公开(公告)号：US07054724B2

公开(公告)日：2006-05-30

申请号：US10484147

申请日：2002-07-16

申请人： Takamasa Koshizen ,  Hiroshi Tsujino ,  Hideaki Ono

发明人： Takamasa Koshizen ,  Hiroshi Tsujino ,  Hideaki Ono

IPC分类号： G05D1/00

CPC分类号： G05D1/0088

摘要： The invention relates to a behavior control apparatus and method for autonomously controlling a mobile unit based on visual information in practical application without the needs of a greatdeal of preparation or computational cost and limiting the type of target object. According to one aspect of the invention, a method for controlling behavior of a mobile unit using behavior command is provided. First, sensory inputs are captured and then the motion of the mobile unit is estimated. The portion which includes a target object to be target for behavior of the mobile unit is segregated from the sensory inputs. The target objects extracted from the segregated portion and the location of the target object is acquired. Finally, the mobile unit is controlled based on the location of target object.

摘要翻译： 本发明涉及一种用于在实际应用中基于视觉信息自主控制移动单元的行为控制装置和方法，而不需要大量准备或计算成本，并限制目标对象的类型。 根据本发明的一个方面，提供了一种使用行为命令来控制移动单元的行为的方法。 首先，捕获感觉输入，然后估计移动单元的运动。 包含目标对象的部分，其目标是针对移动单元的行为与感官输入隔离。 获取从分离部分提取的目标对象和目标对象的位置。 最后，根据目标对象的位置来控制移动单元。

公开(公告)号：US07783082B2

公开(公告)日：2010-08-24

申请号：US10561256

申请日：2004-06-30

申请人： Takamasa Koshizen ,  Bernd Heisele ,  Hiroshi Tsujino

发明人： Takamasa Koshizen ,  Bernd Heisele ,  Hiroshi Tsujino

IPC分类号： G06K9/00

CPC分类号： G06K9/6255 ,  G06K9/00221 ,  G06K9/00281 ,  G06K9/00973

摘要： A face recognition system includes a component learning/extraction module, component classifier training module, knowledge base for component classification (KBCC), component extraction module (CEM), object identification training module (OITM), knowledge base for face identification (KBFI), and object identification module (OIM). The CEM receives image data of faces at various viewpoints and extracts outputs of classification of the component data, using the results of classifier training of the component data, stored in the KBCC. The OITM receives the outputs of classification of the component data and determines indicator component for each person by Bayesian estimation so that posterior probability of a predetermined attention class is maximized under the outputs of classification of the component data at various viewpoints. The KBFI stores indicator components for the individuals. The OIM receives the outputs of classification of the component data and identifies faces using the indicator components stored in the KBFI.

摘要翻译： 面部识别系统包括组件学习/提取模块，组件分类器训练模块，组件分类知识库（KBCC），组件提取模块（CEM），对象识别训练模块（OITM），面部识别知识库（KBFI） 和对象识别模块（OIM）。 CEM在各种视点接收面部的图像数据，利用存储在KBCC中的分量数据的分类器训练结果，提取分量数据的分类输出。 OITM接收分量数据的分类输出，并通过贝叶斯估计确定每个人的指标分量，使得在各种视点下，在分量数据的分类输出下，使预定注意力级别的后验概率最大化。 KBFI存储个人的指示器组件。 OIM接收组件数据分类的输出，并使用KBFI中存储的指示器组件识别面。

公开(公告)号：US20050105771A1

公开(公告)日：2005-05-19

申请号：US10930566

申请日：2004-08-30

申请人： Shinichi Nagai ,  Hiroshi Tsujino ,  Tetsuya Ido ,  Takamasa Koshizen ,  Koji Akatsuka ,  Hiroshhi Kondo ,  Atsushi Miura

发明人： Shinichi Nagai ,  Hiroshi Tsujino ,  Tetsuya Ido ,  Takamasa Koshizen ,  Koji Akatsuka ,  Hiroshhi Kondo ,  Atsushi Miura

IPC分类号： G01V8/10 ,  G06K9/00 ,  G06K9/46 ,  G06K9/52 ,  G06K9/62 ,  G06K9/66 ,  G06T1/00 ,  G06T7/20

CPC分类号： G06K9/4619 ,  G06K9/3241 ,  G06T1/0014 ,  G06T7/215

摘要： The object detection apparatus according to the invention detects an object based on input images that are captured sequentially in time in a moving unit. The apparatus generates an action command to be sent to the moving unit, calculates flow information for each local area in the input image, and estimates an action of the moving unit based on the flow information. The apparatus calculates a difference between the estimated action and the action command and then determines a specific local area as a figure area when such difference in association with that specific local area exhibits an error larger than a predetermined value. The apparatus determines presence/absence of an object in the figure area.

