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1. 发明授权

US08527434B2 Information processing device, information processing method, and program 有权
标题翻译：信息处理装置，信息处理方法和程序
公开(公告)号：US08527434B2
公开(公告)日：2013-09-03
申请号：US12791275
申请日：2010-06-01
申请人： Kuniaki Noda , Kohtaro Sabe , Yukiko Yoshiike , Kenta Kawamoto , Kenichi Hidai
发明人： Kuniaki Noda , Kohtaro Sabe , Yukiko Yoshiike , Kenta Kawamoto , Kenichi Hidai
IPC分类号： G06N5/00
CPC分类号： G06N3/006 , G06K9/00335 , G06K9/00664 , G06K9/6297
摘要： An information processing device includes: a learning section configured to learn a state transition probability model defined by state transition probability for each action of a state making a state transition due to an action performed by an agent capable of performing action and observation probability of a predetermined observed value being observed from the state, using an action performed by the agent and an observed value observed in the agent when the agent has performed the action.
摘要翻译：一种信息处理装置，包括：学习部，其被配置为：由于能够执行动作的代理和所述预定的行为的观察概率而进行的状态转换的状态的每个动作，学习由状态转移概率定义的状态转移概率模型观察到的值是从状态观察到的，使用由试剂进行的作用和在试剂进行作用时在试剂中观察到的观察值。

2. 发明申请

US20100318479A1 INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM 有权
标题翻译：信息处理设备，信息处理方法和程序
公开(公告)号：US20100318479A1
公开(公告)日：2010-12-16
申请号：US12791275
申请日：2010-06-01
申请人： Kuniaki NODA , Kohtaro Sabe , Yukiko Yoshiike , Kenta Kawamoto , Kenichi Hidai
发明人： Kuniaki NODA , Kohtaro Sabe , Yukiko Yoshiike , Kenta Kawamoto , Kenichi Hidai
IPC分类号： G06F15/18 , G06N5/02
CPC分类号： G06N3/006 , G06K9/00335 , G06K9/00664 , G06K9/6297
摘要： An information processing device includes: a learning section configured to learn a state transition probability model defined by state transition probability for each action of a state making a state transition due to an action performed by an agent capable of performing action and observation probability of a predetermined observed value being observed from the state, using an action performed by the agent and an observed value observed in the agent when the agent has performed the action.
摘要翻译：一种信息处理装置，包括：学习部，其被配置为：由于能够执行动作的代理以及预定的行为的观察概率，进行状态转换的状态的状态转移概率而定义的状态转移概率模型观察到的值是从状态观察到的，使用由试剂进行的作用和在试剂进行作用时在试剂中观察到的观察值。

3. 发明授权

US08725510B2 HMM learning device and method, program, and recording medium 有权
标题翻译： HMM学习装置和方法，程序和记录介质
公开(公告)号：US08725510B2
公开(公告)日：2014-05-13
申请号：US12829984
申请日：2010-07-02
申请人： Yukiko Yoshiike , Kenta Kawamoto , Kuniaki Noda , Kohtaro Sabe
发明人： Yukiko Yoshiike , Kenta Kawamoto , Kuniaki Noda , Kohtaro Sabe
IPC分类号： G10L15/14
CPC分类号： G06N99/005
摘要： An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability based on the frequency variables.
摘要翻译： HMM（隐马尔可夫模型）学习装置包括：用于学习作为代理可以执行的动作的函数的状态转移概率的学习单元，基于代理已经执行的动作执行的HMM的学习以及作出的时间序列信息观察信号; 以及存储单元，用于将所述学习单元的学习结果存储为包括状态转换概率表和观察概率表的内部模型数据; 学习单元计算用于HMM状态转换和HMM观察概率的估计计算的频率变量; 存储单元保持状态转移概率表中分别对应于状态转换概率和每个观察概率的频率变量; 并且所述学习单元使用由所述存储单元保持的频率变量来执行学习，并且基于所述频率变量来估计所述状态转换概率和观察概率。

4. 发明申请

US20110010176A1 HMM LEARNING DEVICE AND METHOD, PROGRAM, AND RECORDING MEDIUM 有权
标题翻译： HMM学习设备和方法，程序和记录介质
公开(公告)号：US20110010176A1
公开(公告)日：2011-01-13
申请号：US12829984
申请日：2010-07-02
申请人： Yukiko YOSHIIKE , Kenta Kawamoto , Kuniaki Noda , Kohtaro Sabe
发明人： Yukiko YOSHIIKE , Kenta Kawamoto , Kuniaki Noda , Kohtaro Sabe
IPC分类号： G10L15/14
CPC分类号： G06N99/005
摘要： An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability based on the frequency variables.
摘要翻译： HMM（隐马尔可夫模型）学习装置包括：用于学习作为代理可以执行的动作的函数的状态转移概率的学习单元，基于代理已经执行的动作执行的HMM的学习以及作出的时间序列信息观察信号; 以及存储单元，用于将所述学习单元的学习结果存储为包括状态转换概率表和观察概率表的内部模型数据; 学习单元计算用于HMM状态转换和HMM观察概率的估计计算的频率变量; 存储单元保持状态转移概率表中分别对应于状态转换概率和每个观察概率的频率变量; 并且所述学习单元使用由所述存储单元保持的频率变量来执行学习，并且基于所述频率变量来估计所述状态转换概率和观察概率。

