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公开(公告)号：US08582887B2
公开(公告)日：2013-11-12
申请号：US11813404
申请日：2005-12-26
IPC分类号： G06K9/00
CPC分类号： G06K9/00288 , G06K9/6211 , G06K9/623 , G06T7/00
摘要： The present invention relates to an image processing system, a learning device and method, and a program which enable easy extraction of feature amounts to be used in a recognition process. Feature points are extracted from a learning-use model image, feature amounts are extracted based on the feature points, and the feature amounts are registered in a learning-use model dictionary registration section 23. Similarly, feature points are extracted from a learning-use input image containing a model object contained in the learning-use model image, feature amounts are extracted based on these feature points, and these feature amounts are compared with the feature amounts registered in a learning-use model registration section 23. A feature amount that has formed a pair the greatest number of times as a result of the comparison is registered in the model dictionary registration section 12 as the feature amount to be used in the recognition process. The present invention is applicable to a robot.
摘要翻译： 本发明涉及图像处理系统，学习装置和方法以及能够容易地提取在识别处理中使用的特征量的程序。 从学习用模型图像提取特征点，基于特征点提取特征量，并且将特征量登记在学习用模型字典注册部23中。同样，从学习用途中提取特征点 基于这些特征点提取含有包含在学习用模型图像中的模型对象的输入图像，并将这些特征量与在学习用模型登记部23中登记的特征量进行比较。特征量 作为比较的结果，在模型字典登记部12中登记了作为识别处理中使用的特征量的最大次数的对。 本发明可应用于机器人。
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公开(公告)号：US20050213818A1
公开(公告)日：2005-09-29
申请号：US10517615
申请日：2004-04-22
申请人： Hirotaka Suzuki , Kohtaro Sabe , Masahiro Fujita
发明人： Hirotaka Suzuki , Kohtaro Sabe , Masahiro Fujita
CPC分类号： G06K9/6212 , G06K9/4609 , G06K9/6211 , G06T7/73
摘要： In an image recognition apparatus (1), feature point extraction sections (10a) and (10b) extract feature points from a model image and an object image. Feature quantity retention sections (11a) and (11b) extract a feature quantity for each of the feature points and retain them along with positional information of the feature points. A feature quantity comparison section (12) compares the feature quantities with each other to calculate the similarity or the dissimilarity and generates a candidate-associated feature point pair having a high possibility of correspondence. A model attitude estimation section (13) repeats an operation of projecting an affine transformation parameter determined by three pairs randomly selected from the candidate-associated feature point pair group onto a parameter space. The model attitude estimation section (13) assumes each member in a cluster having the largest number of members formed in the parameter space to be an inlier. The model attitude estimation section (13) finds the affine transformation parameter according to the least squares estimation using the inlier and outputs a model attitude determined by this affine transformation parameter.
摘要翻译： 在图像识别装置（1）中，特征点提取部（10a）和（10b）从模型图像和对象图像中提取特征点。 特征量保留部分（11a）和（11b）提取每个特征点的特征量并将它们与特征点的位置信息一起保留。 特征量比较部分（12）将特征量彼此进行比较，以计算相似度或相似性，并且生成具有高对应可能性的候选关联特征点对。 模型姿态估计部（13）重复将从候选关联特征点对组中随机选择的三对决定的仿射变换参数投影到参数空间的动作。 模型姿态估计部（13）假设具有在参数空间中形成的最大数量的成员的簇中的每个成员是一个较早的。 模型姿态估计部（13）根据使用该误差的最小二乘估计求出仿射变换参数，并输出由该仿射变换参数确定的模型姿态。
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3.发明授权Image recognition device using feature points, method for recognizing images using feature points, and robot device which recognizes images using feature points 有权使用特征点的图像识别装置，使用特征点识别图像的方法，以及使用特征点识别图像的机器人装置公开(公告)号：US07627178B2
公开(公告)日：2009-12-01
申请号：US10517615
申请日：2004-04-22
申请人： Hirotaka Suzuki , Kohtaro Sabe , Masahiro Fujita
发明人： Hirotaka Suzuki , Kohtaro Sabe , Masahiro Fujita
IPC分类号： G06K9/46
CPC分类号： G06K9/6212 , G06K9/4609 , G06K9/6211 , G06T7/73
摘要： In an image recognition apparatus, feature point extraction sections and extract feature points from a model image and an object image. Feature quantity retention sections extract a feature quantity for each of the feature points and retain them along with positional information of the feature points. A feature quantity comparison section compares the feature quantities with each other to calculate the similarity or the dissimilarity and generates a candidate-associated feature point pair having a high possibility of correspondence. A model attitude estimation section repeats an operation of projecting an affine transformation parameter determined by three pairs randomly selected from the candidate-associated feature point pair group onto a parameter space. The model attitude estimation section assumes each member in a cluster having the largest number of members formed in the parameter space to be an inlier. The model attitude estimation section finds the affine transformation parameter according to the least squares estimation using the inlier and outputs a model attitude determined by this affine transformation parameter.
