各种笔记组成。 反过来，这些独立变量也与历史政治不稳定有关。 因变量对应于不稳定性的预测。 通过使用当前数据填充自变量，可以预测未来的政治不稳定性。
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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
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。
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4.发明授权公开(公告)号：US08774504B1
公开(公告)日：2014-07-08
申请号：US13282389
申请日：2011-10-26
IPC分类号： G06K9/00
CPC分类号： G06K9/6267 , G06K9/00201 , G06K9/342 , G06K9/4652 , G06K9/4671 , G06K9/66 , G06T7/11 , G06T7/194 , G06T2207/20081 , G06T2207/20164
摘要： The present invention describes a system for recognizing objects from color images by detecting features of interest, classifying them according to previous objects' features that the system has been trained on, and finally drawing a boundary around them to separate each object from others in the image. Furthermore, local feature detection algorithms are applied to color images, outliers are removed, and resulting feature descriptors are clustered to achieve effective object recognition. Additionally, the present invention describes a system for extracting foreground objects and the correct rejection of the background from an image of a scene. Importantly, the present invention allows for changes to the camera viewpoint or lighting between training and test time. The system uses a supervised-learning algorithm and produces blobs of foreground objects that a recognition algorithm can then use for object detection/recognition.
摘要翻译： 本发明描述了一种用于通过检测感兴趣的特征来识别彩色图像中的对象的系统，根据系统已经被训练的先前对象的特征对它们进行分类，并且最终在它们周围绘制边界以将每个对象与图像中的其他物体分开 。 此外，将局部特征检测算法应用于彩色图像，去除异常值，并且生成特征描述符被聚类以实现有效的对象识别。 此外，本发明描述了一种用于从场景的图像中提取前景对象和正确拒绝背景的系统。 重要的是，本发明允许在训练和测试时间之间改变相机视点或照明。 该系统使用监督学习算法，并产生一组前景对象，识别算法随后可用于对象检测/识别。
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公开(公告)号：US08756183B1
公开(公告)日：2014-06-17
申请号：US13160336
申请日：2011-06-14
摘要： Described is a system for representing, storing, and reconstructing an input signal. The system constructs an index of unique polychronous groups (PCGs) from a spiking neuron network. Thereafter, a basis set of spike codes is generated from the unique PCGs. An input signal can then be received, with the input signal being spike encoded using the basis set of spike codes from the unique PCGs. The input signal can then be reconstructed by looking up in a reconstruction table, for each unique PCG in the basis set in temporal order according to firing times, anchor neurons. Using a neuron assignment table, an output location can be looked up for each anchor neuron to place a value based on the firing times of each unique PCG. Finally, the output locations of the anchor neurons can be compiled to reconstruct the input signal.
摘要翻译： 描述了用于表示，存储和重建输入信号的系统。 该系统构建了一个来自尖峰神经元网络的独特的多组同步组（PCG）的索引。 此后，从独特的PCG生成基准组尖峰码。 然后可以接收输入信号，其中输入信号使用来自唯一PCG的基准组尖峰码进行尖峰编码。 然后可以通过在重构表中查找输入信号，对于根据点火时间的时间顺序在基组中的每个唯一PCG来锚定神经元来重构。 使用神经元分配表，可以查找每个锚神经元的输出位置，以根据每个唯一PCG的点火时间放置一个值。 最后，可以编译锚神经元的输出位置，以重构输入信号。
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