Module 1: Introduction to Computer Vision
- What computer vision is and why it matters
- Key applications: facial recognition, autonomous vehicles, medical imaging, AR/VR
- Overview of image processing vs. deep learning approaches
Module 2: Image Fundamentals
- Digital image basics: pixels, color spaces (RGB, HSV, grayscale)
- Image transformations: scaling, rotation, cropping
- Filtering: blurring, sharpening, edge detection
- Hands-on: using OpenCV for basic image manipulation
Module 3: Feature Detection and Extraction
- Corner detection (Harris, FAST)
- Edge detection (Canny, Sobel)
- Feature descriptors (SIFT, SURF, ORB)
- Case study: object tracking in video
Module 4: Classical Computer Vision Techniques
- Image segmentation (thresholding, clustering, watershed)
- Morphological operations (erosion, dilation)
- Template matching
- Case study: license plate recognition
Module 5: Deep Learning for Vision
- Convolutional Neural Networks (CNNs)
- Architectures: LeNet, AlexNet, VGG, ResNet
- Transfer learning and fine-tuning
- Hands-on: image classification with TensorFlow or PyTorch
Module 6: Advanced Vision Applications
- Object detection (YOLO, Faster R-CNN, SSD)
- Semantic segmentation (U-Net, Mask R-CNN)
- Image generation (GANs, diffusion models)
- Case study: medical image segmentation
Module 7: Tools and Frameworks
- OpenCV for classical vision tasks
- TensorFlow and PyTorch for deep learning
- Hugging Face for vision transformers
- Hands-on labs: building vision pipelines
Module 8: Challenges and Risks
- Data quality and annotation challenges
- Bias in vision datasets
- Adversarial attacks on vision models
- Scalability and real-time performance issues
Module 9: Ethics and Governance
- Privacy concerns in facial recognition
- Regulatory frameworks for vision applications
- Responsible AI practices in vision systems
- Building trust with stakeholders
Module 10: Future Trends
- Vision transformers (ViT, DINO)
- Multimodal AI (vision + language + audio)
- Edge AI for real-time vision on devices
- Computer vision in robotics and autonomous systems