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AI Image Processing

Image Processing Computer Vision Python OpenCV Deep Learning
  • Created at 13 Nov 2024

Course Overview #

This course is designed for everyone from beginners in AI-based image processing to those who want to apply it in practice.

Learning Objectives #

  • Understand fundamental principles and techniques of image processing
  • Gain proficiency in image processing libraries like OpenCV and PIL
  • Implement deep learning-based image classification and object detection
  • Gain practical experience applicable to real-world projects

Course Structure #

Part 1: Basic Image Processing

  • Reading, writing, and converting images
  • Filtering and noise removal
  • Image transformations (rotation, resizing, cropping)
  • Histogram and brightness/contrast adjustment

Part 2: Computer Vision Techniques

  • Edge detection (Canny, Sobel)
  • Corner detection (Harris Corner)
  • Template matching
  • Feature detection and matching (SIFT, ORB)

Part 3: Deep Learning-based Image Processing

  • Image classification with CNN
  • Transfer Learning
  • Object detection (YOLO, R-CNN)
  • Image segmentation

Part 4: Practical Projects

  • Face recognition system development
  • Medical image analysis
  • License plate recognition
  • Real-time object tracking

Teaching Method #

  • Online/Offline: Zoom or in-person sessions
  • Hands-on focused: Practical projects every session
  • 1:1 Feedback: Personal project code reviews
  • Real-time Q&A: Live questions and answers during sessions

Target Audience #

  • Developers familiar with Python basics
  • Those interested in image processing
  • Anyone wanting to apply computer vision in practice
  • Those wanting to learn deep learning-based image analysis

Prerequisites #

  • Python basic syntax
  • NumPy basics (recommended)
  • Basic machine learning concepts (recommended)

Contact #

For course schedule and pricing inquiries, please reach out via email.