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1. 发明申请

US20120306934A1 IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, RECORDING MEDIUM, AND PROGRAM 有权
标题翻译：图像处理装置，图像处理方法，记录媒体和程序
公开(公告)号：US20120306934A1
公开(公告)日：2012-12-06
申请号：US13477521
申请日：2012-05-22
申请人： Takeshi Ohashi , Jun Yokono , Takuya Narihira
发明人： Takeshi Ohashi , Jun Yokono , Takuya Narihira
IPC分类号： G09G5/00
CPC分类号： G09G5/34 , G09G2380/08
摘要： There is provided an image processing device including a movement section which scrolls a medical image on a screen, and a display control section which, in a case where the medical image is scrolled on the screen, controls a display section to display the medical image in a manner that an observation reference position of a diagnosis region of the medical image passes through a display reference position of a display region of the screen.
摘要翻译：提供了一种图像处理装置，包括：滚动屏幕上的医学图像的移动部分;以及显示控制部分，其在医学图像在屏幕上滚动的情况下，控制显示部分显示医学图像医疗图像的诊断区域的观察基准位置通过屏幕的显示区域的显示基准位置的方式。

2. 发明授权

US09105239B2 Image processing device, image processing method, recording medium, and program 有权
标题翻译：图像处理装置，图像处理方法，记录介质和程序
公开(公告)号：US09105239B2
公开(公告)日：2015-08-11
申请号：US13477521
申请日：2012-05-22
申请人： Takeshi Ohashi , Jun Yokono , Takuya Narihira
发明人： Takeshi Ohashi , Jun Yokono , Takuya Narihira
IPC分类号： G09G5/00 , G09G5/34
CPC分类号： G09G5/34 , G09G2380/08
摘要： There is provided an image processing device including a movement section which scrolls a medical image on a screen, and a display control section which, in a case where the medical image is scrolled on the screen, controls a display section to display the medical image in a manner that an observation reference position of a diagnosis region of the medical image passes through a display reference position of a display region of the screen.
摘要翻译：提供了一种图像处理装置，包括：滚动屏幕上的医学图像的移动部分;以及显示控制部分，其在医学图像在屏幕上滚动的情况下，控制显示部分显示医学图像医疗图像的诊断区域的观察基准位置通过屏幕的显示区域的显示基准位置的方式。

3. 发明授权

US08345976B2 Systems and methods for segmenting digital images 有权
标题翻译：用于分割数字图像的系统和方法
公开(公告)号：US08345976B2
公开(公告)日：2013-01-01
申请号：US12852096
申请日：2010-08-06
申请人： Su Wang , Shengyang Dai , Akira Nakamura , Takeshi Ohashi , Jun Yokono
发明人： Su Wang , Shengyang Dai , Akira Nakamura , Takeshi Ohashi , Jun Yokono
IPC分类号： G06K9/34
CPC分类号： G06K9/6256 , G06K9/0014 , G06T7/0012 , G06T7/11 , G06T7/136 , G06T7/187 , G06T2207/30024
摘要： Methods and systems disclosed herein provide the capability to automatically process digital pathology images quickly and accurately. According to one embodiment, an digital pathology image segmentation task may be divided into at least two parts. An image segmentation task may be carried out utilizing both bottom-up analysis to capture local definition of features and top-down analysis to use global information to eliminate false positives. In some embodiments, an image segmentation task is carried out using a “pseudo-bootstrapping” iterative technique to produce superior segmentation results. In some embodiments, the superior segmentation results produced by the pseudo-bootstrapping method are used as input in a second segmentation task that uses a combination of bottom-up and top-down analysis.
摘要翻译：本文公开的方法和系统提供了快速且准确地自动处理数字病理图像的能力。根据一个实施例，数字病理图像分割任务可以被划分为至少两部分。图像分割任务可以利用自下而上的分析来捕获特征的局部定义和自上而下的分析，以使用全局信息来消除假阳性。在一些实施例中，使用伪自举迭代技术来执行图像分割任务以产生优异的分割结果。在一些实施例中，通过伪自举方法产生的优越分割结果被用作使用自下而上和自顶向下分析的组合的第二分段任务中的输入。

