公开(公告)号：US08634612B2

公开(公告)日：2014-01-21

申请号：US12719638

申请日：2010-03-08

申请人： Jun Yokono

发明人： Jun Yokono

IPC分类号： G06K9/00 ,  G06K9/56

CPC分类号： G06T7/0012 ,  G06K9/4642 ,  G06K2209/05 ,  G06T7/44 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/30004 ,  G06T2207/30128

摘要： An image processing apparatus identifies tissues in respective parts of a tissue image. A tissue image subdivider subdivides a tissue image for identification into local regions. A detector detects texture feature values of the local regions. A determining unit compares the detected texture feature value of a local region to a learned feature value for identification associated with a predetermined tissue, and on the basis of the comparison result, determines whether or not the local region belongs to the predetermined tissue.

摘要翻译： 图像处理装置识别组织图像的各部分中的组织。 组织图像细分器将组织图像细分为局部区域。 检测器检测局部区域的纹理特征值。 确定单元将检测到的局部区域的纹理特征值与用于与预定组织相关联的识别的学习特征值进行比较，并且基于比较结果，确定局部区域是否属于预定组织。

公开(公告)号：US08560467B2

公开(公告)日：2013-10-15

申请号：US12839321

申请日：2010-07-19

申请人： Masato Ito ,  Kohtaro Sabe ,  Hirotaka Suzuki ,  Jun Yokono ,  Kazumi Aoyama ,  Takashi Hasuo

发明人： Masato Ito ,  Kohtaro Sabe ,  Hirotaka Suzuki ,  Jun Yokono ,  Kazumi Aoyama ,  Takashi Hasuo

IPC分类号： G06F15/18

CPC分类号： G06K9/00335 ,  G06K9/00664 ,  G06K9/00778

摘要： A data processing apparatus includes an obtaining unit for obtaining time-series data, an activity model learning unit for learning an activity model representing a user activity state as a stochastic state transition model from the obtained time-series data, a recognition unit for recognizing a current user activity state by using the learned activity model, and a prediction unit for predicting a user activity state after a predetermined time elapses from a current time from the recognized current user activity state, wherein the prediction unit predicts the user activity state as an occurrence probability, and calculates the occurrence probabilities of the respective states on the basis of the state transition probability of the stochastic state transition model to predict the user activity state, while it is presumed that observation probabilities of the respective states at the respective times of the stochastic state transition model are an equal probability.

摘要翻译： 数据处理装置包括：获取单元，用于获取时间序列数据;活动模型学习单元，用于从所获得的时间序列数据中学习表示用户活动状态的活动模型作为随机状态转换模型;识别单元，用于识别 通过使用所学习的活动模型的当前用户活动状态，以及预测单元，用于在从所识别的当前用户活动状态起从当前时间经过预定时间之后预测用户活动状态，其中，所述预测单元将所述用户活动状态预测为发生 概率，并且基于随机状态转换模型的状态转移概率来计算各个状态的发生概率以预测用户活动状态，同时假设在随机的各个时间的各个状态的观察概率 状态转换模型是相等的概率。

公开(公告)号：US08428369B2

公开(公告)日：2013-04-23

申请号：US13022900

申请日：2011-02-08

申请人： Jun Yokono

发明人： Jun Yokono

IPC分类号： G06K9/62

CPC分类号： G06K9/6224

摘要： An information processing apparatus includes a characteristic amount calculating unit calculating a characteristic amount for each of a plurality of n different image patterns, a specifying unit specifying a best-matching image pattern among the plurality of n image patterns for each of frames forming a learning moving picture and having temporal continuity, a computing unit computing a collocation probability Pij indicating a probability that, for a frame located at a position where a temporal distance to a frame for which a first image pattern Xi is specified among the plurality of n image patterns is within a predetermined threshold τ, a second image pattern Xj is specified among the plurality of n image patterns, and a grouping unit grouping the plurality of n image patterns by using the computed collocation probability Pij.

