OpenCV (Open Source Computer Vision Library) Course Online

OpenCV Course

OpenCV course equips you with the skills to develop advanced computer vision applications using one of the most powerful and widely used open-source libraries in the field. OpenCV (Open Source Computer Vision Library) offers a robust set of tools for image and video analysis, making it an essential skill for anyone working in computer vision, image processing, and machine learning.

OpenCV, initially developed by Intel in 1999, has evolved into a versatile, cross-platform library supported by a vibrant community. Key aspects of OpenCV that make it indispensable in the field include:

Extensive Functionality: OpenCV provides over 2500 optimized algorithms, covering classical and state-of-the-art computer vision and machine learning algorithms.
Real-time Capability: Designed with a strong focus on real-time applications, OpenCV is optimized for computational efficiency and can take advantage of multi-core processing.
Multiple Interface Support: While primarily used with C++, OpenCV also offers interfaces for Python, Java, and MATLAB, making it accessible to developers with different language preferences.
Hardware Acceleration: OpenCV can utilize hardware acceleration through technologies like CUDA, OpenCL, and Intel's IPP, allowing for high-performance computing on various platforms.
Active Development: With regular updates and contributions from a global community, OpenCV continuously incorporates cutting-edge algorithms and improvements.

In this course, you'll learn to leverage these features for practical applications such as:

Implementing advanced image processing techniques
Developing real-time object detection and tracking systems
Creating augmented reality applications
Performing optical character recognition (OCR)
Building automated visual inspection systems for manufacturing
Developing facial recognition systems for security applications
Analyzing medical images for diagnostic support

By mastering OpenCV, you'll gain the ability to tackle complex visual computing challenges across various domains.

Why Learn OpenCV

Versatile Applications: Apply computer vision techniques to fields ranging from robotics to medical imaging.
Industry-Standard Tool: OpenCV is widely used in both industry and academia, making it a valuable skill for your career.
Efficient Development: Leverage OpenCV's optimized algorithms to build high-performance vision applications.
Cross-Platform Compatibility: Develop applications that work across different operating systems and devices.
Integration with AI: Combine OpenCV with machine learning for advanced intelligent vision systems

What You Learn in Our OpenCV course

Fundamentals of digital image processing
OpenCV library structure and core functionality
Image filtering, transformations, and edge detection
Feature detection and description
Object detection and recognition
Camera calibration and 3D vision
Video analysis and background subtraction
Machine learning integration for computer vision tasks

Who Should Attend

Software developers interested in computer vision applications
Robotics engineers working on perception systems
Data scientists expanding into image-based machine learning
Researchers applying computer vision in their field of study
Students and professionals planning a career in AI and computer vision

Prerequisites

Proficiency in Python or C++ programming
Basic understanding of linear algebra and calculus
Familiarity with fundamental image processing concepts (helpful but not required)

Skills & Techniques

Image acquisition and preprocessing
Implementing various image processing algorithms
Developing object detection and tracking systems
Creating augmented reality applications
Optimizing OpenCV code for real-time performance
Integrating OpenCV with other libraries and frameworks
Debugging and troubleshooting computer vision applications

Course Structure

Ch. 1

Introduction to Image and Video Processing

Ch. 2

Signals and Systems

Ch. 3

Fourier Transform and Sampling

Ch. 4

Motion Estimation

Ch. 5

Image Enhancement

Ch. 6

Image segmentation

Ch. 7

Image and Video Segmentation

Ch. 8

Geometric PDEs

Ch. 9

Image Recovery

Ch. 10

Advanced Operations, Detecting Faces and Features

Meet your instructor

Benny Cohen

Embedded Academy Founder and CEO

As a long-time veteran in the technology industry, Benny Cohen combines a deep passion for technology with extensive field experience. With a B.Sc. in Electronics Engineering and an M.Sc. in Communication Engineering, he has spent over 20 years developing software and hardware systems, including the last few years focusing on the cybersecurity industry. In addition to his role as the company founder & CEO, Benny also operates as a hands-on practitioner who specializes in penetration testing and has conducted significant security assessments for leading enterprises and security companies worldwide. His approachable teaching style and real-world expertise make learning both engaging and relevant.

What our graduates say

David M.
Embedded System course graduate

Embedded System engineer

Didn’t realy know what I wanted to focus on when applied but my career advisor laid the options down so we could see what’s a good fit for me professionally. I got into the embedded system program and feel good with this path.

Dima Solomianik
Real Time Embedded course graduate

RT Embedded Engineer, HP

After completing the course I moved onto the job interview preparation process in which I got valuable insights about what employers search for when seeking a talent to take on the role confident in the skills and experience I gained as a student. I got 3 high tech companies getting back to me to proceed to the next level

Alex K.
Real Time Embedded course graduate

Linux Kernel Developer

The Embedded systems course was very detailed and relevant as I got to work on actual client projects that show industry challenges. I now work as an embedded engineer and the skills I gained are directly applicable to my daily work.

David F.
Embedded System course graduate

Device Driver Developer

The real-time embedded program got deep into relevant industry challenges, and I got to apply new strategies I learned to streamline our development processes.

Liam S.
Real-Time Embedded course graduate

RTOS Specialist

I came into the real-time embedded course with limited coding experience, but the structured learning path and personalized mentorship made the transition seamless.