Introduction:

Image recognition and analysis are core applications of Artificial Intelligence (AI) that enable machines to interpret, process, and understand visual data. This 5-day course will immerse participants in the cutting-edge techniques and algorithms that power image recognition systems. Covering everything from the basics of computer vision to advanced AI techniques such as convolutional neural networks (CNNs), object detection, and image segmentation, this course is designed to equip participants with the practical skills needed to develop AI-driven image analysis systems for real-world applications, including healthcare, security, retail, and autonomous systems.

Objectives:

By the end of this course, participants will:

• Understand the fundamentals of computer vision and the role of AI in image recognition.
• Learn how to build and train Convolutional Neural Networks (CNNs) for image classification.
• Master advanced image analysis techniques, including object detection, semantic segmentation, and image generation.
• Gain hands-on experience using popular libraries and frameworks such as TensorFlow, PyTorch, OpenCV, and Keras for AI-based image analysis.
• Apply AI techniques to solve real-world challenges, such as medical image analysis, facial recognition, and visual defect detection.

Who Should Attend:

This course is intended for:

• Data Scientists, Machine Learning Engineers, and AI Researchers seeking to specialize in image recognition and analysis.
• Software developers and engineers with a strong background in Python programming who wish to explore AI applications in computer vision.
• Professionals in industries such as healthcare, autonomous driving, security, and retail, looking to integrate image recognition into their AI systems.
• Researchers and students with a solid understanding of machine learning, eager to delve deeper into the field of computer vision.

Day 1: Introduction to Computer Vision and Image Recognition

• Morning:
  • Introduction to Computer Vision:
    • What is computer vision? Applications and impact in various industries.
    • Image processing basics: pixels, color channels, histograms, and feature extraction.
  • Overview of AI in Image Recognition:
    • Traditional methods vs. deep learning-based approaches.
    • The role of Convolutional Neural Networks (CNNs) in image recognition tasks.
• Afternoon:
  • Fundamentals of Convolutional Neural Networks (CNNs):
    • Understanding CNN architecture: convolutional layers, pooling, and fully connected layers.
    • Activation functions, backpropagation, and gradient descent in CNNs.
  • Hands-on Session:
    • Building a simple CNN for image classification using TensorFlow/Keras on a dataset like MNIST or CIFAR-10.

Day 2: Deep Dive into Convolutional Neural Networks (CNNs)

• Morning:
  • Advanced CNN Architectures:
    • Exploring well-known CNN architectures: AlexNet, VGGNet, GoogLeNet, and ResNet.
    • Transfer learning with pre-trained models: how to fine-tune models for custom tasks.
  • Optimizing CNNs:
    • Techniques for improving CNN performance: batch normalization, dropout, and data augmentation.
    • Hyperparameter tuning and model evaluation.
• Afternoon:
  • Hands-on Session:
    • Fine-tuning a pre-trained CNN model (e.g., VGG16 or ResNet50) for a custom image classification task.
    • Evaluating model performance with metrics such as accuracy, precision, recall, and F1-score.

Day 3: Object Detection and Localization

• Morning:
  • Introduction to Object Detection:
    • Object detection vs. image classification.
    • Key concepts: bounding boxes, intersection over union (IoU), and non-maximum suppression (NMS).
  • Advanced Object Detection Models:
    • Overview of popular object detection models: YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN.
    • Choosing the right model based on accuracy and speed requirements.
• Afternoon:
  • Hands-on Session:
    • Implementing an object detection model using YOLO or Faster R-CNN.
    • Running the model on custom images to detect and classify multiple objects in real time.

Day 4: Image Segmentation and Advanced Image Analysis

• Morning:
  • Introduction to Image Segmentation:
    • What is image segmentation? Pixel-level classification.
    • Applications in medical imaging, satellite image analysis, and autonomous vehicles.
  • Semantic vs. Instance Segmentation:
    • Semantic segmentation: classifying every pixel into a category.
    • Instance segmentation: detecting and segmenting individual object instances.
  • Popular Image Segmentation Models:
    • U-Net, Mask R-CNN, and DeepLabV3.
• Afternoon:
  • Hands-on Session:
    • Implementing image segmentation using U-Net or Mask R-CNN for medical image analysis (e.g., tumor detection).
    • Fine-tuning models and evaluating segmentation performance with metrics like IoU and Dice coefficient.

Day 5: Real-World Applications and Future Trends

• Morning:
  • Applications of AI in Image Recognition:
    • Healthcare: Medical image analysis for disease detection (e.g., detecting tumors in X-rays or MRIs).
    • Security: Facial recognition, video surveillance, and anomaly detection.
    • Retail: Product recognition and visual search in e-commerce.
    • Autonomous Vehicles: Object detection and path planning.
  • Challenges in Image Recognition:
    • Dealing with data imbalance, noisy data, and domain-specific challenges.
    • Transfer learning and fine-tuning for specialized datasets.
• Afternoon:
  • Hands-on Session:
    • Developing a full image recognition pipeline for a real-world application (e.g., facial recognition or product classification).
  • Wrap-Up and Future Trends:
    • The future of AI in image recognition: Explainable AI (XAI), ethical considerations, and the evolution of image analysis.
    • Insights on career paths and further resources to stay updated in the rapidly evolving field of computer vision.

Key Takeaways:

• Mastery of computer vision techniques and AI-driven image recognition algorithms.
• Practical experience in building, fine-tuning, and deploying models for real-world image recognition and analysis tasks.
• An understanding of the challenges in image recognition and how to address them using modern techniques.
• Preparation for future developments in AI for visual perception, including emerging technologies and their applications across various industries.