摘要翻译： 信息处理装置包括特征量计算单元，用于计算多个n个不同图像图案中的每一个的特征量;指定单元，其指定形成学习移动的每个帧中的多个n个图像图案中的最佳匹配图像图案 图像并且具有时间连续性，计算单元计算搭配概率Pij，其指示对于在多个n个图像图案中指定了与第一图像图案Xi指定的帧的时间距离的位置处的帧为 在预定的阈值tau内，在多个n个图像图案中指定第二图像图案Xj，以及通过使用计算的搭配概率Pij对多个n个图像图案进行分组的分组单元。

公开(公告)号：US08396817B2

公开(公告)日：2013-03-12

申请号：US12550188

申请日：2009-08-28

申请人： Jun Yokono ,  Yuichi Hasegawa

发明人： Jun Yokono ,  Yuichi Hasegawa

IPC分类号： G06F15/18 ,  G09B19/00

CPC分类号： G09B19/00

摘要： A learning apparatus includes a feature extractor for extracting a feature at a feature point in a plurality of training images including training images that contains a target object to be recognized and that does not contain the target object, a tentative learner generator for generating a tentative learner for detecting the target object in an image, where the tentative learner is formed from a plurality of weak learners through statistical learning using the training images and the feature obtained from the training images, and a learner generator for generating a final learner that is formed from at least one of the weak learners and that detects the target object in an image by substituting the feature into a feature function formed from some of the weak learners of the tentative learner so as to obtain a new feature and performing statistical learning using the new feature and training images.

摘要翻译： 学习装置包括：特征提取器，用于在多个训练图像中的特征点处提取特征，所述训练图像包括包含要识别的目标对象并且不包含目标对象的训练图像;临时学习者生成器，用于生成临时学习者 用于通过使用训练图像和从训练图像获得的特征的统计学习从多个弱学习者形成临时学习者的图像中的目标对象，以及用于生成最终学习者的学习者生成器，其由 至少一个弱学习者，并且通过将特征代入从临时学习者的一些弱学习者形成的特征函数中来检测图像中的目标对象，以便获得新特征并使用新特征进行统计学习 并训练图像。

公开(公告)号：US08351676B2

公开(公告)日：2013-01-08

申请号：US12902321

申请日：2010-10-12

申请人： Shengyang Dai ,  Su Wang ,  Akira Nakamura ,  Takeshi Ohashi ,  Jun Yokono

发明人： Shengyang Dai ,  Su Wang ,  Akira Nakamura ,  Takeshi Ohashi ,  Jun Yokono

IPC分类号： G06K9/00 ,  G06K9/34

CPC分类号： G06K9/0014 ,  G06K9/00147 ,  G06K9/6282

摘要： Systems and methods for implementing a multi-step image recognition framework for classifying digital images are provided. The provided multi-step image recognition framework utilizes a gradual approach to model training and image classification tasks requiring multi-dimensional ground truths. A first step of the multi-step image recognition framework differentiates a first image region from a remainder image region. Each subsequent step operates on a remainder image region from the previous step. The provided multi-step image recognition framework permits model training and image classification tasks to be performed more accurately and in a less resource intensive fashion than conventional single-step image recognition frameworks.

摘要翻译： 提供了用于实现用于分类数字图像的多步图像识别框架的系统和方法。 提供的多步图像识别框架利用逐步的方法来模拟需要多维地面真实的训练和图像分类任务。 多步图像识别框架的第一步骤将第一图像区域与剩余图像区域区分开。 每个后续步骤对前一步骤的余数图像区域进行操作。 提供的多步图像识别框架允许模型训练和图像分类任务以比传统的单步图像识别框架更精确和更少资源密集的方式执行。

公开(公告)号：US20110029465A1

公开(公告)日：2011-02-03

申请号：US12839321

申请日：2010-07-19

申请人： Masato ITO ,  Kohtaro Sabe ,  Hirotaka Suzuki ,  Jun Yokono ,  Kazumi Aoyama ,  Takashi Hasuo

发明人： Masato ITO ,  Kohtaro Sabe ,  Hirotaka Suzuki ,  Jun Yokono ,  Kazumi Aoyama ,  Takashi Hasuo

IPC分类号： G06F15/18

CPC分类号： G06K9/00335 ,  G06K9/00664 ,  G06K9/00778

摘要： A data processing apparatus includes an obtaining unit configured to obtain time-series data from a wearable sensor, an activity model learning unit configured to learn an activity model representing a user activity state as a stochastic state transition model from the obtained time-series data, a recognition unit configured to recognize a current user activity state by using the activity model of the user obtained by the activity model learning unit, and a prediction unit configured to predict a user activity state after a predetermined time elapses from a current time from the current user activity state recognized by the recognition unit.