4. 发明授权

US09202281B2 Integrated interactive segmentation with spatial constraint for digital image analysis 有权
标题翻译：用于数字图像分析的集成交互式分割与空间约束
公开(公告)号：US09202281B2
公开(公告)日：2015-12-01
申请号：US13423209
申请日：2012-03-17
申请人： Su Wang , Shengyang Dai , Xun Xu , Akira Nakamura , Takeshi Ohashi , Jun Yokono
发明人： Su Wang , Shengyang Dai , Xun Xu , Akira Nakamura , Takeshi Ohashi , Jun Yokono
IPC分类号： G06K9/00 , G06T7/00
CPC分类号： G06T7/0081 , G06K9/00 , G06T7/11 , G06T7/143 , G06T7/162 , G06T7/194 , G06T2207/20081
摘要： An integrated interactive segmentation with spatial constraint method utilizes a combination of several of the most popular online learning algorithms into one and implements a spatial constraint which defines a valid mask local to the user's given marks. Additionally, both supervised learning and statistical analysis are integrated, which are able to compensate each other. Once prediction and activation are obtained, pixel-wised multiplication is conducted to fully indicate how likely each pixel belongs to the foreground or background.
摘要翻译：具有空间约束方法的集成交互式分割将几种最流行的在线学习算法的组合应用于一种，并实现了定义用户给定标记本地的有效掩码的空间约束。此外，监督学习和统计分析都是综合的，可以相互补偿。一旦获得了预测和激活，就进行像素增益乘法以完全指示每个像素属于前景或背景的可能性。

5. 发明授权

US08351676B2 Digital image analysis using multi-step analysis 有权
标题翻译：数字图像分析采用多步分析
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。

6. 发明授权

US06697711B2 Operational control method, program, and recording media for robot device, and robot device 失效
标题翻译：机器人装置的操作控制方法，程序和记录介质，以及机器人装置
公开(公告)号：US06697711B2
公开(公告)日：2004-02-24
申请号：US10258110
申请日：2002-10-18
申请人： Jun Yokono , Kohtaro Sabe , Gabriel Costa , Takeshi Ohashi
发明人： Jun Yokono , Kohtaro Sabe , Gabriel Costa , Takeshi Ohashi
IPC分类号： G06F1900
CPC分类号： G06N3/008
摘要： A robot apparatus (1) includes leg blocks (3A to 3D), head block (4), etc. as a moving part (16), a motion controller (102), learning unit (103), prediction unit (104) and a drive unit (105). When the moving part (106), any of the blocks, is operated from outside, the learning unit (103) learns a time-series signal generated due to the external operation. The motion controller (102) and drive unit (105) control together the moving part (106) based on a signal generated at the moving part (106) due to an external force applied to the robot apparatus (1) and a signal having already been learned by the learning unit (103) to make an action taught by the user. The prediction unit (105) predicts whether the moving part (106) makes the taught action according to the initial signal generated at the moving part (106) due to the applied external force. Thus, the robot apparatus (1) can learn an action taught by the user and determine an external force-caused signal to make the taught action.
摘要翻译：机器人装置（1）包括作为移动部件（16）的腿部块（3A至3D），头部块（4）等，运动控制器（102），学习单元（103），预测单元（104）和驱动单元（105）。当移动部分（106），任何块，从外部操作时，学习单元（103）学习由于外部操作而产生的时间序列信号。运动控制器（102）和驱动单元（105）基于由于施加到机器人装置（1）的外力而在运动部件（106）处产生的信号，以及已经具有的信号，一起控制运动部件（106）被学习单元（103）学习以进行用户教导的动作。预测单元（105）根据施加的外力来预测移动部件（106）是否根据在移动部件（106）产生的初始信号进行教导动作。因此，机器人装置（1）可以学习用户教导的动作，并确定外力产生的信号以进行教导动作。

7. 发明授权

US09208405B2 Systems and methods for digital image analysis 有权
标题翻译：数字图像分析的系统和方法
公开(公告)号：US09208405B2
公开(公告)日：2015-12-08
申请号：US12851818
申请日：2010-08-06
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 , G06K9/62
CPC分类号： G06K9/6292 , G06K9/6254
摘要： Systems and methods for implementing a hierarchical image recognition framework for classifying digital images are provided. The provided hierarchical image recognition framework utilizes a multi-layer approach to model training and image classification tasks. A first layer of the hierarchical image recognition framework generates first layer confidence scores, which are utilized by the second layer to produce a final recognition score. The provided hierarchical 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-layer image recognition frameworks. In some embodiments real-time operator guidance is provided for an image classification task.
摘要翻译：提供了用于实现用于分类数字图像的分层图像识别框架的系统和方法。所提供的分层图像识别框架利用多层方法对训练和图像分类任务进行建模。分层图像识别框架的第一层产生第一层置信度得分，其由第二层利用以产生最终识别分数。所提供的分层图像识别框架允许模型训练和图像分类任务以比常规单层图像识别框架更精确和更少资源密集的方式执行。在一些实施例中，为图像分类任务提供了实时操作者指导。

