System and method for image composition thereof
    1.
    发明授权
    System and method for image composition thereof 有权
    用于图像组成的系统和方法

    公开(公告)号:US09547802B2

    公开(公告)日:2017-01-17

    申请号:US14261211

    申请日:2014-04-24

    Abstract: This disclosure provides an image composition system and its method. The system includes: an image capturing unit configured for capturing at least one first image of a real object; a depth information generating unit disposed on the image capturing unit and configured for measuring a depth information between the depth information generating unit and the real object; a computing unit receiving the at least one first image and the depth information, performing the computation of removing a noise caused by a moving object other than the real object from the received depth information, and computing a moving trajectory of the image capturing unit; and a composition unit compositing the at least one first image and a second image of a virtual scene, and display the composited result on a display unit.

    Abstract translation: 本公开提供了一种图像组合系统及其方法。 该系统包括:图像拍摄单元,被配置为捕获真实物体的至少一个第一图像; 深度信息生成单元,其设置在所述图像拍摄单元上,并且被配置为测量所述深度信息生成单元和所述真实对象之间的深度信息; 接收所述至少一个第一图像和所述深度信息的计算单元,从所接收的深度信息执行去除由所述真实物体以外的移动物体引起的噪声的计算,以及计算所述图像拍摄单元的移动轨迹; 以及组合单元,将所述至少一个第一图像和所述虚拟场景的第二图像合成,并将所述合成结果显示在显示单元上。

    Stereo camera apparatus, self-calibration apparatus and calibration method
    4.
    发明授权
    Stereo camera apparatus, self-calibration apparatus and calibration method 有权
    立体相机装置,自校准装置和校准方法

    公开(公告)号:US09445080B2

    公开(公告)日:2016-09-13

    申请号:US13945931

    申请日:2013-07-19

    CPC classification number: H04N13/246 H04N13/239

    Abstract: A stereo camera apparatus including an image capturing device, an optical axis controlling module and a calculating module is provided. The image capturing device is suitable for obtaining a stereo image, and the image capturing device includes a plurality of image capturing units. The optical axis controlling module is coupled to the image capturing device. The calculating module is coupled to the image capturing device and the optical axis controlling module, wherein the calculating module calculates a calibration condition according to the stereo image. The optical axis controlling module adjusts directions of imaging optical axes of the image capturing units. After being adjusted by the optical axis controlling modules, the imaging optical axes of the image capturing units are aligned. Besides, a self-calibration apparatus and a method of calibration are also provided.

    Abstract translation: 提供了包括图像捕获装置,光轴控制模块和计算模块的立体相机装置。 图像捕获装置适用于获得立体图像,并且图像捕获装置包括多个图像捕获单元。 光轴控制模块耦合到图像捕获装置。 计算模块耦合到图像捕获装置和光轴控制模块,其中计算模块根据立体图像计算校准条件。 光轴控制模块调整摄像单元的成像光轴的方向。 在通过光轴控制模块调节之后,图像捕获单元的成像光轴对齐。 此外,还提供了自校准装置和校准方法。

    STEREO CAMERA APPARATUS, SELF-CALIBRATION APPARATUS AND CALIBRATION METHOD
    5.
    发明申请
    STEREO CAMERA APPARATUS, SELF-CALIBRATION APPARATUS AND CALIBRATION METHOD 有权
    立体相机设备,自校准设备和校准方法

    公开(公告)号:US20140118503A1

    公开(公告)日:2014-05-01

    申请号:US13945931

    申请日:2013-07-19

    CPC classification number: H04N13/246 H04N13/239

    Abstract: A stereo camera apparatus including an image capturing device, an optical axis controlling module and a calculating module is provided. The image capturing device is suitable for obtaining a stereo image, and the image capturing device includes a plurality of image capturing units. The optical axis controlling module is coupled to the image capturing device. The calculating module is coupled to the image capturing device and the optical axis controlling module, wherein the calculating module calculates a calibration condition according to the stereo image. The optical axis controlling module adjusts directions of imaging optical axes of the image capturing units. After being adjusted by the optical axis controlling modules, the imaging optical axes of the image capturing units are aligned. Besides, a self-calibration apparatus and a method of calibration are also provided.

    Abstract translation: 提供了包括图像捕获装置,光轴控制模块和计算模块的立体相机装置。 图像捕获装置适用于获得立体图像,并且图像捕获装置包括多个图像捕获单元。 光轴控制模块耦合到图像捕获装置。 计算模块耦合到图像捕获装置和光轴控制模块,其中计算模块根据立体图像计算校准条件。 光轴控制模块调整摄像单元的成像光轴的方向。 在通过光轴控制模块调节之后,图像捕获单元的成像光轴对齐。 此外,还提供了自校准装置和校准方法。

    METHOD AND APPARATUS FOR EQUIPMENT ANOMALY DETECTION

    公开(公告)号:US20230367306A1

    公开(公告)日:2023-11-16

    申请号:US18173093

    申请日:2023-02-23

    CPC classification number: G05B23/0283 G06N20/00

    Abstract: A method and an apparatus for equipment anomaly detection are provided. In the method, multiple signals of an equipment during normal operation or appearance images of the equipment when an appearance is not damaged are acquired in advance by using a data acquisition device to train a machine learning model stored in a storage device. A real-time signal of the equipment during a current operation or a current image of the appearance of the equipment is acquired by using the data acquisition device, and input to the trained machine learning model to output a detection result indicating a current operation state of the equipment or a current state of the appearance of the equipment.

    CLASSIFICATION DEVICE AND CLASSIFICATION METHOD BASED ON NEURAL NETWORK

    公开(公告)号:US20220114419A1

    公开(公告)日:2022-04-14

    申请号:US17121763

    申请日:2020-12-15

    Abstract: A classification device and a classification method based on a neural network are provided. A heterogeneous integration module includes a convolutional layer, a data normalization layer, a connected layer and a classification layer. The convolutional layer generates a first feature map according to a first image data. The data normalization layer normalizes a first numerical data to generate a first normalized numerical data. The first numerical data corresponds to the first image data. The connected layer generates a first feature vector according to the first feature map and the first normalized numerical data. The classification layer generates a first classification result corresponding to a first time point according to the first feature vector. The heterogeneous integration module generates a second classification result corresponding to a second time point. A recurrent neural network generates a third classification result according to the first classification result and the second classification result.

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