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：US07783086B2

公开(公告)日：2010-08-24

申请号：US12366792

申请日：2009-02-06

申请人： Kohtaro Sabe ,  Kenichi Hidai

发明人： Kohtaro Sabe ,  Kenichi Hidai

IPC分类号： G06K9/00 ,  G06K9/62

CPC分类号： G06K9/6256 ,  G06K9/00248 ,  G06K9/6282

摘要： In an information processing apparatus, such as a robot that discriminates human faces, nodes are hierarchically arranged in a tree structure. Each of the nodes has a number of weak classifiers. Each terminal node learns face images associated with one label. An upper node learns learning samples of all labels learned by lower nodes. When a window image to be classified is input, discrimination is performed sequentially from upper nodes to lower nodes. When it is determined that the window image does not correspond to a human face, discrimination by lower nodes is not performed, and discrimination proceeds to sibling nodes.

摘要翻译： 在诸如识别人脸的机器人等信息处理装置中，以树结构分层地布置节点。 每个节点都有一些弱分类器。 每个终端节点学习与一个标签相关联的脸部图像。 上级节点学习下层节点学习的所有标签的学习样本。 当输入要分类的窗口图像时，从上层节点到下层节点依次进行鉴别。 当确定窗口图像不对应于人脸时，不执行下位节点的鉴别，并且歧视进行到兄弟节点。

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：US20090232363A1

公开(公告)日：2009-09-17

申请号：US12369241

申请日：2009-02-11

申请人： Takeshi OHASHI ,  Kohtaro Sabe ,  Kenichi Hidai

发明人： Takeshi OHASHI ,  Kohtaro Sabe ,  Kenichi Hidai

IPC分类号： G06K9/00

CPC分类号： G06K9/00281

摘要： An information processing apparatus includes: face detecting means for detecting the orientation of a face in a face image; weight distribution generating means for generating a weight distribution based on a statistical distribution of the position of a predetermined feature of the face in the face image according to the orientation of the face; first calculation means for calculating a first evaluation value for evaluating each of predetermined regions of the face image to determine whether the region is the predetermined feature of the face; and face feature identifying means for identifying the predetermined region as the predetermined feature of the face based on the first evaluation value and the weight distribution.

摘要翻译： 信息处理装置包括：面部检测装置，用于检测面部图像中的脸部的取向; 权重分布生成单元，根据面部的取向，根据脸部的脸部的规定特征的位置的统计分布生成体重分布; 第一计算装置，用于计算用于评估面部图像的每个预定区域的第一评估值，以确定该区域是否是面部的预定特征; 以及面部特征识别装置，用于基于第一评估值和重量分布，将预定区域识别为面部的预定特征。

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：US20090138418A1

公开(公告)日：2009-05-28

申请号：US12357917

申请日：2009-01-22

申请人： Kenichi HIDAI ,  Kohtaro Sabe

发明人： Kenichi HIDAI ,  Kohtaro Sabe

IPC分类号： G06F15/18

CPC分类号： G05B13/0265

摘要： A learning control apparatus for an autonomous agent including a functional module having a function of multiple inputs and multiple outputs, the function receiving at least one variable and outputting at least one value, includes an estimating unit for estimating a causal relationship of at least one variable, a grouping unit for grouping at least one variable into a variable group in accordance with the estimated causal relationship, a determining for determining a behavior variable corresponding to each of the variable groups, and a layering unit for layering, in accordance with the variable group and the behavior variable, the function corresponding to each variable group, the function receiving the variable grouped into the variable group and outputting the behavior variable.

摘要翻译： 一种用于包括具有多个输入和多个输出的功能的功能模块的自主代理的学习控制装置，所述功能接收至少一个变量并输出至少一个值，所述学习控制装置包括：估计单元，用于估计至少一个变量 ，分组单元，用于根据估计的因果关系将至少一个变量分组成变量组，确定用于确定与每个可变组相对应的行为变量，以及分层单元，用于根据变量组 和行为变量，对应于每个变量组的函数，接收变量的函数分组到变量组中并输出行为变量。

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：US20070122012A1

公开(公告)日：2007-05-31

申请号：US11656115

申请日：2007-01-22

申请人： Atsushi Okubo ,  Kohtaro Sabe ,  Kenta Kawamoto ,  Masaki Fukuchi

发明人： Atsushi Okubo ,  Kohtaro Sabe ,  Kenta Kawamoto ,  Masaki Fukuchi

IPC分类号： G06K9/00

CPC分类号： G06K9/00228 ,  G06K9/00221 ,  G06T7/246 ,  G06T7/90

摘要： A robot apparatus includes face includes a face tracking module (M2) for tracking a face in an image photographed by a CCD camera, a face detecting module (M1) for detecting face data of the face in the image photographed by the image pickup device, based on the face tracking information by the face tracking module (M2) and a face identification module (M3) for identifying a specified face based on the face data as detected by the face data detecting module (M1).

摘要翻译： 一种机器人装置，包括面部跟踪模块（M 2），用于跟踪由CCD照相机拍摄的图像中的脸部，面部检测模块（M 1），用于检测由图像拾取器拍摄的图像中的脸部的脸部数据 基于面部追踪模块（M 2）的脸部跟踪信息和面部识别模块（M 3），根据面部数据检测模块（M1）检测到的面部数据，识别指定脸部的装置。