Computer Vision
Day 1:
Foundations of Computer Vision
Introduction to computer vision: history, applications, and key challenges.
Basics of digital image processing: pixel manipulation, filters, and transformations.
Understanding color spaces and histogram analysis.
Hands-on lab: implementing basic image processing techniques using Python libraries.
Day 2:
Machine Learning for Vision
Overview of supervised and unsupervised learning in computer vision.
Feature engineering: SIFT, HOG, and other traditional methods.
Introduction to convolutional neural networks (CNNs): architecture and functionality.
Lab session: building a simple CNN for image classification.
Day 3:
Advanced Techniques and Tools
Object detection frameworks: YOLO, SSD, and Faster R-CNN.
Semantic segmentation and instance segmentation techniques.
Transfer learning and fine-tuning pre-trained models.
Practical exercise: deploying a pre-trained model for a custom dataset.
Day 4:
Real-World Applications and Ethics
Case studies: computer vision in healthcare, retail, and autonomous systems.
Addressing bias and fairness in AI models.
Regulatory compliance and data privacy considerations.
Group activity: designing an ethical AI solution for a given scenario.
Day 5:
Deployment and Future Trends
Edge computing and real-time computer vision applications.
Integrating computer vision with IoT and cloud services.
Emerging trends: generative adversarial networks (GANs) and augmented reality.
Final project presentation: participants showcase their end-to-end computer vision solution.
