METHOD FOR TRAINING DEFECTIVE-SPOT DETECTION MODEL, METHOD FOR DETECTING DEFECTIVE-SPOT, AND METHOD FOR RESTORING DEFECTIVE-SPOT

    公开(公告)号:US20250037255A1

    公开(公告)日:2025-01-30

    申请号:US18282589

    申请日:2022-10-28

    Inventor: Dan ZHU Ran DUAN

    Abstract: The present disclosure provides a defective-spot detection model training method, a defective-spot detection method and a defective-spot restoration method. The defective-spot detection model training method includes: obtaining a first training data set and a second training data set; generating a transparent layer based on a resolution of the sample detection image; replacing an image in a certain region of the transparent layer based on at least one of the plurality of frames of sample defective-spot images to generate a frame of transparent mask; generating a sample training image with a defective-spot based on the frame of transparent mask and the sample detection image; and processing the sample detection image by using the at least one of the plurality of frames of sample defective-spot images to generate a frame of sample training image.

    METHODS AND SYSTEMS FOR USING COMPACT OBJECT IMAGE DATA TO CONSTRUCT A MACHINE LEARNING MODEL FOR POSE ESTIMATION OF AN OBJECT

    公开(公告)号:US20250022184A1

    公开(公告)日:2025-01-16

    申请号:US18221032

    申请日:2023-07-12

    Abstract: An illustrative model construction system may access object image data representative of one or more images depicting an object having a plurality of labeled keypoint features. Based on the object image dataset, the model construction system may generate a training target dataset including a plurality of training target images. Each training target image may be generated by selecting a background image distinct from the object image data, manipulating a depiction of the object represented within the object image data, and overlaying the manipulated depiction of the object onto the selected background image with the labeled keypoint features. Based on this training target dataset, the model construction system may train a machine learning model to recognize and estimate a pose of the object when the object is depicted in input images analyzed using the trained machine learning model. Corresponding methods and systems are also disclosed.

    CONTROL APPARATUS AND CONTROL METHOD

    公开(公告)号:US20250013311A1

    公开(公告)日:2025-01-09

    申请号:US18883358

    申请日:2024-09-12

    Abstract: In a control apparatus, a reference point setting unit receives spatial coordinates of each of a first part, a second part, and a third part of a body of a user located in front of a display, identifies a midpoint between the spatial coordinates of the first part and the spatial coordinates of the second part, and sets a point on the display intersecting a virtual straight line passing through the midpoint identified and the spatial coordinates of the third part as a reference point. A function execution unit performs a process, determined by a movement of the first part and the second part, on an image displayed on the display, with reference to the reference point set.

    System and Method for Generating Training Images for Training a Model to Classify Grains in a Sample Image and Identification of Impurities in a Sample Image

    公开(公告)号:US20250005830A1

    公开(公告)日:2025-01-02

    申请号:US18756715

    申请日:2024-06-27

    Abstract: Images for training a grain detection model are generated by: providing a starting image, repeatedly selecting a polygon from annotated sample images of known polygons and modifying the starting image by placing the selected polygons in the starting image so that the resulting image comprises a random assortment of known polygons from different sources. Uniformly annotated grain sample images can thus be introduced to a in modified images without introducing bias to the model. In a further aspect, selected objects in a grain sample can be sized based on sizing apertures of a sizing device by defining a sizing polygon representing the sizing apertures and then iteratively positioning the selected object polygon relative to the sizing polygon so that the overlap amount can be compared to a threshold to distinguish grains from impurities without actually passing the grains through the sizing device.

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