Mastering Computer Vision: Basics, Deep Learning, and Real-World Applications

The Computer Vision workshop focuses on the principles and practical techniques of enabling machines to process, analyze, and interpret visual data. Participants will explore key concepts such as image processing, object detection, and neural networks. Through hands-on sessions and industry use cases, this program provides a solid foundation for understanding the role of computer vision in fields like healthcare, autonomous vehicles, and retail.

• To introduce participants to the fundamentals of Computer Vision and its applications.
• To train participants in developing and deploying vision-based AI solutions.
• To explore real-world use cases and challenges in Computer Vision.
• To provide hands-on experience with state-of-the-art Computer Vision tools and frameworks.
• To prepare participants for advanced roles in AI and Computer Vision.

Day 1: Introduction to Computer Vision

• What is Computer Vision?
  • Overview of computer vision and its real-world applications (e.g., facial recognition, autonomous vehicles, healthcare).
• Core Concepts of Computer Vision
  • Pixels, images, and image processing basics.
  • Common tasks: Object detection, image classification, and segmentation.
• Getting Started with Tools
  • Installing and setting up Python libraries: OpenCV, NumPy, and Matplotlib.
  • Basic image operations: Loading, displaying, resizing, and cropping.

Day 2: Image Processing and Feature Extraction

• Image Preprocessing Techniques
  • Converting images to grayscale and binary.
  • Applying filters: Gaussian blur, edge detection (Sobel, Canny).
• Feature Extraction
  • Detecting features: Corners, edges, and contours.
  • Understanding keypoint detection methods: SIFT, ORB.
• Hands-On Session
  • Building a basic image classification pipeline using OpenCV.

Day 3: Deep Learning in Computer Vision

• Deep Learning for Vision Tasks
  • Introduction to Convolutional Neural Networks (CNNs).
  • Popular architectures: VGG, ResNet, and MobileNet.
• Pretrained Models and Transfer Learning
  • Using TensorFlow/Keras for applying pretrained models to image datasets.
  • Hands-on: Classifying images with a pretrained model (e.g., ResNet50).
• Real-World Applications and Future Trends
  • Applications: Autonomous vehicles, facial recognition, healthcare imaging.
  • Emerging trends: GANs, real-time object detection, and AR/VR.

• AI and machine learning professionals
• Students and researchers in computer science, robotics, and AI
• Developers interested in visual data processing
• Professionals in healthcare, retail, automotive, and security sectors