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1. 发明授权

US08531514B2 Image providing system and image providing method 有权
标题翻译：图像提供系统和图像提供方法
公开(公告)号：US08531514B2
公开(公告)日：2013-09-10
申请号：US12679277
申请日：2008-09-18
申请人： Motoaki Shimizu , Hitoshi Imaoka , Keiichi Chono , Fumihiro Adachi , Satoshi Nakazawa , Shin Ishiguro , Masayuki Yuguchi
发明人： Motoaki Shimizu , Hitoshi Imaoka , Keiichi Chono , Fumihiro Adachi , Satoshi Nakazawa , Shin Ishiguro , Masayuki Yuguchi
IPC分类号： H04N7/18
CPC分类号： G06F3/0482 , G06F3/0346 , G06K9/3258 , H04N1/00244 , H04N1/00307 , H04N1/00323 , H04N1/32776 , H04N7/173 , H04N21/21805 , H04N21/25841 , H04N21/41407 , H04N21/4223 , H04N2201/0084
摘要： A terminal has a visual axis by using which a user points to a target object. An information generating server acquires an internal image or an external image of the target object pointed by a user, and generates a display image which is displayed on the terminal.
摘要翻译：终端具有通过使用用户指向目标对象的视轴。信息生成服务器获取由用户指向的目标对象的内部图像或外部图像，并生成显示在终端上的显示图像。

2. 发明授权

US08121357B2 Coefficient determining method, feature extracting method, system, and program, and pattern checking method, system, and program 有权
标题翻译：系数确定方法，特征提取方法，系统和程序，模式检查方法，系统和程序
公开(公告)号：US08121357B2
公开(公告)日：2012-02-21
申请号：US12091160
申请日：2006-10-23
申请人： Hitoshi Imaoka
发明人： Hitoshi Imaoka
IPC分类号： G06K9/62
CPC分类号： G06K9/00288 , G06K9/6235
摘要： [PROBLEMS] To provide a feature extracting method for quickly extracting a feature while preventing lowering of the identification performance of the kernel judgment analysis, a feature extracting system, and a feature extracting program.[MEANS FOR SOLVING PROBLEMS] Judgment feature extracting device (104) computes an interclass covariance matrix SB and an intraclass covariance matrix SW about a learning face image prepared in advance, determines optimum vectors η, γ which maximizes the ratio of the interclass covariance to the intraclass covariance, derives a conversion formula for converting an inputted frequency feature vector x into a frequency feature vector y in a judgment space, and extracts judgment features of a face image for record and a face image for check by using a restructured conversion formula. Similarity computing device (105) computes the similarity by comparing the judgment features. Check judging device judges whether or not the persons are the same by comparing the similarity with a threshold.
摘要翻译： [问题]提供一种特征提取方法，用于快速提取特征，同时防止降低核心判断分析的识别性能，特征提取系统和特征提取程序。 [解决问题的手段]判断特征提取装置（104）计算关于预先准备的学习面部图像的类间协方差矩阵SB和类内协方差矩阵SW，确定最佳向量＆eegr，γ，其使类间协方差与类内协方差导出用于将输入的频率特征向量x转换为判断空间中的频率特征向量y的转换公式，并且通过使用重构的转换公式来提取用于记录的面部图像的判断特征和用于检查的面部图像。相似度计算装置（105）通过比较判断特征来计算相似度。检查判断装置通过将相似度与阈值进行比较来判断人是否相同。

3. 发明申请

US20110002511A1 MOLE IDENTIFYING DEVICE, AND PERSONAL AUTHENTICATION DEVICE, METHOD, AND PROGRAM 审中-公开
标题翻译： MOLE识别设备和个人认证设备，方法和程序
公开(公告)号：US20110002511A1
公开(公告)日：2011-01-06
申请号：US12919061
申请日：2009-02-06
申请人： Hitoshi Imaoka
发明人： Hitoshi Imaoka
IPC分类号： G06K9/00
CPC分类号： G06F21/32 , A61B5/1176 , G06K9/00362 , G06K9/2018
摘要： A mole identifying method having the following steps: inputting a multispectral image photographed using an imaging device and composed of a plurality of spectra; detecting mole candidates from said multispectral image; and identifying said mole candidates as a true mole or false mole based on the absorption spectra of said detected mole candidates.
摘要翻译：一种摩擦识别方法，具有以下步骤：输入使用成像装置拍摄并由多个光谱组成的多光谱图像; 从所述多光谱图像中检测痣候选物; 并且基于所述检测到的痣候选物的吸收光谱，将所述痣候选物鉴定为真痣或假痣。

