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Week 1: Introduction to Computer Vision and Python Basics

Session 1: Introduction to Computer Vision

  • What is Computer Vision?
  • Applications of Computer Vision
  • Overview of the Boot Camp

Session 2: Python Basics for Computer Vision

  • Introduction to Python
  • Basic Syntax and Data Structures
  • Introduction to Digital Image
  • Python Libraries for Computer Vision

Session 3: Setting Up the Environment

  • Installing Python and necessary libraries (OpenCV, NumPy, etc.)
  • Introduction to Jupyter Notebooks & Anaconda
  • Writing your first Python script for image processing
  • Basic operations on images (reading, displaying, and saving images)

Week 2: Image Processing Techniques with OpenCV

Session 4: Image Transformations

  • Image transformations (resize, rotate, crop)
  • Color spaces and conversions
  • Histograms and histogram equalization
  • Filtering techniques (blurring, sharpening, edge detection)

Session 5: Advanced Image Processing

  • Morphological transformations
  • Contour detection and analysis
  • Image thresholding

Session 6: Feature Detection and Description

  • Understanding features
  • Edge detection techniques (Sobel, Canny)
  • Keypoint detection: SIFT, SURF, and ORB algorithms

Week 3: Deep Learning Basics for Computer Vision

Session 7: Introduction to Deep Learning

  • What is Deep Learning?
  • Overview of Neural Networks

Session 8: Artificial Neural Networks (ANNs)

  • Understanding ANN architecture
  • Building a simple ANN with TensorFlow/Keras
  • Training with MNIST Dataset
  • Training an ANN on image data
  • Evaluating model performance
  • Improving model accuracy

Session 9: Convolutional Neural Networks (CNNs)

  • Introduction to CNNs
  • Understanding CNN architecture
  • Convolution and pooling layers
  • Building a simple CNN with TensorFlow/Keras
  • Training and evaluating CNNs

Week 4: Transfer Learning and Fine-Tuning

Session 10: Transfer Learning

  • What is Transfer Learning?
  • Using pre-trained models
  • Fine-tuning models for specific tasks

Session 11: Image Classification

  • Introduction to image classification
  • Techniques for improving classification accuracy

Session 12: Facial Recognition

  • Overview of facial recognition technology
  • Applications and ethical considerations
  • Facial recognition pipeline: detection, alignment, feature extraction, matching
  • Building a Facial Recognition System
  • Feature extraction methods: PCA, LBP, HOG, deep learning-based approaches

Week 5: Advanced Computer Vision Techniques

Session 13: Advanced Image Segmentation

  • Semantic segmentation
  • Instance segmentation
  • Using segmentation models (U-Net, Mask R-CNN)

Session 14: Optical Character Recognition (OCR)

  • Introduction to OCR
  • Applications of OCR in Computer Vision
  • Implementing OCR using Tesseract and OpenCV

Session 15: Object Detection

  • Introduction to object detection
  • YOLO, SSD, and Faster R-CNN algorithms

Week 6: Object Tracking, Image Generation, and Final Review

Session 16: Object Tracking

  • Introduction to object tracking
  • Tracking algorithms (KLT, MeanShift, CamShift)

Session 17: Image Generation with GANs

  • Introduction to Generative Adversarial Networks (GANs)
  • Building and training a simple GAN
  • Applications of GANs in Computer Vision

Session 18: Final Project Presentation

  • Participants present their projects
  • Feedback and discussion

Session 19: Review and Q&A

  • Recap of key topics
  • Q&A session
  • Closing remarks and next steps

Instructors: