Computer Vision Algorithm and Real Time Processing training course

Available Dates

Venue

Dec-30-2024

Kuala Lumpur
Dec-30-2024

Istanbul
Dec-30-2024

Dubai
Feb-10-2025

London
Feb-10-2025

Dubai
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
Mar-17-2025

Dubai
May-12-2025

Dubai
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
Aug-11-2025

Dubai
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
Oct-27-2025

Dubai
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

Dubai
Dec-22-2025

London

Course Details

Computer vision is a field that focuses on enabling machines to interpret and make decisions based on visual input, simulating the human ability to perceive and understand images.

With the increasing demand for automated visual analysis across industries such as healthcare, robotics, and security, understanding the algorithms and real-time processing techniques behind computer vision is more important than ever. The Computer Vision Algorithm and Real-Time Processing course provides a detailed exploration of the fundamental concepts, methods, and technologies that drive modern computer vision systems.

It covers image processing techniques, feature extraction, object detection, and advanced deep learning models. The course emphasizes both theoretical knowledge and practical skills, using Python and OpenCV for hands-on activities to ensure that participants gain a solid understanding of real-time computer vision applications.

By the end of this course, participants will have developed a deep understanding of key computer vision concepts and their real-world applications. Participants will be able to:

Understand the fundamentals of computer vision and image processing, including their benefits and applications
Implement image processing techniques, such as filtering, noise reduction, enhancement, and image transformation using Python and OpenCV
Apply feature extraction, object detection, and object segmentation techniques using deep learning models
Develop and train CNN models to solve computer vision tasks and optimize real-time processing
Implement real-time processing and optimizing techniques to meet performance constraints and improve image processing systems
Calculate depth in 2D images implement reconstruction techniques to transform images from 2D to 3D

This course is designed for professionals who work with or are interested in computer vision and real-time image processing. It is particularly beneficial for:

Software Engineering
Data Scientists
Machine Learning Engineers
Developers
AI Enthusiasts

Course Outline

5 days course

Day 1

Day 2

Day 3

Day 4

Day 5

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:

Filtering
Noise reduction
Enhancement
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:

Supervised learning for image classification
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

Computer Vision Algorithm and Real Time Processing

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