5. 发明申请

US20100318478A1 INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM 审中-公开
标题翻译：信息处理设备，信息处理方法和程序
公开(公告)号：US20100318478A1
公开(公告)日：2010-12-16
申请号：US12791240
申请日：2010-06-01
申请人： Yukiko Yoshiike , Kenta Kawamoto , Kuniaki Noda , Kohtaro Sabe
发明人： Yukiko Yoshiike , Kenta Kawamoto , Kuniaki Noda , Kohtaro Sabe
IPC分类号： G06N5/02 , G06F15/18
CPC分类号： G06N3/006 , G06K9/00335 , G06K9/00664 , G06K9/6297
摘要： An information processing device includes: a calculating unit configured to calculate a current-state series candidate that is a state series for an agent capable of actions reaching the current state, based on a state transition probability model obtained by performing learning of the state transition probability model stipulated by a state transition probability that a state will be transitioned according to each of actions performed by an agent capable of actions, and an observation probability that a predetermined observation value will be observed from the state, using an action performed by the agent, and an observation value observed at the agent when the agent performs an action; and a determining unit configured to determine an action to be performed next by the agent using the current-state series candidate in accordance with a predetermined strategy.
摘要翻译：一种信息处理设备，包括：计算单元，被配置为基于通过执行状态转移概率的学习获得的状态转移概率模型来计算作为达到当前状态的动作的代理的状态序列的当前状态序列候选由状态转移概率规定的模式，其状态将根据由能够执行动作的代理执行的动作而转变，并且使用由代理执行的动作从该状态观察到预定观察值的观察概率，以及当代理人执行动作时在代理处观察到的观察值; 以及确定单元，被配置为根据预定策略来确定所述代理使用所述当前状态序列候选来执行的动作。

6. 发明授权

US08738555B2 Data processing device, data processing method, and program 有权
标题翻译：数据处理装置，数据处理方法和程序
公开(公告)号：US08738555B2
公开(公告)日：2014-05-27
申请号：US13248296
申请日：2011-09-29
申请人： Takashi Hasuo , Kohtaro Sabe , Kenta Kawamoto , Yukiko Yoshiike
发明人： Takashi Hasuo , Kohtaro Sabe , Kenta Kawamoto , Yukiko Yoshiike
IPC分类号： G06F15/18
CPC分类号： G06N3/006 , G06N99/005
摘要： A data processing device includes a state value calculation unit which calculates a state value of which the value increases as much as a state with a high transition probability for each state of the state transition model, an action value calculation unit which calculates an action value, of which the value increases as a transition probability increases for each state of the state transition model and each action that the agent can perform, a target state setting unit which sets a state with great unevenness in the action value among states of the state transition model to a target state that is the target to reach by action performed by the agent, and an action selection unit which selects an action of the agent so as to move toward the target state.
摘要翻译：数据处理装置包括状态值计算单元，其计算与状态转移模型的每个状态的转移概率高的状态一起增加的状态值，动作值计算单元，其计算动作值，其中该值随状态转换模型的每个状态和代理可以执行的每个动作的转移概率增加而增加;目标状态设置单元，其在状态转换模型的状态之间设置动作值中具有很大不均匀性的状态到作为由代理执行的动作达到的目标的目标状态;以及动作选择单元，其选择代理人的动作以朝向目标状态移动。

7. 再颁专利

USRE43873E1 Device and method for detecting object and device and method for group learning 有权
标题翻译：用于组学习的物体和装置的检测装置及方法
公开(公告)号：USRE43873E1
公开(公告)日：2012-12-25
申请号：US13208123
申请日：2011-08-11
申请人： Kenichi Hidai , Kohtaro Sabe , Kenta Kawamoto
发明人： Kenichi Hidai , Kohtaro Sabe , Kenta Kawamoto
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/6282 , G06K9/00248 , G06K9/6256
摘要： An object detecting device for detecting an object in a given gradation image. A scaling section generates scaled images by scaling down a gradation image input from an image output section. A scanning section sequentially manipulates the scaled images and cutting out window images from them and a discriminator judges if each window image is an object or not. The discriminator includes a plurality of weak discriminators that are learned in a group by boosting and an adder for making a weighted majority decision from the outputs of the weak discriminators. Each of the weak discriminators outputs an estimate of the likelihood of a window image to be an object or not by using the difference of the luminance values between two pixels. The discriminator suspends the operation of computing estimates for a window image that is judged to be a non-object, using a threshold value that is learned in advance.
摘要翻译：一种用于检测给定灰度图像中的物体的物体检测装置。缩放部分通过缩小从图像输出部分输入的灰度图像来生成缩放图像。扫描部分顺序地操纵缩放图像并从中切出窗口图像，并且鉴别器判断每个窗口图像是否是对象。鉴别器包括通过升压在一组中学习的多个弱识别器和用于从弱识别器的输出进行加权多数决定的加法器。每个弱识别器通过使用两个像素之间的亮度值的差异来输出窗口图像成为对象的可能性的估计。鉴别器使用预先学习的阈值暂停对被判断为非对象的窗口图像的计算估计的操作。

