中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

21 records (took 0.018 seconds)

    Joint enhancement of lightness, color and contrast of images and video
    12.
    发明授权
    Joint enhancement of lightness, color and contrast of images and video 有权
    联合增强图像和视频的亮度，颜色和对比度

    公开(公告)号：US09053523B2

    公开(公告)日：2015-06-09

    申请号：US13923451

    申请日：2013-06-21

    Applicant: Intel Corporation

    Inventor： Abhijit Sarkar ,  Jorge E. Caviedes ,  Mahesh Subedar

    IPC: H04N5/45 ,  G06T5/00 ,  H04N1/60

    CPC classification number: G06T5/00 ,  G06T5/003 ,  G06T5/007 ,  G06T2207/10016 ,  G06T2207/10024 ,  H04N1/6005 ,  H04N1/6027 ,  H04N1/608

    Abstract: In some embodiments, color and contrast enhancement video processing may be done in one shot instead of adjusting one of color and contrast enhancement, then the other, and then going back to the first one to readjust because of the second adjustment. In some embodiments, global lightness adjustment, local contrast enhancement, and saturation enhancement may be done at the same time and in parallel. Lightness adjustment improves visibility of details for generally dark or generally light images without changing intended lighting conditions in the original shot, and is used to enhance the range of color/saturation enhancement. Local contrast enhancement done in parallel improves visual definition of objects and textures and thus local contrast and perceived sharpness.

    Abstract translation: 在一些实施例中，可以一次进行颜色和对比度增强视频处理，而不是调整颜色和对比度增强之一，然后调整另一个，然后返回到第一个以由于第二调整而重新调整。 在一些实施例中，可以同时并行地进行全局亮度调整，局部对比度增强和饱和度增强。 亮度调整提高了一般黑暗或一般较轻的图像的细节的可视性，而不改变原始照片中的预期照明条件，并且用于增强色彩/饱和度增强的范围。 并行的局部对比度增强可以提高对象和纹理的视觉定义，从而提高局部对比度和感知锐度。

    METHODS AND APPARATUS TO DETECT ANOMALIES IN VIDEO DATA
    15.
    发明申请
    METHODS AND APPARATUS TO DETECT ANOMALIES IN VIDEO DATA 有权

    公开(公告)号：US20240420468A1

    公开(公告)日：2024-12-19

    申请号：US18821328

    申请日：2024-08-30

    Applicant: Intel Corporation

    Inventor： Jiaxiang Jiang ,  Omesh Tickoo ,  Mahesh Subedar ,  Ibrahima Jacques Ndiour

    IPC: G06V20/40 ,  G06V10/25 ,  G06V10/77

    Abstract: Methods and apparatus to detect anomalies in video data are disclosed. An example apparatus disclosed herein generates a reconstructed feature vector corresponding to an input feature vector representative of a video segment, the reconstructed feature vector based on a transformation applied to the input feature vector and an inverse of the transformation applied to an output of the transformation, the input feature vector and the reconstructed feature vector including features associated with a plurality of dimensions including a time dimension. The disclosed example apparatus also generates an error vector based on a difference between the input feature vector and the reconstructed feature vector. The disclosed example apparatus further generates an anomaly map based on sums of elements of the error vector across at least the time dimension, the anomaly map corresponding to the video segment.

    OUT-OF-DISTRIBUTION DETECTION USING A NEURAL NETWORK
    17.
    发明公开
    OUT-OF-DISTRIBUTION DETECTION USING A NEURAL NETWORK 审中-公开

    公开(公告)号：US20230298322A1

    公开(公告)日：2023-09-21

    申请号：US18325436

    申请日：2023-05-30

    Applicant: Intel Corporation

    Inventor： Ibrahima Ndiour ,  Nilesh Ahuja ,  Ranganath Krishnan ,  Mahesh Subedar ,  Omesh Tickoo ,  Ergin Genc

    IPC: G06V10/77 ,  G06V10/82 ,  G06V10/80

    CPC classification number: G06V10/7715 ,  G06V10/82 ,  G06V10/80

    Abstract: Features extracted from one or more layers of a trained deep neural network (DNN) are used to detect out-of-distribution (OOD) data, such as anomalies. An OOD detection process includes transforming a feature output from a layer of the DNN from a relatively high-dimensional feature space to a lower-dimensional space, and then performing a reverse transformation back to the higher-dimensional feature space, resulting in a reconstructed feature. A feature reconstruction error is calculated based on a difference between the reconstructed feature and the original feature output from the DNN. The OOD detection process may further include calculating a score based on the feature reconstruction error and generating a visual representation of the feature reconstruction error.

    METHODS AND APPARATUS TO FACILITATE CONTINUOUS LEARNING
    18.
    发明申请
    METHODS AND APPARATUS TO FACILITATE CONTINUOUS LEARNING 有权

    公开(公告)号：US20210117792A1

    公开(公告)日：2021-04-22

    申请号：US17132858

    申请日：2020-12-23

    Applicant: Intel Corporation

    Inventor： Nilesh Ahuja ,  Mahesh Subedar ,  Ranganath Krishnan ,  Ibrahima Ndiour ,  Omesh Tickoo

    IPC: G06N3/08 ,  G06N3/04 ,  G06F7/58

    Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate continuous learning. An example apparatus includes a trainer to train a first Bayesian neural network (BNN) and a second BNN, the first BNN associated with a first weight distribution and the second BNN associated with a second weight distribution. The example apparatus includes a weight determiner to determine a first sampling weight associated with the first BNN and a second sampling weight associated with the second BNN. The example apparatus includes a network sampler to sample at least one of the first weight distribution or the second weight distribution based on a pseudo-random number, the first sampling weight, and the second sampling weight. The example apparatus includes an inference controller to generate an ensemble weight distribution based on the sample.

Patent Agency Ranking