Fei Wu

Fei Wu, Ph.D.

Researcher

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room: Room 09.158
Martensstr. 3
91058 Erlangen

ORCID iD icon  https://orcid.org/0000-0003-4196-0289

Academic CV

  • Since 09/2023:
    Postdoctoral researcher at the Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • 09/2017 – 07/2023:
    Doctor of Philosophy, School of Electrical, Electronics and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China.
  • 09/2013 – 07/2017:
    Bachelor of Engineering, School of Computer Science and Engineering, Central South University, Changsha, China.

Projects

2023

  • Research on handwriting analysis, object tracking and segmentation based on machine learning

    (Own Funds)

    Term: November 16, 2023 - December 1, 2024

    This project aims at exploring the potential of Convolution Neural Networks (CNNs) and Vision Transformers (ViTs) in two fundamental tasks of computer vision, i.e., handwriting analysis, object tracking and segmentation. 

    Handwriting document analysis aims to evaluate and recognize the handwritten manuscripts according to different intentions, such as text recognition, spotting, layout analysis, text alignment, and writer recognition. As an important issue in the first step of digitizing scanned documents, this project will focus on layout analysis and line segmentation. 

    Object tracking and segmentation aims at continuously estimating the state of an object based on a given bounding box extracted by a simple rectangle/mask from the initial frame of a video sequence. It is widely applied in various applications such as surveillance, autonomous driving, human-computer interaction, etc. Despite the progress made so far, its main challenge lies in the limited discriminative power of the classifiers. Also, it is prone to the introduced endless distractors in real-world surveillance applications. This project will investigate state-of-the-art algorithms for achieving accurately and stably object tracking and segmentation. 

Publications

2025

Journal Articles

Conference Contributions

2024

Journal Articles

Conference Contributions

2023

Journal Articles

2020

Journal Articles

Lectures

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