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。

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。

公开(公告)号：US20050102246A1

公开(公告)日：2005-05-12

申请号：US10871494

申请日：2004-06-17

申请人： Javier Movellan ,  Marian Bartlett ,  Gwendolen Littlewort ,  John Hershey ,  Ian Fasel ,  Eric Carlson ,  Josh Susskind ,  Kohtaro Sabe ,  Kenta Kawamoto ,  Kenichi Hidai

发明人： Javier Movellan ,  Marian Bartlett ,  Gwendolen Littlewort ,  John Hershey ,  Ian Fasel ,  Eric Carlson ,  Josh Susskind ,  Kohtaro Sabe ,  Kenta Kawamoto ,  Kenichi Hidai

IPC分类号： G06T1/00 ,  G06K9/00 ,  G06K9/62 ,  G06N3/00 ,  G06T7/00 ,  G06F19/00 ,  G06F15/18 ,  G06N3/08

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：US09104980B2

公开(公告)日：2015-08-11

申请号：US13429130

申请日：2012-03-23

申请人： Kuniaki Noda ,  Takashi Hasuo ,  Kenta Kawamoto ,  Kohtaro Sabe

发明人： Kuniaki Noda ,  Takashi Hasuo ,  Kenta Kawamoto ,  Kohtaro Sabe

IPC分类号： G06K9/00 ,  G06N99/00

CPC分类号： G06N99/005

摘要： An information processing device includes a learning unit that performs, using an action performed by an object and an observation value of an image as learning data, learning of a separation learning model that includes a background model that is a model of the background of the image and one or more foreground model(s) that is a model of a foreground of the image, which can move on the background, in which the background model includes a background appearance model indicating the appearance of the background, and at least one among the one or more foreground model(s) includes a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action, and a foreground appearance model indicating the appearance of the foreground.

摘要翻译： 信息处理装置包括：学习单元，其使用由对象执行的动作和图像的观察值作为学习数据;学习包括作为图像的背景的模型的背景模型的分离学习模型; 以及一个或多个前景模型，其是可以在背景上移动的图像的前景的模型，其中背景模型包括指示背景的外观的背景外观模型，以及至少一个背景模型 一个或多个前景模型包括转移概率，对于每个动作，通过由对应于前景的对象执行的动作来转换对应于背景上的前景的位置的状态，以及前景外观模型 指示前景的外观。