LOCALITY-BASED DETECTION OF TRAY SLOT TYPES AND TUBE TYPES IN A VISION SYSTEM
    1.
    发明申请
    LOCALITY-BASED DETECTION OF TRAY SLOT TYPES AND TUBE TYPES IN A VISION SYSTEM 审中-公开
    基于局部检测的托盘类型和管道类型在视觉系统中

    公开(公告)号:WO2016133926A1

    公开(公告)日:2016-08-25

    申请号:PCT/US2016/018112

    申请日:2016-02-16

    Abstract: A method for detecting properties of sample tubes is provided that includes extracting image patches substantially centered on a tube slot of a tray or a tube top in a slot. For each image patch, the method may include assigning a first location group defining whether the image patch is an image center, a comer of an image or a middle edge of an image, selecting a trained classifier based on the first location group and determining whether each tube slot contains a tube. The method may also include assigning a second location group defining whether the image patch is from an image center, a left comer of the image, a right comer of the image, a left middle of the image; a center middle of the image or a right middle of the image, selecting a trained classifier based on the second location group and determining a tube property.

    Abstract translation: 提供了一种用于检测样品管性质的方法,其包括提取基本中心在槽中的托盘或管顶的管槽上的图像片。 对于每个图像补丁,该方法可以包括分配定义图像补丁是图像中心,图像的角落还是图像的中间边缘的第一定位组,基于第一位置组来选择训练分类器,并且确定是否 每个管槽包含管。 该方法还可以包括:分配定义图像补丁是来自图像中心的第二位置组,图像的左角,图像的右角,图像的左中间; 图像的中心中间或图像的右中间,基于第二位置组选择训练有素的分类器并确定管属性。

    OPTIMIZING MULTI-CLASS IMAGE CLASSIFICATION USING PATCH FEATURES
    2.
    发明申请
    OPTIMIZING MULTI-CLASS IMAGE CLASSIFICATION USING PATCH FEATURES 审中-公开
    使用PATCH特性优化多类别图像分类

    公开(公告)号:WO2016118286A1

    公开(公告)日:2016-07-28

    申请号:PCT/US2015/067554

    申请日:2015-12-28

    CPC classification number: G06K9/6227 G06K9/6218 G06K9/623 G06K9/6262

    Abstract: Optimizing multi-class image classification by leveraging patch-based features extracted from weakly supervised images to train classifiers is described. A corpus of images associated with a set of labels may be received. One or more patches may be extracted from individual images in the corpus. Patch-based features may be extracted from the one or more patches and patch representations may be extracted from individual patches of the one or more patches. The patches may be arranged into clusters based at least in part on the patch-based features. At least some of the individual patches may be removed from individual clusters based at least in part on determined similarity values that are representative of similarity between the individual patches. The system may train classifiers based in part on patch-based features extracted from patches in the refined clusters. The classifiers may be used to accurately and efficiently classify new images.

    Abstract translation: 描述了通过利用从弱监督图像提取的基于补丁的特征来训练分类器来优化多类图像分类。 可以接收与一组标签相关联的图像语料库。 可以从语料库中的各个图像中提取一个或多个补丁。 可以从一个或多个补丁中提取基于补丁的特征,并且可以从一个或多个补丁的各个补丁提取补丁表示。 该补丁可以至少部分地基于基于补丁的特征来布置成群集。 可以至少部分地基于代表各个贴片之间的相似性的所确定的相似度值,从单个簇中去除至少一些单个贴片。 该系统可以部分地基于从精简集群中的补丁提取的基于补丁的特征来训练分类器。 分类器可用于准确和有效地对新图像进行分类。

    ジェスチャー認識装置、操作入力装置およびジェスチャー認識方法
    3.
    发明申请
    ジェスチャー認識装置、操作入力装置およびジェスチャー認識方法 审中-公开
    手势识别装置,操作输入装置和手势识别方法

    公开(公告)号:WO2015104919A1

    公开(公告)日:2015-07-16

    申请号:PCT/JP2014/081901

    申请日:2014-12-02

    Abstract:  本発明のジャスチャー認識装置は、バッテリー駆動であって、動画像から、ユーザによって行われたジェスチャーを互いに異なる処理量で認識する複数のジェスチャー認識処理を備え、前記複数のジェスチャー認識処理の中から前記バッテリーの電力残量に応じてジェスチャー認識処理を選択してこの選択したジェスチャー認識処理で前記ユーザによって行われたジェスチャーを認識する。

    Abstract translation: 该手势识别装置是电池驱动的,并且被提供有多个手势识别处理,其使用相互不同的处理量来从视频识别由用户做出的手势。 根据电池中剩余的电力量,从多个手势识别处理中选择手势识别处理,并且通过该选择的手势识别处理识别用户做出的手势。

