Patents by Inventor Christos SAKARIDIS

Christos SAKARIDIS has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Method and a system training a model to perform semantic segmentation on foggy images
    Patent number: 11941815
    Abstract: A system and a method for training a model to be used for semantic segmentation of images, comprising: a—obtaining (S01) a first plurality of foggy images (101), b—training (S02) a classification model for estimating fog density, c—classifying (S03) a second plurality of images (101) having light fog, d—obtaining (S04) a third plurality of foggy images (103) having light fog, e—training (S05) a semantic segmentation model using the third plurality of foggy images, f—applying (S06) the semantic segmentation model to the second plurality of foggy (102) images to obtain semantic segmentations (102?), g—obtaining (S07) a fourth plurality of foggy images (104) having dense fog, h—training (S08) using the previously obtained foggy images.
    Type: Grant
    Filed: July 24, 2018
    Date of Patent: March 26, 2024
    Assignees: TOYOTA MOTOR EUROPE, ETH ZURICH
    Inventors: Hiroaki Shimizu, Dengxin Dai, Christos Sakaridis, Luc Van Gool
  • SYSTEM AND METHOD FOR TRAINING A MODEL TO PERFORM SEMANTIC SEGMENTATION ON LOW VISIBILITY IMAGES USING HIGH VISIBILITY IMAGES HAVING A CLOSE CAMERA VIEW
    Publication number: 20220284696
    Abstract: A system and a method for training a semantic segmentation model includes obtaining a plurality of sets of images each having an index z for visibility, iteratively training the model. Iteratively training the model includes (a) for each z above 1, obtaining preliminary semantic segmentation labels for each image of the set of images of index z?1 by applying the model to each image of the set of images of index z?1, (b) processing each preliminary semantic segmentation labels using semantic segmentation labels obtained using the model on a selected image of index 1, and obtaining processed semantic segmentation labels, (c) training the model using the set of images of index z?1 and the associated processed semantic segmentation labels, and (d) performing steps (a) to (c) for z+1.
    Type: Application
    Filed: July 10, 2019
    Publication date: September 8, 2022
    Applicants: TOYOTA MOTOR EUROPE, ETH ZURICH THE SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH
    Inventors: Wim ABBELOOS, Christos SAKARIDIS, Luc VAN GOOL, Dengxin DAI
  • A METHOD AND A SYSTEM TRAINING A MODEL TO PERFORM SEMANTIC SEGMENTATION ON FOGGY IMAGES
    Publication number: 20210158098
    Abstract: A system and a method for training a model to be used for semantic segmentation of images, comprising: a—obtaining (S01) a first plurality of foggy images (101), b—training (S02) a classification model for estimating fog density, c—classifying (S03) a second plurality of images (101) having light fog, d—obtaining (S04) a third plurality of foggy images (103) having light fog, e—training (S05) a semantic segmentation model using the third plurality of foggy images, f—applying (S06) the semantic segmentation model to the second plurality of foggy (102) images to obtain semantic segmentations (102?), g—obtaining (S07) a fourth plurality of foggy images (104) having dense fog, h—training (S08) using the previously obtained foggy images.
    Type: Application
    Filed: July 24, 2018
    Publication date: May 27, 2021
    Applicants: TOYOTA MOTOR EUROPE, ETH ZURICH
    Inventors: Hiroaki SHIMIZU, Dengxin DAI, Christos SAKARIDIS, Luc Van GOOL