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Overview Of Computer Vision By Using C++ and OpenCV
Computer vision using C++ is one of the most powerful skills for modern developers working with OpenCV and GPU acceleration. This course introduces every stage of image processing, algorithm optimisation and real-time computer-vision deployment. Moreover, you will master how to compile and configure OpenCV with GPU modules, allowing your projects to run faster and handle complex tasks efficiently.
In addition, learners practise C++ coding while applying CUDA-enabled functions for object detection, tracking and classification. Therefore, this online course prepares you to design advanced computer-vision systems using C, OpenCV and GPU support.
Description –
The computer vision using C++ course takes you through the entire process of building intelligent visual systems. You’ll start with basic pixel operations and move on to edge detection, noise reduction, and colour analysis. Moreover, you’ll explore how OpenCV integrates with C to create powerful applications that recognise faces, detect movement, and process live video streams.
Throughout the course, you’ll complete hands-on projects that demonstrate your understanding of computer vision principles. In addition, you’ll practise writing efficient code that can analyse images quickly and accurately. By the end, you’ll have built several mini-projects and gained the confidence to apply computer vision techniques to robotics, automation, and artificial intelligence.
This Computer Vision Using C++ course is ideal for beginners in programming and professionals who want to expand their skills in image analysis and visual computing Wikipedia
This OpenCV C++ training course uses video tutorials, code samples, practical labs and end-to-end case-studies to ensure you apply your learning immediately. Moreover, you will explore performance benchmarking, debugging of GPU-based algorithms and deployment considerations for vision systems. Consequently, you will complete the training ready to create and optimise high-speed vision applications using C++ and OpenCV with GPU support.
Learning Outcomes:
Build and configure OpenCV with GPU support as taught in the computer vision using C++ and OpenCV with GPU support course.
Implement image–processing pipelines in C++ using OpenCV and optimise them for performance.
Utilise GPU acceleration to speed up vision-based tasks such as object detection, feature matching and motion tracking.
Analyse and benchmark vision algorithms and enhance throughput in vision systems using OpenCV GPU modules.
Map learned techniques from the online computer vision with OpenCV C++ course to real-world applications in robotics, autonomous systems or research.
Demonstrate proficiency in delivering efficient, scalable computer vision solutions using C++, OpenCV and GPU support.
Why Choose Us?
- This course is accredited by the CPD Quality Standards.
- Lifetime access to the whole collection of the learning materials.
- Online test with immediate results.
- Enroling in the course has no additional cost.
- You can study and complete the course at your own pace.
- Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.
Certificate of Achievement
Upon successful completion of this course offered by NextGen Learning, you will receive a CPD-certified PDF certificate for the cost of £9.99. This certificate is recognised in the UK and internationally, demonstrating your achievement in computer vision using C and OpenCV with GPU support. To further your software development skills, you may explore our Software & Programming Courses at NextGen Learning.
Who Is This Course For?
Software developers and engineers working on vision applications or robotics.
C++ programmers seeking to advance into computer vision and GPU-accelerated workflows.
Researchers and technologists interested in high-performance image-processing systems.
Professionals in autonomous systems, industrial automation or surveillance developing real-time vision solutions.
Developers looking for a CPD-certified online computer vision with OpenCV course in the UK.
Career Path
Vision Software Developer – deploy C++ and OpenCV algorithms in commercial applications.
Computer Vision Engineer – design and implement high-performance vision systems with GPU support.
Robotics Engineer – integrate vision pipelines into robotic platforms or autonomous vehicles.
Machine Vision Specialist – deliver optimised image processing solutions in automation or manufacturing.
Research & Development Engineer – leverage advanced computer vision techniques for innovative projects.
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FAQs –
What is covered in the computer vision using C and OpenCV with GPU support course?
This course from NextGen Learning covers image-processing, object detection, GPU-enabled OpenCV compilation and algorithm implementation using C++.
Do I need prior experience before enrolling in the online computer vision with OpenCV C++ course?
Yes. Basic C++ programming and understanding of image processing are beneficial though the course builds advanced skills guided
Can I apply what I learn in this OpenCV GPU accelerated computer vision course in real-world systems?
Absolutely. The training is designed to give you practical experience in deploying and optimising vision algorithms with GPU support.
Will the certificate from this course be recognised in the UK?
Yes. The PDF certificate from NextGen Learning is CPD-certified and suitable for UK professional development.
Course Curriculum
| Unit 01: Set up Necesssary Environments | |||
| Module 01: Driver installation | 00:06:00 | ||
| Module 02: Cuda toolkit installation | 00:01:00 | ||
| Module 03: Compile OpenCV from source with CUDA support part-1 | 00:06:00 | ||
| Module 04: Compile OpenCV from source with CUDA support part-2 | 00:05:00 | ||
| Module 05: Python environment for flownet2-pytorch | 00:09:00 | ||
| Unit 02: Introduction with a few basic examples! | |||
| Module 01: Read camera & files in a folder (C++) | 00:11:00 | ||
| Module 02: Edge detection (C++) | 00:08:00 | ||
| Module 03: Color transformations (C++) | 00:07:00 | ||
| Module 04: Using a trackbar (C++) | 00:06:00 | ||
| Module 05: Image filtering with CUDA (Introduction to using OpenCV GPU methods on C++) | 00:13:00 | ||
| Unit 03: Background segmentation | |||
| Module 01: Background segmentation with MOG (C++) | 00:04:00 | ||
| Module 02: MOG and MOG2 cuda implementation (C++ – CUDA) | 00:03:00 | ||
| Module 03: Special app: Track class | 00:06:00 | ||
| Module 04: Special app: Track bgseg Foreground objects | 00:08:00 | ||
| Unit 04: Object detection with openCV ML module (C++ CUDA) | |||
| Module 01: A simple application to prepare dataset for object detection (C++) | 00:08:00 | ||
| Module 02: Train model with openCV ML module (C++ and CUDA) | 00:13:00 | ||
| Module 03: Object detection with openCV ML module (C++ CUDA) | 00:06:00 | ||
| Unit 05: Optical Flow | |||
| Module 01: Optical flow with Farneback (C++) | 00:08:00 | ||
| Module 02: Optical flow with Farneback (C++ CUDA) | 00:06:00 | ||
| Module 03: Optical flow with Nvidia optical flow SDK (C++ CUDA) | 00:05:00 | ||
| Module 04: Optical flow with Nvidia Flownet2 (Python) | 00:05:00 | ||
| Module 05: Performance Comparison | 00:07:00 | ||
| Additional Resource | |||
| Resources | 00:00:00 | ||
| Assignment | |||
| Assignment – Computer Vision by Using C++ and OpenCV | 00:00:00 | ||
| Order Your Certificate | |||
| Order Your Certificate | 00:00:00 | ||
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