Probability of accidental garment match
    4.
    发明申请
    Probability of accidental garment match 审中-公开
    意外服装搭配的概率

    公开(公告)号:US20140219513A1

    公开(公告)日:2014-08-07

    申请号:US14258671

    申请日:2014-04-22

    IPC分类号: G06K9/62

    摘要: A digital image of a first garment having one or more first garment portions is received. A user has identified the first garment portions as matching one or more corresponding second garment portions of a second garment. The probability of accidental match of the first garment within the digital image in relation to the second garment is determined, by using a statistical model based on one or more parameters and based on analyses of the first garment portions. The probability of accidental match is output.

    摘要翻译: 接收具有一个或多个第一衣服部分的第一衣服的数字图像。 用户已经将第一衣服部分识别为匹配第二衣服的一个或多个对应的第二衣服部分。 通过使用基于一个或多个参数的统计模型并且基于第一衣服部分的分析来确定数字图像中与第二衣服相关的第一衣服的意外匹配的概率。 输出意外匹配的概率。

    Road sign detection and tracking within field-of-view (FOV) video data
    5.
    发明申请
    Road sign detection and tracking within field-of-view (FOV) video data 审中-公开
    视场(FOV)视频数据中的道路标志检测和跟踪

    公开(公告)号:US20130034261A1

    公开(公告)日:2013-02-07

    申请号:US13649644

    申请日:2012-10-11

    IPC分类号: G06K9/00

    CPC分类号: G06K9/00818

    摘要: Road signs are recognized within field-of-view (FOV) video data having frames. Within a first stage, one or more candidate road signs within the FOV video data are identified, by statically analyzing each frame of the FOV video data independently to detect the one or more candidate road signs within the FOV video data. Within a second stage, each candidate road sign is confirmed or rejected as an actual candidate road sign within the FOV video data by dynamically analyzing the frames of the FOV video data interdependently. The first stage is a static analysis that considers each frame of the FOV video data independently. The second stage is a dynamic analysis that considers the frames of the FOV video data interdependently.

    摘要翻译: 在具有帧的视场(FOV)视频数据中识别道路标志。 在第一阶段内,通过静态分析FOV视频数据的每一帧独立地检测FOV视频数据中的一个或多个候选道路标志,来识别FOV视频数据内的一个或多个候选路标。 在第二阶段内,通过相互依赖地动态地分析FOV视频数据的帧,在FOV视频数据内确认或拒绝每个候选路牌作为实际的候选路标。 第一阶段是独立考虑FOV视频数据的每个帧的静态分析。 第二阶段是动态分析,视频视频数据帧相互依赖。

    Dot templates for object detection in images
    8.
    发明授权
    Dot templates for object detection in images 有权
    用于图像中物体检测的点模板

    公开(公告)号:US08712112B2

    公开(公告)日:2014-04-29

    申请号:US13709272

    申请日:2012-12-10

    摘要: Dot templates are used for detecting objects within images. A computer-implemented method is performed for each of a number of dot templates corresponding to the object to be detected within an image. Each dot template is defined as a collection of points. At each position within the image, a value of the image is determined at each point of the dot template. The dot template is effectively overlaid at the given position within the image. A score of the dot template at this position is determined, based on the values of the image determined at the points of the dot template. Where the score is greater than a predetermined threshold, it can be concluded that the object is at least potentially located within the image at the position in question at which the dot template has been effectively overlaid.

    摘要翻译: 点模板用于检测图像内的对象。 对与图像内的要检测对象相对应的多个点模板中的每一个执行计算机实现的方法。 每个点模板被定义为点的集合。 在图像内的每个位置,在点模板的每个点处确定图像的值。 点模板有效地覆盖在图像中的给定位置。 基于在点模板的点处确定的图像的值来确定该位置处的点模板的得分。 在分数大于预定阈值的情况下,可以得出结论,该对象至少可能位于所述位置处的图像内,其中点模板已被有效地覆盖。

    Dot templates for object detection in images

    公开(公告)号:US20130094757A1

    公开(公告)日:2013-04-18

    申请号:US13709272

    申请日:2012-12-10

    IPC分类号: G06K9/00

    摘要: Dot templates are used for detecting objects within images. A computer-implemented method is performed for each of a number of dot templates corresponding to the object to be detected within an image. Each dot template is defined as a collection of points. At each position within the image, a value of the image is determined at each point of the dot template. The dot template is effectively overlaid at the given position within the image. A score of the dot template at this position is determined, based on the values of the image determined at the points of the dot template. Where the score is greater than a predetermined threshold, it can be concluded that the object is at least potentially located within the image at the position in question at which the dot template has been effectively overlaid.