Computer Vision Algorithm and Real Time Processing

Course Info

Date: May-12-2025

Length: 1 Week

City: Dubai

Fees: 3,985

Type: In Classroom

Available Dates

  • Dec-30-2024

    Dubai

  • Feb-10-2025

    Dubai

  • Mar-17-2025

    Dubai

  • May-12-2025

    Dubai

  • Aug-11-2025

    Dubai

  • Oct-27-2025

    Dubai

  • Dec-22-2025

    Dubai

Dates in Other Venues

  • Dec-23-2024

    Barcelona

  • Dec-30-2024

    Kuala Lumpur

  • Dec-30-2024

    Istanbul

  • Feb-10-2025

    London

  • Mar-03-2025

    Kuala Lumpur

  • Mar-03-2025

    Istanbul

  • Mar-03-2025

    Paris

  • Mar-03-2025

    Barcelona

  • Mar-03-2025

    Amsterdam

  • Mar-03-2025

    Singapore

  • Mar-17-2025

    London

  • May-12-2025

    London

  • June-02-2025

    Barcelona

  • June-02-2025

    Singapore

  • June-02-2025

    Amsterdam

  • June-02-2025

    Paris

  • June-02-2025

    Istanbul

  • June-02-2025

    Kuala Lumpur

  • Aug-11-2025

    London

  • Sep-01-2025

    Paris

  • Sep-01-2025

    Amsterdam

  • Sep-01-2025

    Barcelona

  • Sep-01-2025

    Istanbul

  • Sep-01-2025

    Singapore

  • Sep-01-2025

    Kuala Lumpur

  • Oct-27-2025

    London

  • Dec-01-2025

    Istanbul

  • Dec-01-2025

    Barcelona

  • Dec-01-2025

    Singapore

  • Dec-01-2025

    Amsterdam

  • Dec-01-2025

    Paris

  • Dec-01-2025

    Kuala Lumpur

  • Dec-22-2025

    London

Course Details

Course Outline

5 days course

Introduction to Computer Vision and Image Processing 


  • Introduction to computer vision: Concepts, benefits and applications 
  • Exploring the historical evolution of computer vision 
  • Distinguishing between traditional computer vision and AI-based approaches 
  • Definition and applications of image processing 
  • Exploring image processing techniques:


  1. Filtering 
  2. Noise reduction 
  3. Enhancement 
  4. Image transformation  



  • Activity: Performing basic image manipulation techniques using Python and OpenCV


Feature Extraction, Object Detection and Segmentation  


  • Feature extraction methods and algorithms: SIFT, SURF, and HOG
  • Object detection techniques, focusing on deep-learning techniques 
  • Understanding image segmentation and its usage in identifying objects within images 
  • Introduction to object tracking algorithms: Kalman filters and optical flow 
  • Challenges and issues with object detection and segmentation 
  • Activity: Implementing feature extraction, object detection, and segmentation techniques using per-trained models

Machine Learning and Deep Learning for Computer Vision ~


  • Machine learning techniques for computer vision: 


  1. Supervised learning for image classification 
  2. Unsupervised learning for feature extraction and reconstruction 



  • Understanding CNNs architecture and applications in computer vision tasks 
  • Methods of using pre-trained deep learning models to improve computer vision tasks 
  • Steps of training deep learning models 
  • Metrics and methods to evaluate model performance 
  • Activity: Training a CNN model for image classification using TensorFlow or PyTorch


Real-Time Processing Techniques 


  • Discussing real-time constraints in computer vision  
  • Techniques for optimizing neural networks for speed: Pruning, quantization, model compression 
  • Understanding the role of GPUs and TPUs in parallel processing and multithreading 
  • Discussing the application of edge computing in computer vision 
  • Understanding hardware acceleration 
  • Activity: Implementing real-time image processing techniques on a GPU-based system

3D Computer Vision and Depth Estimation  


  • Introduction to 3D computer vision and the transformation from 2D images to 3D images 
  • Techniques for calculating and estimating depth in images: Monocular and stereo 
  • Steps and methods for 3D reconstruction 
  • Exploring the applications of 3D computer vision 
  • Final project presentation and feedback 
  • Recap and lesson learned