Embedding space for images with multiple text labels

    公开(公告)号:US10026020B2

    公开(公告)日:2018-07-17

    申请号:US14997011

    申请日:2016-01-15

    Abstract: Embedding space for images with multiple text labels is described. In the embedding space both text labels and image regions are embedded. The text labels embedded describe semantic concepts that can be exhibited in image content. The embedding space is trained to semantically relate the embedded text labels so that labels like “sun” and “sunset” are more closely related than “sun” and “bird”. Training the embedding space also includes mapping representative images, having image content which exemplifies the semantic concepts, to respective text labels. Unlike conventional techniques that embed an entire training image into the embedding space for each text label associated with the training image, the techniques described herein process a training image to generate regions that correspond to the multiple text labels. The regions of the training image are then embedded into the training space in a manner that maps the regions to the corresponding text labels.

    Patch partitions and image processing

    公开(公告)号:US09978129B2

    公开(公告)日:2018-05-22

    申请号:US15707418

    申请日:2017-09-18

    Abstract: Patch partition and image processing techniques are described. In one or more implementations, a system includes one or more modules implemented at least partially in hardware. The one or more modules are configured to perform operations including grouping a plurality of patches taken from a plurality of training samples of images into respective ones of a plurality of partitions, calculating an image processing operator for each of the partitions, determining distances between the plurality of partitions that describe image similarity of patches of the plurality of partitions, one to another, and configuring a database to provide the determined distance and the image processing operator to process an image in response to identification of a respective partition that corresponds to a patch taken from the image.

    DYNAMIC FONT SIMILARITY
    55.
    发明申请

    公开(公告)号:US20170262414A1

    公开(公告)日:2017-09-14

    申请号:US15067108

    申请日:2016-03-10

    CPC classification number: G06F17/214 G06N3/0454

    Abstract: Embodiments of the present invention are directed at providing a font similarity system. In one embodiment, a new font is detected on a computing device. In response to the detection of the new font, a pre-computed font list is checked to determine whether the new font is included therein. The pre-computed font list including feature representations, generated independently of the computing device, for corresponding fonts. In response to a determination that the new font is absent from the pre-computed font list, a feature representation for the new font is generated. The generated feature representation capable of being utilized for a similarity analysis of the new font. The feature representation is then stored in a supplemental font list to enable identification of one or more fonts installed on the computing device that are similar to the new font. Other embodiments may be described and/or claimed.

    Random Sample Consensus for Groups of Data
    56.
    发明申请

    公开(公告)号:US20170228613A1

    公开(公告)日:2017-08-10

    申请号:US15494106

    申请日:2017-04-21

    Inventor: Hailin Jin Kai Ni

    Abstract: In one embodiment, a computer accessible storage medium stores a plurality of instructions which, when executed: group a set of reconstructed three dimensional (3D) points derived from image data into a plurality of groups based on one or more attributes of the 3D points; select one or more groups from the plurality of groups; and sample data from the selected groups, wherein the sampled data is input to a consensus estimator to generate a model that describes a 3D model of a scene captured by the image data. Other embodiments may bias sampling into a consensus estimator for any data set, based on relative quality of the data set.

    Plane-based self-calibration for structure from motion
    60.
    发明授权
    Plane-based self-calibration for structure from motion 有权
    基于平面的自校准结构从运动

    公开(公告)号:US09552639B2

    公开(公告)日:2017-01-24

    申请号:US14884338

    申请日:2015-10-15

    Abstract: Robust techniques for self-calibration of a moving camera observing a planar scene. Plane-based self-calibration techniques may take as input the homographies between images estimated from point correspondences and provide an estimate of the focal lengths of all the cameras. A plane-based self-calibration technique may be based on the enumeration of the inherently bounded space of the focal lengths. Each sample of the search space defines a plane in the 3D space and in turn produces a tentative Euclidean reconstruction of all the cameras that is then scored. The sample with the best score is chosen and the final focal lengths and camera motions are computed. Variations on this technique handle both constant focal length cases and varying focal length cases.

    Abstract translation: 用于自动校准移动摄像机观察平面场景的强大技术。 基于平面的自校准技术可以将从点对应估计的图像之间的同形作为输入,并提供所有相机的焦距的估计。 基于平面的自校准技术可以基于焦距的固有界限空间的计数。 搜索空间的每个样本在3D空间中定义一个平面,并且反过来产生所有相机的临时欧几里德重建,然后对其进行评分。 选择具有最佳分数的样本,并计算最终焦距和相机运动。 该技术的变化处理恒定焦距情况和不同焦距情况。

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