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。

公开(公告)号：US20110288835A1

公开(公告)日：2011-11-24

申请号：US13106071

申请日：2011-05-12

申请人： Takashi Hasuo ,  Kenta Kawamoto

发明人： Takashi Hasuo ,  Kenta Kawamoto

IPC分类号： G06F17/10

CPC分类号： G06K9/6297

摘要： A data processing device includes a parameter estimation unit and a structure adjustment unit. The structure adjustment unit notes each state of an HMM as a noted state, obtains, for the noted state, a value corresponding to an eigen value difference which is a difference between a partial eigen value sum and a total eigen value sum, as a target degree value indicating a degree for selecting the noted state as a division target or a mergence target, selects a state having the target degree value larger than a division threshold value, as a division target, and selects a state having the target degree value smaller than a mergence threshold value, as a mergence target.

摘要翻译： 数据处理装置包括参数估计单元和结构调整单元。 结构调整单元将HMM的每个状态表示为所述状态，对于所述状态，获得与作为目标的部分特征值和和总本征值和之间的差的特征值差对应的值 指示用于将所述状态选择为分割目标或合并目标的程度的度值选择具有大于分割阈值的目标度值的状态作为分割目标，并且选择目标度值小于 合并阈值，作为合并目标。

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。

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。

公开(公告)号：US20090234467A1

公开(公告)日：2009-09-17

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： G05B13/02 ,  G06F17/10 ,  G06F15/18

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。

公开(公告)号：US07574037B2

公开(公告)日：2009-08-11

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： 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。

公开(公告)号：US20080247598A1

公开(公告)日：2008-10-09

申请号：US12075080

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： G06K9/00

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.

公开(公告)号：US07388879B2

公开(公告)日：2008-06-17

申请号：US10111564

申请日：2001-08-28

申请人： Kohtaro Sabe ,  Kenta Kawamoto ,  Gabriel Costa

发明人： Kohtaro Sabe ,  Kenta Kawamoto ,  Gabriel Costa

IPC分类号： H04L12/28 ,  H04Q7/00 ,  G06F15/16

CPC分类号： H04L67/10 ,  A63H2200/00 ,  G06F19/00 ,  H04L69/329

摘要： A gateway object (48) for transmitting and receiving data to and from an object of a robot apparatus (1) is allocated to a radio LAN PC card (41) of the robot apparatus (1), and a gateway object (52) for transmitting and receiving data to and from an object on a personal computer (32) is allocated to a network adapter (31) of a remote system (30). When the radio LAN PC card (41) and the network adapter (31) are connected with each other by radio or wired connection, inter-object communication is carried out between the gateway object (48) of the radio LAN PC card (41) and the gateway object (52) of the network adapter (31), thereby carrying out inter-object communication between the object of the robot apparatus (1) and the object of the personal computer (32). Thus, preparation of a program is facilitated.

摘要翻译： 向机器人装置（1）的对象发送和接收数据的网关对象（48）分配给机器人装置（1）的无线LAN PC卡（41），网关对象（52） 向个人计算机（32）上的对象发送和接收数据被分配给远程系统（30）的网络适配器（31）。 无线LAN PC卡（41）和网络适配器（31）通过无线或有线连接相互连接时，在无线LAN PC卡（41）的网关对象（48）之间进行对象间通信， 和网络适配器（31）的网关对象（52），由此执行机器人装置（1）的对象与个人计算机（32）的对象之间的对象间通信。 因此，方便了程序的准备。

公开(公告)号：US07379568B2

公开(公告)日：2008-05-27

申请号：US10871494

申请日：2004-06-17

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： G06K9/00

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。

公开(公告)号：US20070239635A1

公开(公告)日：2007-10-11

申请号：US11732807

申请日：2007-04-04

申请人： Yukiko Yoshiike ,  Masato Ito ,  Katsuki Minamino ,  Hirotaka Suzuki ,  Kenta Kawamoto

发明人： Yukiko Yoshiike ,  Masato Ito ,  Katsuki Minamino ,  Hirotaka Suzuki ,  Kenta Kawamoto

IPC分类号： G06N3/02

CPC分类号： G06N3/02

摘要： An information processing apparatus includes a storage unit configured to store a node holding dynamics; an input-weight-coefficient adjuster configured to adjust input-weight coefficients on a dimension-by-dimension basis, the input-weight coefficients being weight coefficients for individual dimensions of input data input to input units of the node, the input data being observed time-series data having a plurality of dimensions; and an output-weight-coefficient adjuster configured to adjust output-weight coefficients on a dimension-by-dimension basis, the output-weight coefficients being weight coefficients for individual dimensions of output data having a plurality of dimensions and output from output units of the node.

摘要翻译： 一种信息处理装置，包括：存储单元，被配置为存储保存动态的节点; 输入权重系数调整器，其被配置为在逐个维度的基础上调整输入权重系数，输入权重系数是输入到节点的输入单元的输入数据的各个维度的权重系数，观察到的输入数据 具有多个维度的时间序列数据; 以及输出权重系数调整器，其被配置为在逐个维度的基础上调整输出权重系数，所述输出权重系数是具有多个维度的输出数据的各个维度的权重系数，并且从所述输出单元的输出单元输出 节点。