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🧠 Computer Vision Handbook: Image & Video Processing — Syllabus

Dive into the world of Computer Vision through a hands-on, module-based programme. Each module builds on the last, blending theory with practical exercises and linking to detailed Wiki pages.

📅 Duration

8–10 weeks (self-paced)

📘 Module 1: Introduction to Computer Vision

  • What is computer vision?
  • Applications in industry and research
  • Image formation basics
  • Cameras, sensors and pixels

🖼️ Module 2: Digital Image Fundamentals

  • Image representation: grayscale, RGB, etc.
  • Colour spaces (RGB, HSV, YCrCb)
  • Sampling and quantisation
  • Image histograms and enhancement

🎛️ Module 3: Image Filtering & Convolution

  • Convolution and correlation
  • Smoothing filters (Gaussian, box)
  • Edge detection (Sobel, Canny)
  • Sharpening and gradient operators

✂️ Module 4: Image Segmentation

  • Thresholding: global, adaptive, Otsu
  • Region growing and watershed
  • Contours and connected components
  • Superpixels

📌 Module 5: Feature Detection & Matching

  • Corner detectors (Harris, Shi–Tomasi)
  • Keypoints and descriptors (SIFT, SURF, ORB)
  • Feature matching (Brute-force, FLANN)
  • Homographies and transformations

🎞️ Module 6: Motion & Video Analysis

  • Optical flow (Lucas–Kanade, Farneback)
  • Background subtraction
  • Object tracking (Kalman filters, Mean-Shift, CamShift)
  • Video stabilisation

🎯 Module 7: Object Detection & Recognition

  • HOG + SVM
  • Viola–Jones face detector
  • Deep learning methods (YOLO, SSD, Faster R-CNN)
  • Pretrained models and transfer learning

🤖 Module 8: Deep Learning in Computer Vision

  • CNN architecture fundamentals
  • Image classification with CNNs
  • Semantic & instance segmentation (U-Net, Mask R-CNN)
  • OCR, face recognition, medical imaging

🛠️ Module 9: Project & Applications

  • Select a mini-project (e.g. real-time object tracker)
  • Dataset selection and preparation
  • Model training, evaluation and optimisation
  • Deployment best practices

✅ Prerequisites

  • Python (basics)
  • NumPy, Matplotlib
  • OpenCV
  • (Optional) PyTorch or TensorFlow

Stay curious and keep building! 🧑‍💻✨