8. 发明申请

US20130243308A1 INTEGRATED INTERACTIVE SEGMENTATION WITH SPATIAL CONSTRAINT FOR DIGITAL IMAGE ANALYSIS 有权
标题翻译：用于数字图像分析的空间约束的集成交互分段
公开(公告)号：US20130243308A1
公开(公告)日：2013-09-19
申请号：US13423209
申请日：2012-03-17
申请人： Su Wang , Shengyang Dai , Xun Xu , Akira Nakamura , Takeshi Ohashi , Jun Yokono
发明人： Su Wang , Shengyang Dai , Xun Xu , Akira Nakamura , Takeshi Ohashi , Jun Yokono
IPC分类号： G06K9/62
CPC分类号： G06T7/0081 , G06K9/00 , G06T7/11 , G06T7/143 , G06T7/162 , G06T7/194 , G06T2207/20081
摘要： An integrated interactive segmentation with spatial constraint method utilizes a combination of several of the most popular online learning algorithms into one and implements a spatial constraint which defines a valid mask local to the user's given marks. Additionally, both supervised learning and statistical analysis are integrated, which are able to compensate each other. Once prediction and activation are obtained, pixel-wised multiplication is conducted to fully indicate how likely each pixel belongs to the foreground or background.
摘要翻译：具有空间约束方法的集成交互式分割将几种最流行的在线学习算法的组合应用于一种，并实现了定义用户给定标记本地的有效掩码的空间约束。此外，监督学习和统计分析都是综合的，可以相互补偿。一旦获得了预测和激活，就进行像素增益乘法以完全指示每个像素属于前景或背景的可能性。

9. 发明申请

US20120033861A1 SYSTEMS AND METHODS FOR DIGITAL IMAGE ANALYSIS 有权
标题翻译：数字图像分析系统与方法
公开(公告)号：US20120033861A1
公开(公告)日：2012-02-09
申请号：US12851818
申请日：2010-08-06
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
CPC分类号： G06K9/6292 , G06K9/6254
摘要： Systems and methods for implementing a hierarchical image recognition framework for classifying digital images are provided. The provided hierarchical image recognition framework utilizes a multi-layer approach to model training and image classification tasks. A first layer of the hierarchical image recognition framework generates first layer confidence scores, which are utilized by the second layer to produce a final recognition score. The provided hierarchical 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-layer image recognition frameworks. In some embodiments real-time operator guidance is provided for an image classification task.
摘要翻译：提供了用于实现用于分类数字图像的分层图像识别框架的系统和方法。所提供的分层图像识别框架利用多层方法对训练和图像分类任务进行建模。分层图像识别框架的第一层产生第一层置信度得分，其由第二层利用以产生最终识别分数。所提供的分层图像识别框架允许模型训练和图像分类任务以比常规单层图像识别框架更精确和更少资源密集的方式执行。在一些实施例中，为图像分类任务提供了实时操作者指导。

10. 发明授权

US08588518B2 Superpixel-boosted top-down image recognition methods and systems 有权
标题翻译：超像素增强自顶向下图像识别方法和系统
公开(公告)号：US08588518B2
公开(公告)日：2013-11-19
申请号：US12951702
申请日：2010-11-22
申请人： Su Wang , Shengyang Dai , Akira Nakamura , Takeshi Ohashi , Jun Yokono
发明人： Su Wang , Shengyang Dai , Akira Nakamura , Takeshi Ohashi , Jun Yokono
IPC分类号： G06K9/62
CPC分类号： G06K9/4671 , G06K9/468 , G06T7/12 , G06T2207/20076
摘要： Systems and methods for implementing a superpixel boosted top-down image recognition framework are provided. The framework utilizes superpixels comprising contiguous pixel regions sharing similar characteristics. Feature extraction methods described herein provide non-redundant image feature vectors for classification model building. The provided framework differentiates a digitized image into a plurality of superpixels. The digitized image is characterized through image feature extraction methods based on the plurality of superpixels. Image classification models are generated from the extracted image features and ground truth labels and may then be used to classify other digitized images.
摘要翻译：提供了用于实现超像素增强自顶向下图像识别框架的系统和方法。框架利用包含具有相似特征的相邻像素区域的超像素。本文描述的特征提取方法提供用于分类模型构建的非冗余图像特征向量。所提供的框架将数字化图像区分为多个超像素。数字化图像的特征在于基于多个超像素的图像特征提取方法。图像分类模型从提取的图像特征和地面真值标签生成，然后可以用于对其他数字化图像进行分类。

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