摘要翻译： 根据本发明的对象检测装置基于在移动单元中及时地顺序地捕获的输入图像来检测对象。 该装置生成要发送到移动单元的动作命令，计算输入图像中的每个局部区域的流量信息，并基于流信息来估计移动单元的动作。 该装置计算估计动作和动作命令之间的差，然后当与该特定局部区域相关联的这种差异表现出大于预定值的误差时，将特定局部区域确定为图形区域。 该装置确定图形区域中的对象的存在/不存在。

公开(公告)号：US07221797B2

公开(公告)日：2007-05-22

申请号：US10133114

申请日：2002-04-26

申请人： Takamasa Koshizen ,  Koji Akatsuka ,  Hiroshi Tsujino

发明人： Takamasa Koshizen ,  Koji Akatsuka ,  Hiroshi Tsujino

IPC分类号： G06K9/46

CPC分类号： G06K9/00335 ,  G05D1/0253

摘要： An image recognizing apparatus and method is provided for recognizing behavior of a mobile unit accurately with an image of external environment acquired during the mobile unit is moving.Behavior command output block 12 outputs behavior commands to cause the mobile unit 32 move. Local feature extraction block 16 extracts features of local areas of the image from the image of external environment acquired on the mobile unit 32 when the behavior command is output. Global feature extraction block 18 extracts feature of global area of the image using the features of local areas. Learning block 20 calculates probability models for recognizing behavior given to the mobile unit 32 based on the feature of global area of the image. After learning is finished, behavior of the mobile unit 32 may be recognized rapidly and accurately by applying the probability models to an image of external environment acquired in mobile unit 32 afresh.

摘要翻译： 提供一种图像识别装置和方法，用于在移动单元移动期间获取的外部环境的图像精确地识别移动单元的行为。 行为命令输出块12输出行为命令以使移动单元32移动。 局部特征提取块16当输出行为命令时，从移动单元32上获取的外部环境的图像中提取图像的局部区域的特征。 全局特征提取块18使用局部区域的特征提取图像的全局区域的特征。 学习块20基于图像的全局区域的特征来计算用于识别给予移动单元32的行为的概率模型。 在完成学习之后，通过将概率模型应用于移动单元32中获取的外部环境的图像，可以快速且准确地识别移动单元32的行为。

公开(公告)号：US20060280341A1

公开(公告)日：2006-12-14

申请号：US10561256

申请日：2004-06-30

申请人： Takamasa Koshizen ,  Bernd Heisele ,  Hiroshi Tsujino

发明人： Takamasa Koshizen ,  Bernd Heisele ,  Hiroshi Tsujino

IPC分类号： G06K9/00

CPC分类号： G06K9/6255 ,  G06K9/00221 ,  G06K9/00281 ,  G06K9/00973

摘要： A production device and method which produce a multiple-system film having metal components such as TiAlN greatly different in melting point by a melting-evaporation type ion plating method that provides a high material utilization efficiency and a good film quality. Power needed to evaporate a material (4) is first supplied, and then power gradually increased over the initail power is repeatedly supplied until a needed maximum power is reached. Concurrently, a plasma control is performed for converging plasma (7) onto an initial area needed to evaporate the material, and then a plasma control is performed for continuously and sequentially moving/expanding plasma from the initial plasma area up to a maximum plasma area to thereby gradually melt the non-melted portion of the material.

公开(公告)号：US20060155660A1

公开(公告)日：2006-07-13

申请号：US10468316

申请日：2002-02-04

申请人： Takamasa Koshizen ,  Hiroshi Tsujino

发明人： Takamasa Koshizen ,  Hiroshi Tsujino

IPC分类号： G06E1/00

CPC分类号： G05B13/0265 ,  G05B13/027

摘要： An agent learning apparatus comprises a sensor (301) for acquiring a sense input, an action controller (307) for creating an action output in response to the sense input and giving the action output to a controlled object, an action state evaluator (303) for evaluating the behavior of the controlled object, a selective attention mechanism (304) for storing the action output and the sense input corresponding to the action output in one of the columns according to the evaluation, calculating a probability model from the action outputs stored in the columns, and outputting, as a learning result, the action output related to a newly given sense input in the column where the highest confidence obtained by applying the newly given sense input to the probability model is stored. By thus learning, the selective attention mechanism (304) obtains a probability relationship between the sense input and the column. An action output is calculated on the basis of the column evaluated as a stable column. As a result, the dispersion of the action output is quickly minimized, and thereby the controlled object can be stabilized.