4. 发明申请

US20090123077A1 COEFFICIENT DETERMINING METHOD, FEATURE EXTRACTING METHOD, SYSTEM, AND PROGRAM, AND PATTERN CHECKING METHOD, SYSTEM, AND PROGRAM 有权
标题翻译：系统确定方法，特征提取方法，系统和程序，以及模式检查方法，系统和程序
公开(公告)号：US20090123077A1
公开(公告)日：2009-05-14
申请号：US12091160
申请日：2006-10-23
申请人： Hitoshi Imaoka
发明人： Hitoshi Imaoka
IPC分类号： G06K9/56
CPC分类号： G06K9/00288 , G06K9/6235
摘要： [PROBLEMS] To provide a feature extracting method for quickly extracting a feature while preventing lowering of the identification performance of the kernel judgment analysis, a feature extracting system, and a feature extracting program. [MEANS FOR SOLVING PROBLEMS] Judgment feature extracting device (104) computes an interclass covariance matrix SB and an intraclass covariance matrix SW about a learning face image prepared in advance, determines optimum vectors η, γ which maximizes the ratio of the interclass covariance to the intraclass covariance, derives a conversion formula for converting an inputted frequency feature vector x into a frequency feature vector y in a judgment space, and extracts judgment features of a face image for record and a face image for check by using a restructured conversion formula. Similarity computing device (105) computes the similarity by comparing the judgment features. Check judging device judges whether or not the persons are the same by comparing the similarity with a threshold.
摘要翻译： [问题]提供一种特征提取方法，用于快速提取特征，同时防止降低核心判断分析的识别性能，特征提取系统和特征提取程序。 [解决问题的手段]判断特征提取装置（104）计算关于预先准备的学习面部图像的类间协方差矩阵SB和类内协方差矩阵SW，确定最佳向量eta，使最大化跨类协方差与类内协方差导出用于将输入的频率特征向量x转换为判断空间中的频率特征向量y的转换公式，并且通过使用重构的转换公式来提取用于记录的面部图像的判断特征和用于检查的面部图像。相似度计算装置（105）通过比较判断特征来计算相似度。检查判断装置通过将相似度与阈值进行比较来判断人是否相同。

5. 发明申请

US20050169516A1 Pattern feature selection method, classification method, judgment method, program, and device 有权
标题翻译：模式特征选择方法，分类方法，判断方法，程序和装置
公开(公告)号：US20050169516A1
公开(公告)日：2005-08-04
申请号：US10505903
申请日：2003-02-27
申请人： Kenji Okajima , Hitoshi Imaoka , Masanobu Miyasita
发明人： Kenji Okajima , Hitoshi Imaoka , Masanobu Miyasita
IPC分类号： G06F17/30 , G06K9/62 , G06N3/00 , G06T7/00
CPC分类号： G06K9/623
摘要： Feature decision means (303) decides a set of features appropriate for pattern identification from a plenty of feature candidates generated by feature candidate generation means (302) by using learning patterns stored in learning, pattern storage means (301). The feature decision means (303) successively decides features according to a reference of information maximization under the condition that the decided feature is known while adding an effective noise to the learning pattern and performs information amount calculation approximately and at a high speed while merging the learning patterns into a set of N elements when required. As a result, it is possible to automatically create a feature set appropriate for pattern identification of a high performance without requiring enormous learning. Moreover, by using a transition table (305) containing transitions between sets, it is possible to perform pattern judgment with a high efficiency.
摘要翻译：特征决定装置（303）通过使用存储在学习模式存储装置（301）中的学习模式，从特征候选生成装置（302）产生的大量特征候选中，确定适合于模式识别的特征的一组。特征决定装置（303）在所确定的特征已知的条件下，根据信息最大化的参考依次确定特征，同时向学习模式添加有效噪声，并且在合并学习期间大约和高速地执行信息量计算当需要时，模式成为一组N个元素。因此，可以自动创建适合于高性能的图案识别的特征集，而不需要巨大的学习。此外，通过使用包含组之间的转换的转换表（305），可以以高效率执行模式判断。

