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    • 1. 发明授权
    • Multi-frame depth image information identification
    • 多帧深度图像信息识别
    • US08966613B2
    • 2015-02-24
    • US13250542
    • 2011-09-30
    • Eric HorvitzDesney S. TanJames Chia-Ming Liu
    • Eric HorvitzDesney S. TanJames Chia-Ming Liu
    • G06F21/00H04L9/32G06F21/32H04L29/06
    • H04L9/3231G06F21/32G06K9/00335H04L63/0861
    • Embodiments of the present invention relate to systems, methods, and computer storage media for identifying, authenticating, and authorizing a user to a device. A dynamic image, such as a video captured by a depth camera, is received. The dynamic image provides data from which geometric information of a portion of a user may be identified as well as motion information of a portion of the user may be identified. Consequently, a geometric attribute is identified from the geometric information. A motion attribute may also be identified from the motion information. The geometric attribute is compared to one or more geometric attributes associated with authorized users. Additionally, the motion attribute may be compared to one or more motion attributes associated with the authorized users. A determination may be made that the user is an authorized user. As such the user is authorized to utilize functions of the device.
    • 本发明的实施例涉及用于识别,认证和授权用户到设备的系统,方法和计算机存储介质。 接收动态图像,例如由深度相机拍摄的视频。 动态图像提供可以识别用户的一部分的几何信息的数据,并且可以识别用户的一部分的运动信息。 因此,从几何信息中识别几何属性。 也可以根据运动信息识别运动属性。 将几何属性与与授权用户相关联的一个或多个几何属性进行比较。 另外,运动属性可以与与授权用户相关联的一个或多个运动属性进行比较。 可以确定用户是授权用户。 因此,用户被授权利用该设备的功能。
    • 2. 发明申请
    • OMNI-SPATIAL GESTURE INPUT
    • US20130082978A1
    • 2013-04-04
    • US13250532
    • 2011-09-30
    • Eric HorvitzKenneth P. HinckleyHrvoje BenkoDesney S. Tan
    • Eric HorvitzKenneth P. HinckleyHrvoje BenkoDesney S. Tan
    • G06F3/042G06F3/041
    • G06F3/017G06F3/0346G06F2203/0381
    • Embodiments of the present invention relate to systems, methods and computer storage media for detecting user input in an extended interaction space of a device, such as a handheld device. The method and system allow for utilizing a first sensor of the device sensing in a positive z-axis space of the device to detect a first input, such as a user's non-device-contacting gesture. The method and system also contemplate utilizing a second sensor of the device sensing in a negative z-axis space of the device to detect a second input. Additionally, the method and system contemplate updating a user interface presented on a display in response to detecting the first input by the first sensor in the positive z-axis space and detecting the second input by the second sensor in the negative z-axis space.
    • 本发明的实施例涉及用于检测诸如手持设备的设备的扩展交互空间中的用户输入的系统,方法和计算机存储介质。 所述方法和系统允许利用设备的正z轴空间中感测的设备的第一传感器来检测诸如用户的非设备接触姿态的第一输入。 该方法和系统还考虑利用设备的负Z轴空间中感测的设备的第二传感器来检测第二输入。 此外,该方法和系统考虑响应于在正z轴空间中检测到第一传感器的第一输入并且在负z轴空间中检测第二传感器的第二输入,来更新呈现在显示器上的用户界面。
    • 6. 发明授权
    • Interactive concept learning in image search
    • 图像搜索中的互动概念学习
    • US09008446B2
    • 2015-04-14
    • US13429342
    • 2012-03-24
    • Desney S. TanAshish KapoorSimon A. J. WinderJames A. Fogarty
    • Desney S. TanAshish KapoorSimon A. J. WinderJames A. Fogarty
    • G06K9/62G06F17/30
    • G06F17/30247G06K9/6215
    • An interactive concept learning image search technique that allows end-users to quickly create their own rules for re-ranking images based on the image characteristics of the images. The image characteristics can include visual characteristics as well as semantic features or characteristics, or may include a combination of both. End-users can then rank or re-rank any current or future image search results according to their rule or rules. End-users provide examples of images each rule should match and examples of images the rule should reject. The technique learns the common image characteristics of the examples, and any current or future image search results can then be ranked or re-ranked according to the learned rules.
    • 一种交互式概念学习图像搜索技术,允许最终用户基于图像的图像特征快速创建自己的重新排序图像的规则。 图像特征可以包括视觉特征以及语义特征或特征,或者可以包括两者的组合。 然后,最终用户可以根据其规则或规则对当前或将来的图像搜索结果进行排名或重新排序。 最终用户提供每个规则应该匹配的图像的示例以及规则应该拒绝的图像的示例。 该技术学习示例的常见图像特征,然后可以根据学习的规则对任何当前或将来的图像搜索结果进行排名或重新排序。
    • 9. 发明授权
    • Interactive visualization for generating ensemble classifiers
    • 用于生成综合分类器的交互式可视化
    • US08306940B2
    • 2012-11-06
    • US12408663
    • 2009-03-20
    • Bongshin LeeAshish KapoorDesney S. TanJustin Talbot
    • Bongshin LeeAshish KapoorDesney S. TanJustin Talbot
    • G06F15/18G06F17/00G06N5/04
    • G06N99/005
    • A real-time visual feedback ensemble classifier generator and method for interactively generating an optimal ensemble classifier using a user interface. Embodiments of the real-time visual feedback ensemble classifier generator and method use a weight adjustment operation and a partitioning operation in the interactive generation process. In addition, the generator and method include a user interface that provides real-time visual feedback to a user so that the user can see how the weight adjustment and partitioning operation affect the overall accuracy of the ensemble classifier. Using the user interface and the interactive controls available on the user interface, a user can iteratively use one or both of the weigh adjustment operation and partitioning operation to generate an optimized ensemble classifier.
    • 一种实时视觉反馈综合分类器生成器和方法,用于使用用户界面交互式生成最佳集合分类器。 实时视觉反馈综合分类器发生器和方法的实施例在交互式生成过程中使用权重调整操作和分割操作。 此外,发生器和方法包括向用户提供实时视觉反馈的用户界面,使得用户可以看到权重调整和分割操作如何影响整体分类器的整体精度。 使用用户界面上的用户界面和交互式控件,用户可以迭代地使用权重调整操作和分区操作中的一个或两者来生成优化的整体分类器。