摘要翻译： 代理学习装置包括用于获取感测输入的传感器（301），用于响应于感测输入创建动作输出并将动作输出给受控对象的动作控制器（307），动作状态评估器（303） 用于评估受控对象的行为的选择性注意机制（304），用于根据评估将动作输出和对应于动作输出的感测输入存储在一列中的选择性注意机制（304），从存储在 并且作为学习结果输出与通过将新给定的感测输入应用于概率模型获得的最高置信度的列中的新给定的感测输入相关的动作输出被存储。 通过这样学习，选择性注意机制（304）获得感知输入和列之间的概率关系。 基于作为稳定列计算的列计算动作输出。 结果，动作输出的分散被快速地最小化，从而可以使受控对象稳定。

公开(公告)号：US08774968B2

公开(公告)日：2014-07-08

申请号：US13004517

申请日：2011-01-11

申请人： Chyon Hae Kim ,  Hiroshi Tsujino

发明人： Chyon Hae Kim ,  Hiroshi Tsujino

IPC分类号： G05B19/04

CPC分类号： B25J9/1666 ,  G05B2219/40446

摘要： A trajectory planning system obtains a trajectory for controlling a state of an object toward a goal state. The system includes a search tree generating section which registers a state of the object as a root of a search tree in a state space, registers a next state of the object after a lapse of a predetermined time interval obtained through dynamical relationships during the time interval as a branch of the search tree in the state space. The system further includes a known-state registration tree storing section which stores a known-state registration tree and a known-state registration tree generating section which determines a cell to which the next state belongs among a plurality of cells previously prepared by segmenting the state space, determines whether or not a state which belongs to the cell has already been registered as a branch of the known-state registration tree, discards the next state when a state which belongs to the cell has been registered, and registers the next step as a branch of the known-state registration tree when a state which belongs to the cell has not been registered. The system further includes a trajectory generating section which selects a state whose distance to the goal state is minimum among states registered as branches of the known-state registration tree and obtains a trajectory using a sequence of states in a backward direction from the state toward the root of the known-state registration tree.

摘要翻译： 轨迹规划系统获得用于控制物体朝向目标状态的状态的轨迹。 该系统包括搜索树生成部分，其将对象的状态作为搜索树的根登记在状态空间中，在经过在时间间隔期间通过动态关系获得的预定时间间隔之后登记对象的下一状态 作为状态空间中搜索树的分支。 该系统还包括已知状态注册树存储部分，其存储已知状态注册树和已知状态注册树生成部分，该已知状态注册树生成部分通过分割状态来确定先前准备的多个小区中下一状态所属的小区 空间，确定属于小区的状态是否已经被注册为已知状态注册树的分支，当属于小区的状态已被注册时，丢弃下一状态，并将下一步骤注册为 当属于小区的状态尚未被注册时，已知状态注册树的分支。 该系统还包括：轨迹生成部，其选择在已知状态登记树的分支中登记为状态的目标状态的距离为最小的状态，并使用从状态朝向状态的向后方向的状态序列获取轨迹 已知状态注册树的根。

公开(公告)号：US08131542B2

公开(公告)日：2012-03-06

申请号：US12133691

申请日：2008-06-05

申请人： Hirofumi Nakajima ,  Kazuhiro Nakadai ,  Yuji Hasegawa ,  Hiroshi Tsujino

发明人： Hirofumi Nakajima ,  Kazuhiro Nakadai ,  Yuji Hasegawa ,  Hiroshi Tsujino

IPC分类号： G10L19/00 ,  H03F1/26

CPC分类号： G06K9/6242 ,  G06K9/6245 ,  G10L21/028 ,  G10L2021/02166

摘要： A system capable of separating sound source signals with high precision while improving a convergence rate and convergence precision. A process of updating a current separation matrix Wk to a next separation matrix Wk+1 such that a next value J(Wk+1) of a cost function is closer to a minimum value J(W0) than a current value J(Wk) is iteratively performed. An update amount ΔWk of the separation matrix is increased as the current value J(Wk) of the cost function is increased and is decreased as a current gradient ∂J(Wk)/∂W of the cost function is rapid. On the basis of input signals x from a plurality of microphones Mi and an optimal separation matrix W0, it is possible to separate sound source signals y(=W0·x) with high precision while improving a convergence rate and convergence precision.