6. 发明申请

US20110135167A1 PERSONAL AUTHENTICATION SYSTEM AND PERSONAL AUTHENTICATION METHOD 有权
标题翻译：个人认证系统和个人认证方法
公开(公告)号：US20110135167A1
公开(公告)日：2011-06-09
申请号：US13003270
申请日：2009-07-06
申请人： Hitoshi Imaoka
发明人： Hitoshi Imaoka
IPC分类号： G06K9/00
CPC分类号： G06F21/32 , G06K9/00275 , G06K9/6243
摘要： A personal authentication system according to the present invention includes a matrix generation unit, a feature extraction unit, a feature transformation unit, a processing unit, and a data matching unit. The matrix generation unit classifies a plurality of feature amounts of face image data, which are recorded in advance, into classes and calculates as a mapping matrix, coefficients of a linear discriminant equation which uses the recorded plurality of feature amounts as variables. The feature extraction unit extracts a first feature amount from first face image data and extracts a second feature amount from second face image data. The feature transformation unit, by using the mapping matrix, transforms the first feature amount into a first transformed feature amount and transforms the second feature amount into a second transformed feature amount. The processing unit calculates as a similarity, a normalized correlation value between the first transformed feature amount and the second transformed feature amount. The data matching unit judges that the first face image data and the second face image data are image data of same person when the similarity exceeds a predetermined threshold.
摘要翻译：根据本发明的个人认证系统包括矩阵生成单元，特征提取单元，特征变换单元，处理单元和数据匹配单元。矩阵生成单元将预先记录的面部图像数据的多个特征量分类为类别，并且将使用所记录的多个特征量的线性判别式的系数作为映射矩阵来计算。特征提取单元从第一面部图像数据提取第一特征量，并从第二面部图像数据提取第二特征量。特征变换单元通过使用映射矩阵将第一特征量变换为第一变换特征量，并将第二特征量变换为第二变换特征量。处理单元计算第一变换特征量与第二变换特征量之间的相似度，归一化相关值。当相似度超过预定阈值时，数据匹配单元判断第一面部图像数据和第二面部图像数据是相同人物的图像数据。

7. 发明授权

US07634140B2 Pattern feature selection method, classification method, judgment method, program, and device 有权
标题翻译：模式特征选择方法，分类方法，判断方法，程序和装置
公开(公告)号：US07634140B2
公开(公告)日：2009-12-15
申请号：US10505903
申请日：2003-02-27
申请人： Kenji Okajima , Hitoshi Imaoka , Masanobu Miyasita
发明人： Kenji Okajima , Hitoshi Imaoka , Masanobu Miyasita
IPC分类号： G06K9/62
CPC分类号： G06K9/623
摘要： Feature decision means (303) decides a set of features appropriate for pattern identification from a plenty of feature candidates generated by feature candidate generation means (302) by using learning patterns stored in learning, pattern storage means (301). The feature decision means (303) successively decides features according to a reference of information maximization under the condition that the decided feature is known while adding an effective noise to the learning pattern and performs information amount calculation approximately and at a high speed while merging the learning patterns into a set of N elements when required. As a result, it is possible to automatically create a feature set appropriate for pattern identification of a high performance without requiring enormous learning. Moreover, by using a transition table (305) containing transitions between sets, it is possible to perform pattern judgment with a high efficiency.
摘要翻译：特征决定装置（303）通过使用存储在学习模式存储装置（301）中的学习模式，从特征候选生成装置（302）产生的大量特征候选中，确定适合于模式识别的特征的一组。特征决定装置（303）在所确定的特征已知的条件下，根据信息最大化的参考依次确定特征，同时向学习模式添加有效噪声，并且在合并学习期间大约和高速地执行信息量计算当需要时，模式成为一组N个元素。因此，可以自动创建适合于高性能的图案识别的特征集，而不需要巨大的学习。此外，通过使用包含组之间的转换的转换表（305），可以以高效率执行模式判断。

8. 发明申请

US20090087036A1 Pattern Matching Method, Pattern Matching System, and Pattern Matching Program 审中-公开
标题翻译：模式匹配方法，模式匹配系统和模式匹配程序
公开(公告)号：US20090087036A1
公开(公告)日：2009-04-02
申请号：US11921323
申请日：2006-05-25
申请人： Hitoshi Imaoka
发明人： Hitoshi Imaoka
IPC分类号： G06K9/00
CPC分类号： G06K9/00288 , G06K9/6234 , G06K9/6255
摘要： A variation image generation means generates a plurality of variation images having different postures, facial positions, and sizes with respect to a normalized image. A characteristic extraction means extracts a frequency characteristic from the plurality of variation images. A discriminant space projection means projects the frequency characteristic on a discriminant space having high discriminant ability that is obtained by linear discriminant analysis. A reference person comparison means performs a reference person comparison to extract a highly discriminant characteristic. A discriminant characteristic is extracted for a match image using the characteristic extraction means and the discriminant space projection means. A score computation means uses a discriminant axis obtained from a registered image, and the discriminant characteristic obtained from the match image to output a match score. A match determination means determines whether the person is the same person by comparing the match score with a threshold value.
摘要翻译：变化图像生成装置生成相对于归一化图像具有不同姿态，面部位置和尺寸的多个变形图像。特征提取装置从多个变化图像中提取频率特性。判别空间投影装置将通过线性判别分析获得的具有高判别能力的判别空间投射频率特性。参考人比较装置执行参考人比较以提取高度判别特性。使用特征提取装置和判别空间投影装置为匹配图像提取判别特性。分数计算装置使用从注册图像获得的判别轴，并且从匹配图像获得的判别特性输出匹配分数。匹配确定装置通过将匹配分数与阈值进行比较来确定该人是否是同一人。