摘要翻译： 在图像识别装置中，特征点提取部分并从模型图像和对象图像中提取特征点。 特征量保留部分提取每个特征点的特征量，并将其与特征点的位置信息一起保留。 特征量比较部分将特征量彼此进行比较以计算相似度或相似性，并生成具有高对应可能性的候选相关特征点对。 模型姿态估计部重复将从候选关联特征点对组中随机选择的三对决定的仿射变换参数投影到参数空间的动作。 模型姿态估计部分假设在参数空间中形成的具有最大数量的成员的群组中的每个成员是一个较早的。 模型姿态估计部根据使用该误差的最小二乘估计求出仿射变换参数，并输出由该仿射变换参数确定的模型姿态。
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公开(公告)号：US20090041340A1
公开(公告)日：2009-02-12
申请号：US11813404
申请日：2005-12-26
IPC分类号： G06K9/46
CPC分类号： G06K9/00288 , G06K9/6211 , G06K9/623 , G06T7/00
摘要： The present invention relates to an image processing system, a learning device and method, and a program which enable easy extraction of feature amounts to be used in a recognition process. Feature points are extracted from a learning-use model image, feature amounts are extracted based on the feature points, and the feature amounts are registered in a learning-use model dictionary registration section 23. Similarly, feature points are extracted from a learning-use input image containing a model object contained in the learning-use model image, feature amounts are extracted based on these feature points, and these feature amounts are compared with the feature amounts registered in a learning-use model registration section 23. A feature amount that has formed a pair the greatest number of times as a result of the comparison is registered in the model dictionary registration section 12 as the feature amount to be used in the recognition process. The present invention is applicable to a robot.
摘要翻译： 本发明涉及图像处理系统，学习装置和方法以及能够容易地提取在识别处理中使用的特征量的程序。 从学习用模型图像提取特征点，基于特征点提取特征量，并且将特征量登记在学习用模型字典注册部23中。同样，从学习用途中提取特征点 基于这些特征点提取含有包含在学习用模型图像中的模型对象的输入图像，并将这些特征量与在学习用模型登记部23中登记的特征量进行比较。特征量 作为比较的结果，在模型字典登记部12中登记了作为识别处理中使用的特征量的最大次数的对。 本发明可应用于机器人。
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5.发明授权Information processing apparatus, information processing method, and computer program 有权信息处理装置，信息处理方法和计算机程序公开(公告)号：US08290885B2
公开(公告)日：2012-10-16
申请号：US12381499
申请日：2009-03-12
CPC分类号： G05B19/045 , G05B13/0265 , G05B2219/23288 , G06N99/005
摘要： An information processing apparatus includes: model learning means for self-organizing, on the basis of a state transition model having a state and state transition to be learned by using time series data as data in time series, an internal state from an observation signal obtained by a sensor; and controller learning means for performing learning for allocating a controller, which outputs an action, to each of transitions of a state or each of transition destination states in the state transition model indicating the internal state self-organized by the model learning means.
