Computer Vision (CV)

Detailed syllabus for the Computer Vision course.

• Introduction to Computer Vision
  • Overview and applications of computer vision
  • Key challenges in computer vision
• Image Processing Basics
  • Understanding pixels, images, and image representation
  • Basic image processing techniques (filtering, smoothing, edge detection)
• Feature Detection and Extraction
  • Detecting edges, corners, and shapes
  • Using techniques like SIFT, SURF, and ORB
• Object Detection and Classification
  • Understanding object detection methods (YOLO, SSD)
  • Building object detection and classification models
• Convolutional Neural Networks for Vision
  • Applying CNNs to image classification and detection
  • Transfer learning for CV applications