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11. 发明申请

US20080247598A1 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
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.

12. 发明授权

US07379568B2 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：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。

13. 发明申请

US20050102246A1 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：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。

14. 发明授权

US08571315B2 Information processing apparatus, 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：US08571315B2
公开(公告)日：2013-10-29
申请号：US13288231
申请日：2011-11-03
申请人： Kohtaro Sabe , Kenichi Hidai , Kiyoto Ichikawa
发明人： Kohtaro Sabe , Kenichi Hidai , Kiyoto Ichikawa
IPC分类号： G06K9/00 , G06K9/38 , G06K9/62 , G06K9/68
CPC分类号： G06K9/4614 , G06K9/6256
摘要： An information processing apparatus includes: a distinguishing unit which, by using an ensemble classifier, which includes a plurality of weak classifiers outputting weak hypotheses which indicates whether a predetermined subject is shown in an image in response to inputs of a plurality of features extracted from the image, and a plurality of features extracted from an input image, sequentially integrates the weak hypotheses output by the weak classifiers in regard to the plurality of features and distinguishes whether the predetermined subject is shown in the input image based on the integrated value. The weak classifier classifies each of the plurality of features to one of three or more sub-divisions based on threshold values, calculates sum divisions of the sub-divisions of the plurality of features as whole divisions into which the plurality of features is classified, and outputs, as the weak hypothesis, a reliability degree of the whole divisions.
摘要翻译：一种信息处理装置，包括：识别单元，其使用整体分类器，其包括输出弱假设的多个弱分类器，所述弱分类器指示响应于从所述图像提取的多个特征的输入，是否在图像中示出预定对象图像和从输入图像提取的多个特征，顺序地对由弱分类器输出的关于多个特征的弱假设进行积分，并且基于积分值区分输入图像中是否显示预定对象。所述弱分类器基于阈值将所述多个特征中的每一个分类为三个或更多个子分割中的一个，并且将所述多个特征的子分割的和除作为将所述多个特征分类成的整个分割，以及作为弱假设的输出是整个分区的可靠性程度。

15. 发明授权

US07657085B2 Information processing apparatus and method, recording medium, and program 有权
标题翻译：信息处理装置和方法，记录介质和程序
公开(公告)号：US07657085B2
公开(公告)日：2010-02-02
申请号：US11089229
申请日：2005-03-24
申请人： Kohtaro Sabe , Kenichi Hidai
发明人： Kohtaro Sabe , Kenichi Hidai
IPC分类号： G06K9/62 , G05B13/02
CPC分类号： G06K9/00248 , G06K9/6217 , G06K9/6256 , G06K9/6282
摘要： In an information processing apparatus, such as an image processing apparatus, positive samples and negative samples are learned by a number of weak classifiers. During learning by the weak classifiers, a value of weighted majority is calculated as a sum of products of the results of classification by the respective weak classifiers and associated weights, and a learning threshold is also calculated. When the number of negative samples is greater than or equal to one half of the number of positive samples, negative samples for which the value of weighted majority is less than the learning threshold are removed.
摘要翻译：在诸如图像处理装置的信息处理装置中，由许多弱分类器学习正样本和负样本。在弱分类器的学习期间，加权多数的值被计算为相应弱分类器和关联权重的分类结果的乘积之和，并且还计算学习阈值。当阴性样本的数量大于或等于阳性样本数量的一半时，取消加权多数值小于学习阈值的负样本。

16. 发明授权

US07630525B2 Information processing apparatus and method, recording medium, and program 有权
标题翻译：信息处理装置和方法，记录介质和程序
公开(公告)号：US07630525B2
公开(公告)日：2009-12-08
申请号：US11089932
申请日：2005-03-25
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。

17. 发明授权

US07418312B2 Robot apparatus and walking control method thereof 失效
标题翻译：机器人装置及步行控制方法
公开(公告)号：US07418312B2
公开(公告)日：2008-08-26
申请号：US10934366
申请日：2004-09-07
申请人： Kenichi Hidai , Kohtaro Sabe
发明人： Kenichi Hidai , Kohtaro Sabe
IPC分类号： G06F19/00
CPC分类号： B62D57/032
摘要： An object of the present invention is to provide a robot apparatus and a walking control method thereof capable of changing walking control modes in accordance with floor surfaces by discriminating states of the floor surfaces for walking without modifying a step-based walking schedule and capable of providing stable walking even if floor surface states change greatly.A robot apparatus comprises: an action control section 11 to output a walking start instruction; a floor surface discrimination section 12 to discriminate a category for a current floor surface; and a walking control section 13 to compute an adaptive operation amount. The walking control section 13 obtains sensor values of a foot sole sensor and the like from the current floor surface by means of an in-place stepping motion and the like. Based on the sensor value, the walking control section 13 computes the adaptive operation amount as a correction amount from a standard gait model. The floor surface discrimination section 12 performs pattern recognition for the adaptive operation amount to discriminate the category for the current floor surface. The walking control section 13 is supplied with the floor surface category and selects an optimum walking model for the floor surface category. Again from the sensor value, the walking control section 13 computes the adaptive operation amount as a correction amount for the sensor value and provides walking control accordingly.
摘要翻译：本发明的目的是提供一种机器人装置及其行走控制方法，其能够通过区分用于行走的地板表面的状态来改变与地板表面相应的步行控制模式，而无需修改基于步进的步行时间表，并且能够提供即使地板表面状态发生很大变化，也能稳定地行走。机器人装置包括：动作控制部11，输出步行开始指示; 用于区分当前地板表面的类别的地板表面鉴别部分12; 以及步行控制部13，计算自适应动作量。步行控制部13通过就地步进动作等从当前的地面获得足底传感器等的传感器值。基于传感器值，步行控制部13根据标准步态模型计算自适应操作量作为校正量。地板面判别部12对自适应操作量进行模式识别，以区分当前地面的种类。行走控制部13被提供有地板表面类别，并且选择用于地板表面类别的最佳步行模型。从传感器值再次，步行控制部13计算自适应操作量作为传感器值的校正量，并相应地提供步行控制。

18. 发明申请

US20060190156A1 Learning control apparatus, learning control method, and computer program 审中-公开
标题翻译：学习控制装置，学习控制方法和计算机程序
公开(公告)号：US20060190156A1
公开(公告)日：2006-08-24
申请号：US11347227
申请日：2006-02-06
申请人： Kenichi Hidai , Kohtaro Sabe
发明人： Kenichi Hidai , Kohtaro Sabe
IPC分类号： G06F17/00
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。

19. 发明授权

US08401283B2 Information processing apparatus, 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：US08401283B2
公开(公告)日：2013-03-19
申请号：US12816779
申请日：2010-06-16
申请人： Kohtaro Sabe , Atsushi Okubo , Kenichi Hidai
发明人： Kohtaro Sabe , Atsushi Okubo , Kenichi Hidai
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06K9/00288 , G06K9/4614
摘要： An information processing apparatus includes the following elements. A learning unit is configured to perform Adaptive Boosting Error Correcting Output Coding learning using image feature values of a plurality of sample images each being assigned a class label to generate a multi-class classifier configured to output a multi-dimensional score vector corresponding to an input image. A registration unit is configured to input a register image to the multi-class classifier, and to register a multi-dimensional score vector corresponding to the input register image in association with identification information about the register image. A determination unit is configured to input an identification image to be identified to the multi-class classifier, and to determine a similarity between a multi-dimensional score vector corresponding to the input identification image and the registered multi-dimensional score vector corresponding to the register image.
摘要翻译：信息处理装置包括以下要素。学习单元被配置为使用多个样本图像的图像特征值来执行自适应提升误差校正输出编码学习，每个样本图像被分配类别标签，以生成被配置为输出与输入相对应的多维得分向量的多类分类器图片。注册单元被配置为将注册图像输入到多类分类器，并且与关于注册图像的识别信息相关联地注册与输入注册图像相对应的多维分数向量。确定单元被配置为将要识别的识别图像输入到多类分类器，并且确定与输入的标识图像相对应的多维得分向量与对应于寄存器的登记的多维得分向量之间的相似度图片。

20. 发明申请

US20120072141A1 DATA PROCESSING DEVICE, DATA PROCESSING METHOD, AND PROGRAM 有权
标题翻译：数据处理设备，数据处理方法和程序
公开(公告)号：US20120072141A1
公开(公告)日：2012-03-22
申请号：US13231591
申请日：2011-09-13
申请人： Kenichi Hidai , Kohtaro Sabe , Takashi Hasuo , Naoki Ide
发明人： Kenichi Hidai , Kohtaro Sabe , Takashi Hasuo , Naoki Ide
IPC分类号： G06F19/00
CPC分类号： G06F17/18 , G01R19/2506 , G01R19/2516 , G01R21/001 , G01R22/10 , G06N7/005
摘要： A data processing device includes: a data obtaining section obtaining time series data on a total value of current consumed by a plurality of electric apparatuses; and a parameter estimating section obtaining a model parameter when states of operation of the plurality of electric apparatuses are modeled by a factorial HMM on a basis of the obtained time series data.
摘要翻译：数据处理装置包括：数据获取部分，获得关于由多个电气设备消耗的电流总值的时间序列数据; 以及参数估计部，其基于所获得的时间序列数据，通过阶乘型HMM对多台电气设备的运转状态进行建模，得到模型参数。

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