REAL-TIME DETECTION OF ARTIFACTS IN OPHTHALMIC IMAGES

    公开(公告)号:US20240315552A1

    公开(公告)日:2024-09-26

    申请号:US18680678

    申请日:2024-05-31

    Applicant: Alcon Inc.

    Abstract: Certain aspects of the present disclosure provide a system for processing image data from an intraoperative diagnostic device in real-time during an ophthalmic procedure. The system comprises an image capture element that captures a grayscale image of a first size and an image processing element that scales the grayscale image from the first size to a second size. The system also comprises a two-stage classification model comprising: a feature extraction stage to process the scaled grayscale image and generate a feature vector based on the scaled grayscale image and a classification stage to process the feature vector and generate an output vector. The image processing element is further configured to determine an image quality of the obtained grayscale image based on the output vector for display to an operator and the image quality of the obtained grayscale image indicates a probability that the obtained grayscale image includes an artifact.

    Material Detection in X-Ray Security Screening

    公开(公告)号:US20240302300A1

    公开(公告)日:2024-09-12

    申请号:US18667352

    申请日:2024-05-17

    Abstract: A method for detecting the maximum potential presence of a material in an object. The method includes obtaining raw x-ray image data comprising a plurality of pixels for the object from an X-ray scanning device, wherein each pixel of the plurality of pixels has associated therewith an attenuation value and an effective atomic number (Zeff) for the pixel. The method further includes converting, for each pixel having a Zeff value greater than a threshold effective atomic number (Zeff-threshold), the Zeff at the pixel to the Zeff-threshold while applying a correction factor to the attenuation value for the pixel to bring the attenuation value into correspondence with the conversion of the Zeff value for the pixel and determining a maximum potential amount of the material present at each pixel based on the corrected attenuation value at the pixel. This renders material more apparent in visual display.

    NEURAL NETWORK-BASED IMAGE PROCESSING WITH ARTIFACT COMPENSATION

    公开(公告)号:US20240289922A1

    公开(公告)日:2024-08-29

    申请号:US18430431

    申请日:2024-02-01

    Applicant: Apple Inc.

    Abstract: Methods and systems include neural network-based image processing and blending circuitry to blend an output of the neural network to compensate for potential artifacts from the neural network-based image processing. The neural network(s) apply image processing to image data using one or more neural networks as processed data. Enhance circuitry enhances the image data in a scaling circuitry to generate enhanced data. Blending circuitry receives the processed image data and the enhanced data along with an image plane of the processed data. The blending circuitry also determines whether the image processing using the one or more neural networks has applied a change to the image data greater than a threshold amount. The blending circuitry then, based at least in part in response to the change being greater than the threshold amount and/or edge information of the image data, blends the processed data with the enhanced data.

    Apparatus and method for generating depth map from multi-view image

    公开(公告)号:US12073576B2

    公开(公告)日:2024-08-27

    申请号:US17675415

    申请日:2022-02-18

    Inventor: Han-Shin Lim

    Abstract: Disclosed herein is a method for generating a depth map from multi-view images, the method including receiving multi-view images and parameters thereof; generating an initial depth map, an initial normal map, and an initial reliability map for the multi-view images; performing selection of multiple initial vertices and triangulation based on the initial depth map and the initial normal map and generating depth values and normal values for the result of triangulation; performing regularization based on triangulation, selecting additional vertices in the initial depth map, and generating additional depth values and additional normal values based on the additional vertices and regularization; and refining the initial depth map into a refined depth map by replacing depth values and normal values having reliability below a preset level in the initial depth map and the initial normal map with the additional depth values and the additional normal values.

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