Hicham Badri

Hicham Badri

Metropolregion Berlin/Brandenburg
1306 Follower:innen 500+ Kontakte

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Aktivitäten

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Berufserfahrung

  • Mobius Labs GmbH Grafik

    Mobius Labs GmbH

    Berlin Area, Germany

  • -

    Région de Berlin, Allemagne

  • -

    Région de Berlin, Allemagne

  • -

    Région de Bordeaux, France

  • -

    Région de Bordeaux, France

  • -

    Région de Bordeaux, France

Ausbildung

Bescheinigungen und Zertifikate

Veröffentlichungen

  • A Non-Local Low-Rank Approach to Enforce Integrability

    IEEE Transactions on Image Processing

    Andere Autor:innen
    • Hussein Yahia

  • Low-Rankness Transfer for Realistic Denoising

    IEEE Transactions on Image Processing

    Andere Autor:innen
    • Hussein Yahia

  • Fast Edge-Aware Processing via First Order Proximal Approximation

    IEEE Transactions on Visualization and Computer Graphics

    Andere Autor:innen
    • Hussein Yahia

  • Sparse and Scale-Invariant Methods in Image Processing

    Ph.D. Dissertation

    Veröffentlichung anzeigen

  • Fast and Accurate Texture Recognition with Multilayer Convolution and Multifractal Analysis

    ECCV

    Andere Autor:innen
    • Hussein Yahia
    • Khalid Daoudi

  • Handling Noise in Image Deconvolution with Local/Non-Local Priors

    ICIP

    Andere Autor:innen
    • Hussein Yahia

  • Robust Surface Reconstruction via Triple Sparsity

    CVPR

    Andere Autor:innen
    • Hussein Yahia

  • Fast multi-scale detail decomposition via accelerated iterative shrinkage

    SIGGRAPH Asia

    Andere Autor:innen
    • Hussein Yahia

Patente

  • Method and system for generating a training dataset

    Ausgestellt am WO2022022930A1

    Disclosed are methods and systems for generating and using a dataset for training a classifier algorithm. The method comprises inputting a sample dataset into an annotation module; the annotation module ranking a benchmark dataset based on the sample dataset; based on the ranking, the annotation module outputting a subset of the benchmark dataset ranked within a predetermined similarity threshold to the sample dataset; generating a training dataset by adding the subset of the benchmark dataset…

    Disclosed are methods and systems for generating and using a dataset for training a classifier algorithm. The method comprises inputting a sample dataset into an annotation module; the annotation module ranking a benchmark dataset based on the sample dataset; based on the ranking, the annotation module outputting a subset of the benchmark dataset ranked within a predetermined similarity threshold to the sample dataset; generating a training dataset by adding the subset of the benchmark dataset to the sample dataset; a classification module using the training dataset to train the classifier algorithm. The system comprises a database comprising at least a benchmark dataset; an annotation module configured to receive a sample dataset, rank a benchmark dataset based on the sample dataset; based on the ranking, output a subset of the benchmark dataset ranked within a predetermined similarity threshold to the sample dataset, generate a training dataset by adding the subset of the benchmark dataset to the sample dataset; and a classification module configured to use the training dataset to train the classifier algorithm.

    Andere Erfinder:innen
    Patent anzeigen

  • Automated Capturing of Images Comprising a Desired Feature

    Ausgestellt am DE WO/2020/144068

    The present invention relates to a method, system and software for automatically capturing a target image in a movable device. The method comprises at least one of the steps of providing at least one image feature for the target image to be captured; monitoring image data; and capturing at least one target image when the image data monitored fulfill the image feature.

    Andere Erfinder:innen
    Patent anzeigen

Auszeichnungen/Preise

  • Third place in Hackathon 2016 : "Deep Learning Fashion Style Recommendation"

    Visual Meta GmbH

    Building a fashion style recommendation system (Android app) in 3-4 days.
    A deep convolutional neural network is fine-tuned on various clothes (shirts, skirts, jeans, ...) and feature extraction is used to recommend a suitable outfit.

  • AFRIF Prize Best PhD Dissertation Award

    AFRIF : http://www.afrif.asso.fr/?p=334

  • Best Master Student Award

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Sprachen

  • French

    Muttersprache oder zweisprachig

  • Arabic

    Muttersprache oder zweisprachig

  • English

    Verhandlungssicher

  • German

    Grundkenntnisse

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