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    Image alignment using a virtual gyroscope model
    11.
    发明授权
    Image alignment using a virtual gyroscope model 有权

    公开(公告)号：US09961261B2

    公开(公告)日：2018-05-01

    申请号：US15249347

    申请日：2016-08-26

    Applicant: GoPro, Inc.

    Inventor： Ryan Lustig ,  Balineedu Chowdary Adsumilli

    IPC: H04N5/225 ,  G06K9/62 ,  H04N5/232

    CPC classification number: H04N5/23258 ,  H04N5/23267 ,  H04N5/2328

    Abstract: A target image captured from a fisheye lens or other lens with known distortion parameters may be transformed to align it to a reference image. Corresponding features may be detected in the target image and the reference image. The features may be transformed to a spherical coordinate space. In the spherical space, images may be re-pointed or rotated in three dimensions to align all or a subset of the features of the target image to the corresponding features of the reference image. For example, in a sequence of images, background features of the target image in the spherical image space may be aligned to background features of the reference image in the spherical image space to compensate for camera motion while preserving foreground motion. An inverse transformation may then be applied to bring the images back into the original image space.

    SCENE AND ACTIVITY IDENTIFICATION IN VIDEO SUMMARY GENERATION BASED ON MOTION DETECTED IN A VIDEO
    14.
    发明申请
    SCENE AND ACTIVITY IDENTIFICATION IN VIDEO SUMMARY GENERATION BASED ON MOTION DETECTED IN A VIDEO 有权
    基于视频中检测到的运动的视频摘要生成中的场景和活动识别

    公开(公告)号：US20160224834A1

    公开(公告)日：2016-08-04

    申请号：US15091539

    申请日：2016-04-05

    Applicant: GOPRO, INC.

    Inventor： Balineedu Chowdary Adsumilli ,  Ryan Lustig ,  David A. Newman

    IPC: G06K9/00 ,  G11B27/13 ,  G11B27/30 ,  G11B27/031

    CPC classification number: G11B27/10 ,  G06K9/00718 ,  G06K9/00751 ,  G06K2009/00738 ,  G10L15/063 ,  G10L15/22 ,  G10L25/54 ,  G10L25/57 ,  G10L2015/0631 ,  G10L2015/223 ,  G11B27/031 ,  G11B27/13 ,  G11B27/28 ,  G11B27/3081 ,  G11B27/34 ,  H04N5/77 ,  H04N5/772 ,  H04N5/91 ,  H04N9/8205

    Abstract: Video and corresponding metadata is accessed. Events of interest within the video are identified based on the corresponding metadata, and best scenes are identified based on the identified events of interest. In one example, best scenes are identified based on the motion values associated with frames or portions of a frame of a video. Motion values are determined for each frame and portions of the video including frames with the most motion are identified as best scenes. Best scenes may also be identified based on the motion profile of a video. The motion profile of a video is a measure of global or local motion within frames throughout the video. For example, best scenes are identified from portion of the video including steady global motion. A video summary can be generated including one or more of the identified best scenes.

    Abstract translation: 访问视频和相应的元数据。 基于相应的元数据来识别视频内感兴趣的事件，并且基于所识别的感兴趣的事件来识别最佳场景。 在一个示例中，基于与帧或帧的视频的一部分相关联的运动值来识别最佳场景。 为每个帧确定运动值，并且包括具有最大运动的帧的视频的部分被识别为最佳场景。 还可以基于视频的运动配置文件来识别最佳场景。 视频的运动曲线是整个视频帧内全局或局部运动的度量。 例如，从包括稳定的全局运动的视频的部分中识别最佳场景。 可以生成包括所识别的最佳场景中的一个或多个的视频摘要。

    Scene and Activity Identification in Video Summary Generation Based on Motion Detected in a Video
    15.
    发明申请
    Scene and Activity Identification in Video Summary Generation Based on Motion Detected in a Video 有权
    基于视频中检测到的运动的视频摘要生成中的场景和活动识别

    公开(公告)号：US20160055381A1

    公开(公告)日：2016-02-25

    申请号：US14705864

    申请日：2015-05-06

    Applicant: GoPro, Inc.