摘要翻译： 信息处理装置包括：用于自组织的模型学习装置，基于具有通过使用时间序列数据作为时间序列数据而被学习的状态和状态转变的状态转换模型，从获得的观察信号获得的内部状态 通过传感器; 以及控制器学习装置，用于在指示由模型学习装置自组织的内部状态的状态转换模型中执行学习以分配向控制器输出动作的状态或转换目的地状态中的每一个的转换。
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6.发明申请Information processing apparatus, information processing method, and computer program 有权信息处理装置，信息处理方法和计算机程序公开(公告)号：US20090234467A1
公开(公告)日：2009-09-17
申请号：US12381499
申请日：2009-03-12
CPC分类号： G05B19/045 , G05B13/0265 , G05B2219/23288 , G06N99/005
摘要： An information processing apparatus includes: model learning means for self-organizing, on the basis of a state transition model having a state and state transition to be learned by using time series data as data in time series, an internal state from an observation signal obtained by a sensor; and controller learning means for performing learning for allocating a controller, which outputs an action, to each of transitions of a state or each of transition destination states in the state transition model indicating the internal state self-organized by the model learning means.
摘要翻译： 信息处理装置包括：用于自组织的模型学习装置，基于具有通过使用时间序列数据作为时间序列数据而被学习的状态和状态转变的状态转换模型，从获得的观察信号获得的内部状态 通过传感器; 以及控制器学习装置，用于在指示由模型学习装置自组织的内部状态的状态转换模型中执行学习以分配向控制器输出动作的状态或转换目的地状态中的每一个的转换。
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7.发明授权公开(公告)号：US08494984B2
公开(公告)日：2013-07-23
申请号：US12954381
申请日：2010-11-24
申请人： Masato Ito , Hirotaka Suzuki , Naoki Ide , Kohtaro Sabe
发明人： Masato Ito , Hirotaka Suzuki , Naoki Ide , Kohtaro Sabe
IPC分类号： G06F15/18
CPC分类号： G06K9/6297 , G06K9/00335 , H04N21/466 , H04N21/4663
摘要： An information processing device includes an acquisition unit acquiring a viewing log including information representing content of an operation for viewing content and time of the operation, a learning unit learning, based on the viewing log acquired by the acquisition unit, a viewing behavior model which is a stochastic state transition model representing a viewing behavior of a user, a recognition unit recognizing, using the viewing behavior model obtained through learning by the learning unit, a current viewing state of the user, a prediction unit predicting, using the viewing behavior model, the viewing behavior of the user after a predetermined period of time with the current viewing state of the user recognized by the recognition unit as a starting point, and a display control unit displaying information relating to content predicted to be viewed through the viewing behavior predicted by the prediction unit.
摘要翻译： 一种信息处理设备，包括：获取单元，其获取包括表示用于观看内容和操作时间的操作的内容的信息的观看日志;基于由所述获取单元获取的观看日志的学习单元学习观看行为模型，所述观看行为模型是 表示用户的观看行为的随机状态转换模型，识别单元，使用通过学习单元的学习获得的观看行为模型识别用户的当前观看状态，预测单元，使用观看行为模型， 在由识别单元识别的用户的当前观看状态作为起点的预定时间段之后的用户的观看行为，以及显示控制单元，显示与通过由 预测单元。
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8.发明申请公开(公告)号：US20120057775A1
公开(公告)日：2012-03-08
申请号：US13076744
申请日：2011-03-31
申请人： Hirotaka Suzuki , Masato Ito , Kohtaro Sabe
发明人： Hirotaka Suzuki , Masato Ito , Kohtaro Sabe
CPC分类号： H04N5/76 , G06K9/00711 , H04N5/775 , H04N5/781 , H04N5/783 , H04N9/8211
摘要： An information processing device includes a feature amount extracting unit configured to extract the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene; a clustering unit configured to use cluster information that is the information of the cluster obtained by performing cluster learning; a highlight label generating unit configured to generate a highlight label sequence; and a highlight detector learning unit configured to perform learning of the highlight detector.