摘要翻译： 一种能够在提高收敛速度和收敛精度的同时高精度地分离声源信号的系统。 将当前分离矩阵Wk更新为下一个分离矩阵Wk + 1的过程，使得成本函数的下一个值J（Wk + 1）比当前值J（Wk）更接近最小值J（W0） 被迭代地执行。 随着成本函数的当前值J（Wk）增加，并且随着成本函数的当前梯度∂J（Wk）/∂W）快速地减小，分离矩阵的更新量＆Dgr; Wk增加。 基于来自多个麦克风Mi的输入信号x和最优分离矩阵W0，可以在提高收敛速度和收敛精度的同时高精度地分离声源信号y（= W0·x）。

公开(公告)号：US20090018828A1

公开(公告)日：2009-01-15

申请号：US10579235

申请日：2004-11-12

申请人： Kazuhiro Nakadai ,  Hiroshi Tsujino ,  Hiroshi Okuno

发明人： Kazuhiro Nakadai ,  Hiroshi Tsujino ,  Hiroshi Okuno

IPC分类号： G10L19/14

CPC分类号： G10L15/20 ,  G10L21/028 ,  G10L2021/02166

摘要： An automatic speech recognition system includes: a sound source localization module for localizing a sound direction of a speaker based on the acoustic signals detected by the plurality of microphones; a sound source separation module for separating a speech signal of the speaker from the acoustic signals according to the sound direction; an acoustic model memory which stores direction-dependent acoustic models that are adjusted to a plurality of directions at intervals; an acoustic model composition module which composes an acoustic model adjusted to the sound direction, which is localized by the sound source localization module, based on the direction-dependent acoustic models, the acoustic model composition module storing the acoustic model in the acoustic model memory; and a speech recognition module which recognizes the features extracted by a feature extractor as character information using the acoustic model composed by the acoustic model composition module.

摘要翻译： 一种自动语音识别系统，包括：声源定位模块，用于基于由所述多个麦克风检测到的声信号来定位扬声器的声音方向; 声源分离模块，用于根据声音方向将扬声器的语音信号与声学信号分离; 声学模型存储器，其存储以间隔被调整到多个方向的方向相关的声学模型; 声学模型合成模块，其基于所述方向相关的声学模型，将声学模型组合模块存储在所述声学模型存储器中;声学模型组合模块，其将声学模型组合模块存储在所述声学模型存储器中; 以及语音识别模块，其使用由声学模型组合模块组成的声学模型识别由特征提取器提取的特征作为字符信息。

公开(公告)号：US20080306739A1

公开(公告)日：2008-12-11

申请号：US12133691

申请日：2008-06-05

申请人： Hirofumi Nakajima ,  Kazuhiro Nakadai ,  Yuji Hasegawa ,  Hiroshi Tsujino

发明人： Hirofumi Nakajima ,  Kazuhiro Nakadai ,  Yuji Hasegawa ,  Hiroshi Tsujino

IPC分类号： G10L11/00

CPC分类号： G06K9/6242 ,  G06K9/6245 ,  G10L21/028 ,  G10L2021/02166

摘要： A system capable of separating sound source signals with high precision while improving a convergence rate and convergence precision. A process of updating a current separation matrix Wk to a next separation matrix Wk+1 such that a next value J(Wk+1) of a cost function is closer to a minimum value J(W0) than a current value J(Wk) is iteratively performed. An update amount ΔWk of the separation matrix is increased as the current value J(Wk) of the cost function is increased and is decreased as a current gradient ∂J(Wk)/∂W of the cost function is rapid. On the basis of input signals x from a plurality of microphones Mi and an optimal separation matrix W0, it is possible to separate sound source signals y(=W0·x) with high precision while improving a convergence rate and convergence precision.

摘要翻译： 一种能够在提高收敛速度和收敛精度的同时高精度地分离声源信号的系统。 将当前分离矩阵Wk更新为下一个分离矩阵Wk + 1的过程，使得成本函数的下一个值J（Wk + 1）比当前值J（Wk）更接近最小值J（W0） 被迭代地执行。 随着成本函数的当前值J（Wk）增加，并且随着成本函数的当前梯度∂J（Wk）/∂W）快速地减小，分离矩阵的更新量DeltaWk增加。 基于来自多个麦克风Mi和最佳分离矩阵W0的输入信号x，可以在提高收敛速度和收敛精度的同时高精度地分离声源信号y（= W0.x）。