    Inventor： Balineedu Chowdary Adsumilli ,  Ryan Lustig ,  David A. Newman

    IPC: G06K9/00 ,  G11B27/031 ,  G11B27/30

    CPC classification number: G11B27/10 ,  G06K9/00718 ,  G06K9/00751 ,  G06K2009/00738 ,  G10L15/063 ,  G10L15/22 ,  G10L25/54 ,  G10L25/57 ,  G10L2015/0631 ,  G10L2015/223 ,  G11B27/031 ,  G11B27/13 ,  G11B27/28 ,  G11B27/3081 ,  G11B27/34 ,  H04N5/77 ,  H04N5/772 ,  H04N5/91 ,  H04N9/8205

    Abstract: Video and corresponding metadata is accessed. Events of interest within the video are identified based on the corresponding metadata, and best scenes are identified based on the identified events of interest. In one example, best scenes are identified based on the motion values associated with frames or portions of a frame of a video. Motion values are determined for each frame and portions of the video including frames with the most motion are identified as best scenes. Best scenes may also be identified based on the motion profile of a video. The motion profile of a video is a measure of global or local motion within frames throughout the video. For example, best scenes are identified from portion of the video including steady global motion. A video summary can be generated including one or more of the identified best scenes.

    Abstract translation: 访问视频和相应的元数据。 基于相应的元数据来识别视频内感兴趣的事件，并且基于所识别的感兴趣的事件来识别最佳场景。 在一个示例中，基于与帧或帧的视频的一部分相关联的运动值来识别最佳场景。 为每个帧确定运动值，并且包括具有最大运动的帧的视频的部分被识别为最佳场景。 还可以基于视频的运动配置文件来识别最佳场景。 视频的运动曲线是整个视频帧内全局或局部运动的度量。 例如，从包括稳定的全局运动的视频的部分中识别最佳场景。 可以生成包括所识别的最佳场景中的一个或多个的视频摘要。

    IMAGE ALIGNMENT USING A VIRTUAL GYROSCOPE MODEL
    18.
    发明申请
    IMAGE ALIGNMENT USING A VIRTUAL GYROSCOPE MODEL 审中-公开

    公开(公告)号：US20200077022A1

    公开(公告)日：2020-03-05

    申请号：US16538641

    申请日：2019-08-12

    Applicant: GoPro, Inc.

    Inventor： Ryan Lustig ,  Balineedu Chowdary Adsumilli

    IPC: H04N5/232

    Abstract: A target image captured from a fisheye lens or other lens with known distortion parameters may be transformed to align it to a reference image. Corresponding features may be detected in the target image and the reference image. The features may be transformed to a spherical coordinate space. In the spherical space, images may be re-pointed or rotated in three dimensions to align all or a subset of the features of the target image to the corresponding features of the reference image. For example, in a sequence of images, background features of the target image in the spherical image space may be aligned to background features of the reference image in the spherical image space to compensate for camera motion while preserving foreground motion. An inverse transformation may then be applied to bring the images back into the original image space.

    IMAGE ALIGNMENT USING A VIRTUAL GYROSCOPE MODEL
    19.
    发明申请
    IMAGE ALIGNMENT USING A VIRTUAL GYROSCOPE MODEL 审中-公开

    公开(公告)号：US20180316861A1

    公开(公告)日：2018-11-01

    申请号：US15967364

    申请日：2018-04-30

    Applicant: GoPro, Inc.

    Inventor： Ryan Lustig ,  Balineedu Chowdary Adsumilli

    IPC: H04N5/232

    CPC classification number: H04N5/23258 ,  H04N5/23267 ,  H04N5/2328

    Abstract: A target image captured from a fisheye lens or other lens with known distortion parameters may be transformed to align it to a reference image. Corresponding features may be detected in the target image and the reference image. The features may be transformed to a spherical coordinate space. In the spherical space, images may be re-pointed or rotated in three dimensions to align all or a subset of the features of the target image to the corresponding features of the reference image. For example, in a sequence of images, background features of the target image in the spherical image space may be aligned to background features of the reference image in the spherical image space to compensate for camera motion while preserving foreground motion. An inverse transformation may then be applied to bring the images back into the original image space.

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