摘要翻译： 信息处理装置包括特征量提取单元，其被配置为提取作为用于学习高亮检测器的内容的感兴趣的用于检测器学习的内容的图像的每个帧的特征量，该高亮检测器是用于检测 用户感兴趣的场景作为高亮场景; 聚类单元，被配置为使用作为通过进行群集学习而获得的群集的信息的群集信息; 高亮标签生成单元，被配置为生成高亮标签序列; 以及高亮度检测器学习单元，被配置为执行高光检测器的学习。
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9.发明授权Apparatus, method, and program for predicting user activity state through data processing 有权用于通过数据处理预测用户活动状态的装置，方法和程序公开(公告)号：US08560467B2
公开(公告)日：2013-10-15
申请号：US12839321
申请日：2010-07-19
申请人： Masato Ito , Kohtaro Sabe , Hirotaka Suzuki , Jun Yokono , Kazumi Aoyama , Takashi Hasuo
发明人： Masato Ito , Kohtaro Sabe , Hirotaka Suzuki , Jun Yokono , Kazumi Aoyama , Takashi Hasuo
IPC分类号： G06F15/18
CPC分类号： G06K9/00335 , G06K9/00664 , G06K9/00778
摘要： A data processing apparatus includes an obtaining unit for obtaining time-series data, an activity model learning unit for learning an activity model representing a user activity state as a stochastic state transition model from the obtained time-series data, a recognition unit for recognizing a current user activity state by using the learned activity model, and a prediction unit for predicting a user activity state after a predetermined time elapses from a current time from the recognized current user activity state, wherein the prediction unit predicts the user activity state as an occurrence probability, and calculates the occurrence probabilities of the respective states on the basis of the state transition probability of the stochastic state transition model to predict the user activity state, while it is presumed that observation probabilities of the respective states at the respective times of the stochastic state transition model are an equal probability.
摘要翻译： 数据处理装置包括：获取单元，用于获取时间序列数据;活动模型学习单元，用于从所获得的时间序列数据中学习表示用户活动状态的活动模型作为随机状态转换模型;识别单元，用于识别 通过使用所学习的活动模型的当前用户活动状态，以及预测单元，用于在从所识别的当前用户活动状态起从当前时间经过预定时间之后预测用户活动状态，其中，所述预测单元将所述用户活动状态预测为发生 概率，并且基于随机状态转换模型的状态转移概率来计算各个状态的发生概率以预测用户活动状态，同时假设在随机的各个时间的各个状态的观察概率 状态转换模型是相等的概率。
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10.发明申请公开(公告)号：US20110137835A1
公开(公告)日：2011-06-09
申请号：US12954381
申请日：2010-11-24
申请人： Masato ITO , Hirotaka Suzuki , Naoki Ide , Kohtaro Sabe
发明人： Masato ITO , Hirotaka Suzuki , Naoki Ide , Kohtaro Sabe
IPC分类号： G06F15/18
CPC分类号： G06K9/6297 , G06K9/00335 , H04N21/466 , H04N21/4663
摘要： An information processing device includes an acquisition unit acquiring a viewing log including information representing content of an operation for viewing content and time of the operation, a learning unit learning, based on the viewing log acquired by the acquisition unit, a viewing behavior model which is a stochastic state transition model representing a viewing behavior of a user, a recognition unit recognizing, using the viewing behavior model obtained through learning by the learning unit, a current viewing state of the user, a prediction unit predicting, using the viewing behavior model, the viewing behavior of the user after a predetermined period of time with the current viewing state of the user recognized by the recognition unit as a starting point, and a display control unit displaying information relating to content predicted to be viewed through the viewing behavior predicted by the prediction unit.
摘要翻译： 一种信息处理设备，包括：获取单元，其获取包括表示用于观看内容和操作时间的操作的内容的信息的观看日志;基于由所述获取单元获取的观看日志的学习单元学习观看行为模型，所述观看行为模型是 表示用户的观看行为的随机状态转换模型，识别单元，使用通过学习单元的学习获得的观看行为模型识别用户的当前观看状态，预测单元，使用观看行为模型， 在由识别单元识别的用户的当前观看状态作为起点的预定时间段之后的用户的观看行为，以及显示控制单元，显示与通过由 预测单元。
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