摘要翻译： 数据处理装置包括获取单元，被配置为从可穿戴传感器获取时间序列数据;活动模型学习单元，被配置为从所获得的时间序列数据中学习表示用户活动状态的活动模型作为随机状态转换模型， 识别单元，被配置为通过使用由活动模型学习单元获得的用户的活动模型来识别当前用户活动状态;以及预测单元，被配置为在从当前的当前时间经过预定时间之后预测用户活动状态 用户活动状态由识别单元识别。

公开(公告)号：US20100245394A1

公开(公告)日：2010-09-30

申请号：US12726290

申请日：2010-03-17

申请人： Jun Yokono

发明人： Jun Yokono

IPC分类号： G09G5/00

CPC分类号： G11B27/28 ,  G06T7/269 ,  G06T7/62 ,  G06T2207/10016 ,  G11B27/105

摘要： An image processing apparatus detects a representative frame of a moving image. The image processing apparatus includes a holding section configured to hold the moving image which is inputted, a detecting section configured to detect a peak of zooming that occurs in the inputted moving image, and an extracting section configured to extract the representative frame corresponding to the detected peak from a plurality of frames constituting the held moving image.

摘要翻译： 图像处理装置检测运动图像的代表性帧。 图像处理装置包括：保持部，被配置为保持输入的运动图像;检测部，被配置为检测在所输入的运动图像中发生的变焦的峰值;以及提取部，被配置为提取与检测到的运动图像相对应的代表性帧 从构成保持的运动图像的多个帧的峰值。

公开(公告)号：US07801354B2

公开(公告)日：2010-09-21

申请号：US11589872

申请日：2006-10-31

申请人： Jun Yokono

发明人： Jun Yokono

IPC分类号： G06K9/62

CPC分类号： G06K9/6228 ,  G06K9/6211 ,  G06K9/6256

摘要： An image processing system includes a learning device generating, in advance, a recognizer for recognizing a recognition target; and a recognition device recognizing, using the recognizer, whether a recognition image includes the recognition target. The learning device includes model feature point generator for generating model feature points, model feature quantity generator for generating model-feature quantities, learning feature point generator for generating learning feature points, learning feature quantity generator for generating learning feature quantities, learning correlation feature quantity generator for generating a learning correlation feature quantity, and recognizer generator for generating the recognizer. The recognition device includes recognition feature point generator for generating recognition feature points, recognition feature quantity generator for generating recognition feature quantities, recognition correlation feature quantity generator for generating a recognition correlation feature quantity, and recognition processor for determining whether the recognition image includes the recognition target.

摘要翻译： 图像处理系统包括学习装置，预先生成用于识别识别对象的识别器; 以及识别装置，使用所述识别器识别识别图像是否包括所述识别对象。 学习装置包括用于生成模型特征点的模型特征点生成器，用于生成模型特征量的模型特征量生成器，用于生成学习特征点的学习特征点生成器，用于生成学习特征量的学习特征量生成器，学习相关特征量生成器 用于生成学习相关特征量，以及用于生成识别器的识别器发生器。 识别装置包括用于产生识别特征点的识别特征点发生器，用于产生识别特征量的识别特征量发生器，用于产生识别相关特征量的识别相关特征量发生器和用于确定识别图像是否包括识别目标的识别处理器 。

公开(公告)号：US20100188519A1

公开(公告)日：2010-07-29

申请号：US12688665

申请日：2010-01-15

申请人： Keisuke YAMAOKA ,  Jun Yokono

发明人： Keisuke YAMAOKA ,  Jun Yokono

IPC分类号： H04N5/228 ,  G06K9/46

CPC分类号： G06F3/017 ,  A63F13/40 ,  G06F3/005 ,  G06F3/011 ,  G06F3/0304 ,  G06K9/00342 ,  G06K9/00369 ,  H04N5/222 ,  H04N5/2224

摘要： An information processing device includes: an outline extraction unit extracting an outline of a subject from a picked-up image of the subject; a characteristic amount extraction unit extracting a characteristic amount, by extracting sample points from points making up the outline, for each of the sample points; an estimation unit estimating a posture of a high degree of matching as a posture of the subject by calculating a degree of the characteristic amount extracted in the characteristic amount extraction unit being matched with each of a plurality of characteristic amounts that are prepared in advance and represent predetermined postures different from each other; and a determination unit determining accuracy of estimation by the estimation unit using a matching cost when the estimation unit carries out the estimation.

摘要翻译： 一种信息处理装置，包括：轮廓提取单元，从被摄体的拍摄图像中提取被检体的轮廓; 特征量提取单元，对于每个采样点，通过从构成轮廓的点提取采样点来提取特征量; 估计单元，通过计算在特征量提取单元中提取的特征量的程度与预先准备的表示的多个特征量中的每一个相匹配来估计高度匹配的姿势作为对象的姿势 预定姿势彼此不同; 以及确定单元，当估计单元执行估计时，使用匹配成本确定估计单元的估计精度。

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。