9. 发明授权

US08553983B2 Personal authentication system and personal authentication method 有权
标题翻译：个人认证系统和个人认证方式
公开(公告)号：US08553983B2
公开(公告)日：2013-10-08
申请号：US13003270
申请日：2009-07-06
申请人： Hitoshi Imaoka
发明人： Hitoshi Imaoka
IPC分类号： G06K9/46 , G06K9/66 , G06K9/00 , G06K9/62
CPC分类号： G06F21/32 , G06K9/00275 , G06K9/6243
摘要： A personal authentication system according to the present invention includes a matrix generation unit, a feature extraction unit, a feature transformation unit, a processing unit, and a data matching unit. The matrix generation unit classifies a plurality of feature amounts of face image data, which are recorded in advance, into classes and calculates as a mapping matrix, coefficients of a linear discriminant equation which uses the recorded plurality of feature amounts as variables. The feature extraction unit extracts a first feature amount from first face image data and extracts a second feature amount from second face image data. The feature transformation unit, by using the mapping matrix, transforms the first feature amount into a first transformed feature amount and transforms the second feature amount into a second transformed feature amount. The processing unit calculates as a similarity, a normalized correlation value between the first transformed feature amount and the second transformed feature amount. The data matching unit judges that the first face image data and the second face image data are image data of same person when the similarity exceeds a predetermined threshold.
摘要翻译：根据本发明的个人认证系统包括矩阵生成单元，特征提取单元，特征变换单元，处理单元和数据匹配单元。矩阵生成单元将预先记录的面部图像数据的多个特征量分类为类别，并且将使用记录的多个特征量的线性判别式的系数作为映射矩阵来计算。特征提取单元从第一面部图像数据提取第一特征量，并从第二面部图像数据提取第二特征量。特征变换单元通过使用映射矩阵将第一特征量变换为第一变换特征量，并将第二特征量变换为第二变换特征量。处理单元计算第一变换特征量与第二变换特征量之间的相似度，归一化相关值。当相似度超过预定阈值时，数据匹配单元判断第一面部图像数据和第二面部图像数据是相同人物的图像数据。

10. 发明授权

US07860340B2 Three-dimensional shape estimation system and image generation system 有权
标题翻译：三维形状估计系统和图像生成系统
公开(公告)号：US07860340B2
公开(公告)日：2010-12-28
申请号：US11718601
申请日：2005-11-01
申请人： Atsushi Marugame , Hitoshi Imaoka
发明人： Atsushi Marugame , Hitoshi Imaoka
IPC分类号： G06K9/40 , G06K9/00 , G06K9/36 , G06T1/00 , G06T15/00
CPC分类号： G06T17/10 , G06T7/507 , G06T2207/10024 , G06T2207/10028 , G06T2207/30201
摘要： A 3D shape estimation system has a storage device, a relative shape analysis module, a feature point location search module and an absolute shape analysis module. The storage device stores first and second learning data which represent illumination bases and 3D shapes of objects, respectively. The relative shape analysis module calculates an “illumination basis” of an object based on a 2D image of the object and the first learning data, calculates a “relative shape function” that is partial differential of a “shape function” indicating a 3D shape of the object from the illumination basis, and outputs a relative shape data indicating the relative shape function. The feature point location search module extracts a plurality of feature points from the input 2D face image based on the 2D image and the relative shape data, and outputs a feature point location data indicating locations of the feature points. The absolute shape analysis module receives the relative shape data and the feature point location data, converts the relative shape function into the shape function by referring to the second learning data and the locations of the feature points, and outputs a 3D absolute shape data indicating the shape function.
摘要翻译： 3D形状估计系统具有存储装置，相对形状分析模块，特征点位置搜索模块和绝对形状分析模块。存储装置分别存储表示对象的照明基座和3D形状的第一和第二学习数据。相对形状分析模块基于对象的2D图像和第一学习数据计算对象的“照明基础”，计算表示3D形状的“形状函数”的偏差的“相对形状函数” 并且输出表示相对形状函数的相对形状数据。特征点位置搜索模块基于2D图像和相对形状数据从输入的2D面部图像提取多个特征点，并且输出指示特征点的位置的特征点位置数据。绝对形状分析模块接收相对形状数据和特征点位置数据，通过参考第二学习数据和特征点的位置将相对形状函数转换为形状函数，并输出指示形状函数。

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