    CLASSIFIER GENERATION METHOD USING COMBINATION OF MINI-CLASSIFIERS WITH REGULARIZATION AND USES THEREOF
    4.
    发明申请
    CLASSIFIER GENERATION METHOD USING COMBINATION OF MINI-CLASSIFIERS WITH REGULARIZATION AND USES THEREOF 审中-公开
    使用正则化的小型分类器组合分类器生成方法及其应用

    公开(公告)号:WO2015039021A2

    公开(公告)日:2015-03-19

    申请号:PCT/US2014/055633

    申请日:2014-09-15

    Applicant: BIODESIX, INC

    Abstract: A method for classifier generation includes a step of obtaining data for classification of a multitude of samples, the data for each of the samples consisting of a multitude of physical measurement feature values and a class label. Individual mini-classifiers are generated using sets of features from the samples. The performance of the mini-classifiers is tested, and those that meet a performance threshold are retained. A master classifier is generated by conducting a regularized ensemble training of the retained/filtered set of mini- classifiers to the classification labels for the samples, e.g., by randomly selecting a small fraction of the filtered mini-classifiers (drop out regularization) and conducting logistical training on such selected mini-classifiers. The set of samples are randomly separated into a test set and a training set. The steps of generating the mini-classifiers, filtering and generating a master classifier are repeated for different realizations of the separation of the set of samples into test and training sets, thereby generating a plurality of master classifiers. A final classifier is defined from one or a combination of more than one of the master classifiers.

    Abstract translation: 用于分类器生成的方法包括获得用于多个样本的分类的数据的步骤,每个样本的数据由多个物理测量特征值和类别标签组成。 个别迷你分类器是使用样本中的一组特征生成的。 小型分类器的性能经过测试,并且符合性能阈值的性能得以保留。 主分类器通过将保留/过滤的小分类器集合的正则集成训练生成为样本的分类标签,例如通过随机选择一小部分经过滤的小分类器(丢弃正则化)并传导 对这类精选小型分类器进行后勤培训。 该组样本被随机分为测试集和训练集。 生成微型分类器,过滤和生成主分类器的步骤被重复用于将样本集合分离为测试和训练集合的不同实现,从而生成多个主分类器。 最终的分类器是从一个或多个主分类器的组合中定义的。

    LOCATION-AIDED RECOGNITION
    5.
    发明申请
    LOCATION-AIDED RECOGNITION 审中-公开
    位置识别

    公开(公告)号:WO2012174024A1

    公开(公告)日:2012-12-20

    申请号:PCT/US2012/042106

    申请日:2012-06-13

    CPC classification number: G06K9/6227 G06K9/6231

    Abstract: A mobile device having the capability of performing real-time location recognition with assistance from a server is provided. The approximate geophysical location of the mobile device is uploaded to the server. Based on the mobile device's approximate geophysical location, the server responds by sending the mobile device a message comprising a classifier and a set of feature descriptors. This can occur before an image is captured for visual querying. The classifier and feature descriptors are computed during an offline training stage using techniques to minimize computation at query time. The classifier and feature descriptors are used to perform visual recognition in real-time by performing the classification on the mobile device itself.

    Abstract translation: 提供了具有从服务器的协助进行实时位置识别能力的移动设备。 移动设备的近似地球物理位置被上传到服务器。 基于移动设备的近似地球物理位置,服务器通过向移动设备发送包括分类器和一组特征描述符的消息来进行响应。 这可能在捕获图像以进行视觉查询之前发生。 分类器和特征描述符在离线训练阶段使用技术来计算,以最小化查询时间的计算。 分类器和特征描述符用于通过在移动设备本身上执行分类来实时执行视觉识别。

    FACE RECOGNITION APPARATUS AND METHODS
    6.
    发明申请
    FACE RECOGNITION APPARATUS AND METHODS 审中-公开
    脸部识别装置和方法

    公开(公告)号:WO2011065952A1

    公开(公告)日:2011-06-03

    申请号:PCT/US2009/066053

    申请日:2009-11-30

    CPC classification number: G06K9/6253 G06K9/00221 G06K9/6227

    Abstract: One or more facial recognition categories are assigned to a face region detected in an input image (24). Each of the facial recognition categories is associated with a respective set of one or more different feature extraction modules (66) and a respective set of one or more different facial recognition matching modules (76). For each of the facial recognition categories assigned to the face region, the input image (24) is processed with each of the feature extraction modules (66) associated with the facial recognition category to produce a respective facial region descriptor vector of facial region descriptor values characterizing the face region. A recognition result (96) between the face region and a reference face image (28) is determined based on application of the one or more facial recognition matching modules (76) associated with the facial recognition categories assigned to the face region to the facial region descriptor vectors produced for the face region detected in the input image (24).

