EE569 Digital Image Processing - Projects

• @Author: Boyang Xiao
• @Email: [email protected]
• @USC id: 3326730274
• @Date of init: 2022-01-30
• @Instructor: Prof. C.-C. Jay Kuo

---

💻 Coding Environments:

• @IDEs & languages：
  • VScode - C++
  • MATLAB
  • PyCharm - Python 3.8
• @Platform： Windows 10/11
• @compiler： g++ --version 8.1.0
• @IDE extensions：
  • C/C++ extensions for VScode
  • Image Processing toolbox for MATLAB

⚠️Please do follow the README.md files in each projects carefully to compile and run the codes!!!

---

👉 Index :

• Project - 1

  • Topics:
    • Image demosaicing
    • Histogram manipulating
    • Image denoising
  • Language: C++
  • Repo: 👉 here
  • Project Report: 👉 here

• Project - 2

  • Topics:
    • Edge detection
    • Digital half-toning
  • Language: MATLAB
  • Repo: 👉 here
  • Project Report: 👉 here

• Project - 3

  • Topics:
    • Geometric image modification
    • Homographic transformation & Image stitching
    • Morphological processing
  • Language: MATLAB
  • Repo: 👉 here
  • Project Report: 👉 here

• Project - 4

  • Topics:
    • Texture Analysis
    • Texture segmentation
    • SIFT and Image matching
  • Language: MATLAB
  • Repo: 👉 here
  • Project Report: 👉 here

• Project - 5

  • Topics:
    • Convolutional Neural Networks (CNN) introductions
    • CNN performance analysis: different datasets
    • CNN performance analysis: confusion classes and hard samples
    • CNN performance analysis: Classification with noisy data
  • Language: Python
  • Repo: 👉 here
  • Project Report: 👉 here

• Project - 6

  • Topics:
    • Green Learning (GL)* introductions
    • PixelHop & PixelHop+* introductions
    • Green Learning for Image classification
  • Language: Python
  • Repo: 👉 here
  • Project Report: 👉 here

---

👉 Abstract of Projects

• This repositary includes 6 projects, which have covered a very wide range of the most popular and cut-edge topics in the realm of Digital Image Processing, including image denoising, edge detection, texture analysis, Deep learning and so on.
• Each project is developed, debugged and completed by Boyang Xiao individually, during the semester of 2022 fall in University of Southern California. With all the codes included, all of the projects are proved to be running well. Please read the README.md instructions carefully in each project and then run the codes on your end.
• Each project includes a project report, which has introduced the origin of the project, the theoratical basis of the technology, the process of the experiments and the experimental results. Please feel free to contact Boyang Xiao at [email protected].
• Fight on Trojans!! ✌️