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    • 3. 发明授权
    • Information processing apparatus, information processing method, and program
    • 信息处理装置,信息处理方法和程序
    • US08571315B2
    • 2013-10-29
    • US13288231
    • 2011-11-03
    • Kohtaro SabeKenichi HidaiKiyoto Ichikawa
    • Kohtaro SabeKenichi HidaiKiyoto Ichikawa
    • G06K9/00G06K9/38G06K9/62G06K9/68
    • G06K9/4614G06K9/6256
    • 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.
    • 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。
    • 4. 再颁专利
    • Device and method for detecting object and device and method for group learning
    • 用于组学习的物体和装置的检测装置及方法
    • USRE43873E1
    • 2012-12-25
    • US13208123
    • 2011-08-11
    • Kenichi HidaiKohtaro SabeKenta Kawamoto
    • Kenichi HidaiKohtaro SabeKenta Kawamoto
    • G06K9/62G06K9/00
    • G06K9/6282G06K9/00248G06K9/6256
    • 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.
    • 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。
    • 8. 发明授权
    • Robot apparatus and walking control method thereof
    • 机器人装置及步行控制方法
    • US07418312B2
    • 2008-08-26
    • US10934366
    • 2004-09-07
    • Kenichi HidaiKohtaro Sabe
    • Kenichi HidaiKohtaro Sabe
    • G06F19/00
    • B62D57/032
    • 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.
    • 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。
    • 9. 发明申请
    • Learning control apparatus, learning control method, and computer program
    • 学习控制装置,学习控制方法和计算机程序
    • US20060190156A1
    • 2006-08-24
    • US11347227
    • 2006-02-06
    • Kenichi HidaiKohtaro Sabe
    • Kenichi HidaiKohtaro Sabe
    • G06F17/00
    • G05B13/0265
    • 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.
    • 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。
    • 10. 发明申请
    • Device and method for detecting object and device and method for group learning
    • 用于组学习的物体和装置的检测装置及方法
    • US20050280809A1
    • 2005-12-22
    • US10994942
    • 2004-11-22
    • Kenichi HidaiKohtaro SabeKenta Kawamoto
    • Kenichi HidaiKohtaro SabeKenta Kawamoto
    • G06T1/00G06K9/00G06K9/62G06K9/68G06N3/08G06T7/00G01N21/00
    • G06K9/6282G06K9/00248G06K9/6256
    • 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.
    • 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。