METHOD AND APPARATUS FOR CONVERTING 2D VIDEO IMAGE INTO 3D VIDEO IMAGE
    3.
    发明申请
    METHOD AND APPARATUS FOR CONVERTING 2D VIDEO IMAGE INTO 3D VIDEO IMAGE 有权
    将2D视频图像转换为3D视频图像的方法和装置

    公开(公告)号:US20130266212A1

    公开(公告)日:2013-10-10

    申请号:US13859634

    申请日:2013-04-09

    CPC classification number: H04N13/128 H04N13/261 H04N13/271

    Abstract: A method of converting a two-dimensional video to a three-dimensional video, the method comprising: comparing an image of an nth frame with an accumulated image until an n−1th frame in the two-dimensional video to calculate a difference in a color value for each pixel; generating a difference image including information on a change in a color value for each pixel of the nth frame; storing an accumulated image until the nth frame by accumulating the information on the change in the color value for each pixel until the nth frame; performing an operation for a pixel in which a change in a color value is equal to or larger than a predetermined level by using the difference image to generate a division image and a depth map image; and converting the image of the nth frame to a three-dimensional image by using the depth map image.

    Abstract translation: 一种将二维视频转换为三维视频的方法,所述方法包括:将第n帧的图像与累积图像进行比较,直到二维视频中的第n-1帧,以计算颜色差异 每个像素的值; 生成包括关于第n帧的每个像素的颜色值的变化的信息的差分图像; 通过累积关于每个像素的颜色值的变化的信息直到第n帧来存储累积图像直到第n帧; 通过使用差分图像对颜色值的变化等于或大于预定水平的像素进行操作以产生分割图像和深度图图像; 以及通过使用深度图图像将第n帧的图像转换为三维图像。

    METHOD AND APPARATUS FOR LEARNING DEPENDENCY OF FEATURE DATA

    公开(公告)号:US20240177473A1

    公开(公告)日:2024-05-30

    申请号:US18496063

    申请日:2023-10-27

    CPC classification number: G06V10/82 G06T7/75 G06V10/7715 G06T2207/30196

    Abstract: A neural network device for learning dependency of feature data includes: a memory in which at least one program is stored; and a processor that performs a calculation by executing the at least one program, in which the processor is configured to acquire graph information including a data node for a human body; extract feature data corresponding to a plurality of joints constituting the human body from the graph information; acquire a self-attention output corresponding to the feature data based on a self-attention mechanism; and generate result data for a motion of the human body based on the self-attention output, and the self-attention output includes position information acquired based on positional encoding of the feature data and structural information acquired based on geodesic encoding of the feature data.

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