8. 发明授权

US08290885B2 Information processing apparatus, information processing method, and computer program 有权
标题翻译：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
申请人： Kohtaro Sabe , Katsuki Minamino , Kenta Kawamoto , Hirotaka Suzuki , Kenichi Hidai
发明人： Kohtaro Sabe , Katsuki Minamino , Kenta Kawamoto , Hirotaka Suzuki , Kenichi Hidai
IPC分类号： G06F17/00 , G06N5/00
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。

9. 发明申请

US20050280809A1 Device and method for detecting object and device and method for group learning 有权
标题翻译：用于组学习的物体和装置的检测装置及方法
公开(公告)号：US20050280809A1
公开(公告)日：2005-12-22
申请号：US10994942
申请日：2004-11-22
申请人： Kenichi Hidai , Kohtaro Sabe , Kenta Kawamoto
发明人： Kenichi Hidai , Kohtaro Sabe , Kenta Kawamoto
IPC分类号： G06T1/00 , G06K9/00 , G06K9/62 , G06K9/68 , G06N3/08 , G06T7/00 , G01N21/00
CPC分类号： G06K9/6282 , G06K9/00248 , G06K9/6256
摘要： An object detecting device 1 comprises a scaling section 3 for generating scaled images by scaling down a gradation image input from an image output section 2, a scanning section 4 for sequentially manipulating the scaled images and cutting out window images from them and a discriminator 5 for judging if each window image is an object or not. The discriminator 5 includes a plurality of weak discriminators that are learnt in a group by boosting and an adder for making a weighted majority decision from the outputs of the weak discriminators. Each of the weak discriminators outputs an estimate telling the likelihood of a window image to be an object or not by using the difference of the luminance values of two pixels. The discriminator 5 suspends the operation of computing estimates for a window image that is judged to be a non-object, using a threshold value that is learnt in advance.
摘要翻译：对象检测装置1包括：缩放部分3，用于通过缩小从图像输出部分2输入的灰度图像来生成缩放图像;扫描部分4，用于顺序地操纵缩放图像并从中切出窗口图像;以及鉴别器5，判断每个窗口图像是否为一个对象。鉴别器5包括通过升压在一组中学习的多个弱识别器和用于从弱识别器的输出进行加权多数决定的加法器。每个弱识别器通过使用两个像素的亮度值的差异来输出估计窗口图像成为对象的可能性的估计。鉴别器5使用预先学习的阈值来暂停对被判断为非对象的窗口图像的计算估计的操作。

10. 发明授权

US07624076B2 Weak hypothesis generation apparatus and method, learning apparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial expression recognition apparatus and method, and robot apparatus 有权
标题翻译：弱假设产生装置和方法，学习装置和方法，检测装置和方法，面部表情学习装置和方法，面部表情识别装置和方法以及机器人装置
公开(公告)号：US07624076B2
公开(公告)日：2009-11-24
申请号：US12074931
申请日：2008-03-07
申请人： Javier R. Movellan , Marian S. Bartlett , Gwendolen C. Littlewort , John Hershey , Ian R. Fasel , Eric C. Carlson , Josh Susskind , Kohtaro Sabe , Kenta Kawamoto , Kenichi Hidai
发明人： Javier R. Movellan , Marian S. Bartlett , Gwendolen C. Littlewort , John Hershey , Ian R. Fasel , Eric C. Carlson , Josh Susskind , Kohtaro Sabe , Kenta Kawamoto , Kenichi Hidai
IPC分类号： G06N5/00
CPC分类号： G06K9/00248 , G06K9/00308 , G06K9/6256 , G06N3/004
摘要： A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired. In detection, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition.
摘要翻译：一种面部表情识别系统，其使用面部检测装置，当检测到表示检测对象的区域时，基于整体学习实现有效的学习和高速检测处理，并且对于包括在图像中的面部位置的移动是鲁棒的，并且能够高度准确地表达识别和系统的学习方法。当通过Adaboost的面部检测装置学习数据时，从所有弱假设中选择高性能弱假设的处理，然后根据统计特征从这些高性能弱假设产生新的弱假设，并选择一个弱重复具有这些弱假设的最高判别性能的假设，以依次产生弱假设，从而获得最终假设。在检测中，使用预先学习的中止阈值，每当一个弱假设输出鉴别结果时，确定提供的数据是否可以被明确地判断为非面。如果可以判断，则处理中止。通过Adaboost技术从检测到的脸部图像中选择预定的Gabor滤波器，并且仅学习由所选择的滤波器提取的特征量的支持向量，从而执行表达式识别。

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