    Abstract translation: 一个或多个面部识别类别被分配给在输入图像(24)中检测到的面部区域。 每个面部识别类别与相应的一组一个或多个不同的特征提取模块(66)和相应的一组一个或多个不同的面部识别匹配模块(76)相关联。 对于分配给面部区域的每个面部识别类别,与与面部识别类别相关联的每个特征提取模块(66)处理输入图像(24),以产生面部区域描述符值的相应面部区域描述符向量 表征脸部区域。 基于与分配给面部区域的面部识别类别相关联的一个或多个面部识别匹配模块(76)到面部区域的应用来确定面部区域和参考面部图像(28)之间的识别结果(96) 为在输入图像(24)中检测到的面部区域产生的描述符向量。

    OBJECT CATEGORIZATION USING STATISTICALLY-MODELED CLASSIFIER OUTPUTS
    8.
    发明申请
    OBJECT CATEGORIZATION USING STATISTICALLY-MODELED CLASSIFIER OUTPUTS 审中-公开
    使用统计分类器输出的对象分类

    公开(公告)号:WO2017027877A1

    公开(公告)日:2017-02-16

    申请号:PCT/US2016/047028

    申请日:2016-08-15

    CPC classification number: G06N3/08 G06K9/6227 G06K9/6262 G06K9/6267 G06N7/005

    Abstract: Systems and method for object characterization include generating a classifier that defines a decision hyperplane separating a first classification region of a virtual feature space from a second classification region of the virtual feature space. Input information is provided to the classifier, and a number of classifications are received from the classifier. A distribution of the classifications is determined and used to generate a prediction.

    Abstract translation: 用于对象表征的系统和方法包括生成分类器,其定义将虚拟特征空间的第一分类区域与虚拟特征空间的第二分类区域分开的决策超平面。 将输入信息提供给分类器,并从分类器接收多个分类。 确定分类的分布并用于产生预测。

    IDENTITY AUTHENTICATION BY USING HUMAN BIOLOGICAL CHARACTERISTIC
    9.
    发明申请
    IDENTITY AUTHENTICATION BY USING HUMAN BIOLOGICAL CHARACTERISTIC 审中-公开
    使用人体生物学特征的身份认证

    公开(公告)号:WO2015073860A2

    公开(公告)日:2015-05-21

    申请号:PCT/US2014065759

    申请日:2014-11-14

    Abstract: A human biological characteristic file corresponding to a particular identity is received and used as a base file. A characteristic code to be authenticated is obtained according to a human biological characteristic of a person who requests identity authentication when an identity authentication request corresponding to the particular identity is received. A base characteristic code is collected from a base file. A collecting algorithm applied for collecting the base characteristic code is the same as or matches an algorithm applied for obtaining the characteristic code. The present techniques determine whether the base characteristic code and the characteristic code correspond to a same human biological characteristic. If a result is positive, the identity authentication request is verified. The present techniques implement communication between different terminal devices of different manufacturers and effectively improve user experiences, thereby efficiently and conveniently implementing remote identity authentication.

    Abstract translation: 接收与特定身份相对应的人类生物特征文件并将其用作基本文件。 当接收到与特定身份相对应的身份认证请求时,根据请求身份认证的人的人的生物学特性,获得要认证的特征码。 从基本文件收集基本特征码。 用于收集基本特征码的收集算法与用于获得特征码的算法相同或匹配。 本技术确定基本特征码和特征码是否对应于相同的人类生物特征。 如果结果为正,则验证身份认证请求。 本技术实现不同制造商的不同终端设备之间的通信,有效提高用户体验,从而有效且方便地实现远程身份认证。

    画像処理装置、画像処理方法および画像処理プログラム
    10.
    发明申请
    画像処理装置、画像処理方法および画像処理プログラム 审中-公开
    图像处理设备,图像处理方法和图像处理程序

    公开(公告)号:WO2014024655A1

    公开(公告)日:2014-02-13

    申请号:PCT/JP2013/069510

    申请日:2013-07-18

    Inventor: 堀田 伸一

    Abstract:  画像の周波数成分を利用するテンプレートマッチングに用いられるテンプレート画像を生成するための画像処理装置が提供される。画像処理装置は、入力画像におけるテンプレート候補画像としての領域の設定を受け付ける設定部と、前記入力画像に設定されたテンプレート候補画像について、前記テンプレート候補画像自体を解析することにより、前記テンプレート画像としての適切さを示す有効度を計算する有効度計算部とを含む。前記有効度計算部は、前記テンプレート候補画像の周波数分布に関連する値に基づいて前記有効度を計算する。

    Abstract translation: 提供了一种用于使用图像的频率分量生成用于模板匹配的模板图像的图像处理装置。 图像处理装置包括:设置单元,其在输入图像中接收作为模板候选图像的区域的设置; 以及有效性计算单元,对于输入图像中设置的模板候补图像,通过分析模板候选图像本身,计算表示适合性的有效性,作为模板图像。 有效性计算单元基于与模板候选图像的频率分布有关的值来计算有效性。

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