Introduction

Computer vision is a transformative field of artificial intelligence that enables machines to interpret and understand visual data. From facial recognition to autonomous vehicles, computer vision is driving innovation across industries. This 5-day intensive training course is designed to provide participants with a solid foundation in computer vision, covering essential concepts, techniques, and tools. Participants will gain hands-on experience in building and deploying computer vision models, preparing them to tackle real-world challenges and future advancements in the field.

Objectives

By the end of this course, participants will:

1. Understand the fundamentals of computer vision, including image processing, feature extraction, and object detection.

2. Gain proficiency in using popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch.

3. Learn how to build, train, and evaluate convolutional neural networks (CNNs) for image classification and object detection.

4. Explore advanced topics such as image segmentation, facial recognition, and generative models.

5. Apply computer vision techniques to real-world problems in healthcare, retail, security, and autonomous systems.

6. Understand ethical considerations and future trends in computer vision, including explainable AI and edge computing.

Who Should Attend?

This course is ideal for:

• Data scientists and machine learning engineers looking to specialize in computer vision.

• Software developers and engineers interested in integrating computer vision into applications.

• Researchers and academics exploring image processing and analysis.

• Professionals in healthcare, retail, security, automotive, and other industries where visual data is critical.

• AI enthusiasts and practitioners preparing for future challenges in computer vision.

Course Outline

Day 1: Foundations of Computer Vision

• Morning Session:

  • Introduction to Computer Vision: History, Applications, and Challenges

  • Basics of Image Processing: Pixels, Color Spaces, and Transformations

  • Hands-on Lab: Image Manipulation with OpenCV

• Afternoon Session:

  • Feature Extraction: Edges, Corners, and Keypoints

  • Hands-on Lab: Feature Detection with OpenCV

  • Introduction to Convolutional Neural Networks (CNNs): Architecture and Workflow

Day 2: Image Classification with CNNs

• Morning Session:

  • Building and Training CNNs for Image Classification

  • Hands-on Lab: Building a CNN with TensorFlow/Keras

  • Transfer Learning: Leveraging Pre-trained Models (e.g., VGG, ResNet, Inception)

• Afternoon Session:

  • Hands-on Lab: Fine-tuning a Pre-trained Model for Custom Datasets

  • Case Study: Medical Image Classification Using CNNs

  • Model Evaluation Metrics: Accuracy, Precision, Recall, and Confusion Matrix

Day 3: Object Detection and Localization

• Morning Session:

  • Introduction to Object Detection: Sliding Window, R-CNN, and YOLO

  • Hands-on Lab: Building an Object Detection Model with TensorFlow

  • Advanced Techniques: Faster R-CNN and Single Shot Detectors (SSD)

• Afternoon Session:

  • Hands-on Lab: Real-Time Object Detection with YOLO

  • Case Study: Object Detection in Autonomous Vehicles

  • Challenges in Object Detection: Occlusion, Scale, and Lighting Conditions

Day 4: Advanced Computer Vision Techniques

• Morning Session:

  • Image Segmentation: Semantic and Instance Segmentation

  • Hands-on Lab: Implementing Segmentation with U-Net and Mask R-CNN

  • Facial Recognition: Techniques and Applications

• Afternoon Session:

  • Hands-on Lab: Building a Facial Recognition System

  • Generative Models: GANs for Image Synthesis and Enhancement

  • Case Study: AI-Generated Art and Deepfakes

Day 5: Real-World Applications and Capstone Project

• Morning Session:

  • Deploying Computer Vision Models: Tools and Best Practices

  • Model Interpretability and Explainable AI (XAI) in Computer Vision

  • Ethical Considerations: Bias, Privacy, and Security in Computer Vision

• Afternoon Session:

  • Capstone Project: End-to-End Computer Vision Solution for a Real-World Problem

  • Project Presentations and Feedback

  • Course Wrap-up: Key Takeaways, Resources for Further Learning, and Certification

Key Features of the Course

• Hands-on labs using modern tools like OpenCV, TensorFlow, and PyTorch.

• Real-world case studies and industry-relevant applications.

• Focus on ethical AI, model interpretability, and future-proofing skills.

• Access to course materials, code repositories, and a community forum for ongoing learning.

Preparing for Future Challenges

This course is designed to not only address current industry needs but also prepare participants for emerging trends and challenges in computer vision. By focusing on ethical AI, explainability, and advanced techniques, attendees will be equipped to lead innovation and adapt to the rapidly evolving landscape of visual AI.