Duration
Advanced Computer Vision with OpenCV in R
4 days
Overview
By the end of this course, participants will be able to:
- Master advanced computer vision techniques using OpenCV in R.
- Implement sophisticated object detection and facial recognition solutions.
- Utilize R libraries for comprehensive computer vision tasks.
- Develop and deploy computer vision models using OpenCV.
- Integrate and visualize the output from various computer vision applications.
Target Audience
This course is designed for:
- Data scientists and analysts seeking advanced computer vision knowledge.
- R developers aiming to deepen their expertise in computer vision.
- Machine learning professionals interested in real-world computer vision applications.
- Researchers and students specializing in computer vision, deep learning, and AI.
Prerequisites
Participants should have:
- A computer with R and RStudio installed.
- Basic knowledge of R programming and machine learning concepts.
- Basic understanding of computer vision principles.
- Installation of necessary packages and libraries as guided in the course.
Day 1: Advanced Computer Vision Fundamentals
- Introduction to Advanced Computer Vision
- Deep dive into object detection and facial recognition
- Latest trends and applications in computer vision
- Ethical considerations in computer vision
- Image Processing Techniques
- Advanced image representation and enhancement techniques
- Complex image filtering and edge detection methods
- Sophisticated image segmentation strategies
- Machine Learning for Computer Vision
- In-depth look at machine learning and deep learning for computer vision
- Advanced algorithms and CNNs for object detection
- Evaluation metrics and performance tuning
Day 2: OpenCV in R - Advanced Techniques
- Advanced OpenCV Operations
- Detailed exploration of OpenCV in R
- Complex image transformations and enhancements
- Advanced feature detection and matching techniques
- Computer Vision Tasks with OpenCV
- Implementing state-of-the-art object detection
- Contour detection and shape analysis
- Advanced tracking of moving objects
- Integration and Visualization
- Combining OpenCV with ggplot2 and shiny for advanced visualization
- Data manipulation with dplyr
- Machine learning model integration with caret and OpenCV
Day 3: Open Source Libraries and API Integration
- Exploring Advanced Open Source Libraries
- Integration with TensorFlow and Keras for deep learning
- Using YOLO for high-performance object detection
- Dlib for facial recognition
- API Integration in R
- Advanced API fundamentals and HTTP methods
- Practical implementation with httr package
- Handling complex API responses and errors
- Combining OpenCV with APIs
- Real-time object detection using YOLO and APIs
- Building and testing custom APIs with R and plumber
- Best practices for API security and deployment
Day 4: Video Analysis and AWS Integration
- Advanced Video Processing Techniques
- In-depth video analysis with OpenCV
- Frame-by-frame analysis and real-time object detection
- Handling multiple faces and objects in video streams
- AWS Rekognition Integration
- Detailed overview of AWS Rekognition for object and facial recognition
- Practical implementation using the paws library
- Advanced features like face comparison and real-time video analysis
- Visualization and Reporting
- Parsing and interpreting JSON responses from APIs
- Advanced visualization with R libraries (ggplot2, shiny)
- Creating interactive dashboards and reports
Programme scope, delivery arrangements and suitability are confirmed during enquiry.