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1. 发明申请

US20050036649A1 Robot apparatus, face recognition method, and face recognition apparatus 失效
标题翻译：机器人装置，人脸识别方法和人脸识别装置
公开(公告)号：US20050036649A1
公开(公告)日：2005-02-17
申请号：US10399740
申请日：2002-08-21
申请人： Jun Yokono , Kohtaro Sabe , Kenta Kawamoto
发明人： Jun Yokono , Kohtaro Sabe , Kenta Kawamoto
IPC分类号： G06K9/00
CPC分类号： G06K9/00288 , G06K9/00221 , G06K9/00228
摘要： A robot includes a face extracting section for extracting features of a face included in an image captured by a CCD camera, and a face recognition section for recognizing the face based on a result of face extraction by the face extracting section. The face extracting section is implemented by Gabor filters that filter images using a plurality of filters that have orientation selectivity and that are associated with different frequency components. The face recognition section is implemented by a support vector machine that maps the result of face recognition to a non-linear space and that obtains a hyperplane that separates in that space to discriminate a face from a non-face. The robot is allowed to recognize a face of a user within a predetermined time under a dynamically changing environment.
摘要翻译：机器人包括：面部提取部，用于提取由CCD照相机拍摄的图像中包含的面部的特征;以及面部识别部，其基于面部提取部的面部提取的结果识别脸部。人脸提取部分由Gabor滤波器实现，该滤波器使用具有取向选择性并且与不同频率分量相关联的多个滤波器对图像进行滤波。脸部识别部分由支持向量机实现，该支持向量机将人脸识别的结果映射到非线性空间，并获得在该空间中分离的超平面，以将脸部与非脸部区分开。允许机器人在动态变化的环境下在预定时间内识别用户的脸部。

2. 发明授权

US07369686B2 Robot apparatus, face recognition method, and face recognition apparatus 失效
标题翻译：机器人装置，人脸识别方法和人脸识别装置
公开(公告)号：US07369686B2
公开(公告)日：2008-05-06
申请号：US10399740
申请日：2002-08-21
申请人： Jun Yokono , Kohtaro Sabe , Kenta Kawamoto
发明人： Jun Yokono , Kohtaro Sabe , Kenta Kawamoto
IPC分类号： G06K9/00
CPC分类号： G06K9/00288 , G06K9/00221 , G06K9/00228
摘要： A robot includes a face extracting section for extracting features of a face included in an image captured by a CCD camera, and a face recognition section for recognizing the face based on a result of face extraction by the face extracting section. The face extracting section is implemented by Gabor filters that filter images using a plurality of filters that have orientation selectivity and that are associated with different frequency components. The face recognition section is implemented by a support vector machine that maps the result of face recognition to a non-linear space and that obtains a hyperplane that separates in that space to discriminate a face from a non-face. The robot is allowed to recognize a face of a user within a predetermined time under a dynamically changing environment.
摘要翻译：机器人包括：面部提取部，用于提取由CCD照相机拍摄的图像中包含的面部的特征;以及面部识别部，其基于面部提取部的面部提取的结果识别脸部。人脸提取部分由Gabor滤波器实现，该滤波器使用具有取向选择性并且与不同频率分量相关联的多个滤波器对图像进行滤波。脸部识别部分由支持向量机实现，该支持向量机将人脸识别的结果映射到非线性空间，并获得在该空间中分离的超平面，以将脸部与非脸部区分开。允许机器人在动态变化的环境下在预定时间内识别用户的脸部。

3. 发明授权

US09104980B2 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：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。

4. 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。

5. 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。

6. 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。

7. 发明授权

US08755594B2 Information processing device and method, and program 有权
标题翻译：信息处理装置及方法，程序
公开(公告)号：US08755594B2
公开(公告)日：2014-06-17
申请号：US13116412
申请日：2011-05-26
申请人： Jun Yokono , Kohtaro Sabe
发明人： Jun Yokono , Kohtaro Sabe
IPC分类号： G06K9/00
CPC分类号： G06K9/6257 , G06K9/00791 , G06K2009/4666
摘要： An information processing device includes a first calculation unit which calculates a score of each sample image including a positive image in which an object as an identification object is present and a negative image in which the object as the identification object is not present, for each weak identifier of an identifier including a plurality of weak identifiers, a second calculation unit which calculates the number of scores when the negative image is processed, which are scores less than a minimum score among scores when the positive image is processed; and an realignment unit which realigns the weak identifiers in order from a weak identifier in which the number calculated by the second calculation unit is a maximum.
摘要翻译：一种信息处理设备，包括：第一计算单元，其计算包含其中存在作为识别对象的对象的正图像和不存在作为识别对象的对象的负图像的每个样本图像的得分，对于每个弱标识符包括多个弱标识符的标识符;第二计算单元，当处理正图像时，计算负图像处理时的得分数，该分数小于分数中的最小得分; 以及重新排列单元，其从由第二计算单元计算的数量最大的弱识别符依次重新排列弱标识符。

8. 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。

9. 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。

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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