Introduction to Computer Vision

Course Title: Introduction to Computer Vision

Course Description: This course provides an introduction to the field of computer vision, covering fundamental concepts, algorithms, and applications used in the analysis and understanding of visual data. Topics include image processing, feature extraction, object detection, image segmentation, deep learning for vision, and practical applications of computer vision in various domains.

Course Outline:

  1. Week 1: Introduction to Computer Vision
    • Definition and scope of computer vision
    • History and evolution of computer vision
    • Applications of computer vision in industry and research
  2. Week 2: Image Formation and Preprocessing
    • Digital image fundamentals
    • Image representation (RGB, grayscale, etc.)
    • Image enhancement techniques (contrast enhancement, noise reduction)
  3. Week 3: Image Filtering and Feature Extraction
    • Spatial and frequency domain filtering
    • Edge detection algorithms (Sobel, Canny)
    • Corner detection (Harris corner detector)
  4. Week 4: Image Segmentation
    • Thresholding techniques
    • Region-based segmentation (watershed algorithm)
    • Contour detection and extraction
  5. Week 5: Object Detection and Recognition
    • Introduction to object detection
    • Haar cascades and Viola-Jones algorithm
    • Feature-based object recognition (SIFT, SURF)
  6. Week 6: Deep Learning for Computer Vision
    • Basics of deep learning and neural networks
    • Convolutional Neural Networks (CNNs) for image classification
    • Transfer learning in computer vision tasks
  7. Week 7: Image Classification and Recognition
    • CNN architectures (e.g., ResNet, VGG)
    • Image classification techniques
    • Fine-tuning pre-trained models
  8. Week 8: Object Tracking and Motion Analysis
    • Object tracking algorithms (Kalman filter, optical flow)
    • Motion analysis in video sequences
    • Multiple object tracking
  9. Week 9: 3D Computer Vision
    • Depth perception and stereo vision
    • Structure from Motion (SfM)
    • 3D object reconstruction
  10. Week 10: Applications of Computer Vision
    • Autonomous vehicles and robotics
    • Medical image analysis
    • Augmented reality and virtual reality
    • Surveillance and security systems

Course Assignments:

  • Programming assignments (e.g., implementing image processing algorithms, building CNNs)
  • Image processing and feature extraction exercises
  • Object detection and recognition projects
  • Deep learning model training and evaluation
  • Object tracking and motion analysis assignments
  • 3D computer vision and reconstruction tasks
  • Case studies on real-world applications of computer vision

Course Materials:

  • Textbook: “Computer Vision: Algorithms and Applications” by Richard Szeliski
  • Research papers and articles on computer vision topics
  • Online resources for image processing and deep learning frameworks (OpenCV, TensorFlow, PyTorch)
  • Datasets for computer vision tasks (ImageNet, COCO)
  • Software tools for 3D reconstruction and visualization
  • Case studies and project examples

Assessment:

  • Programming assignments and projects
  • Midterm exam covering theoretical concepts
  • Final project showcasing practical implementation of computer vision techniques
  • Class participation and engagement in discussions and presentations

Prerequisites: Basic programming skills (Python recommended), familiarity with linear algebra and calculus, and an understanding of machine learning concepts would be beneficial but not mandatory.