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Computer Vision By Using C++ and OpenCV with GPU support



    The Computer Vision By Using C++ and OpenCV with GPU support is the best way for you to gain deep insight and knowledge of this topic. You will learn from industry experts and obtain an accredited certificate after completing the course. Enrol now for a limited-time discounted price.

    Like all the courses of One Education, this Computer Vision By Using C++ and OpenCV with GPU support is designed with the utmost attention and thorough research. All the topics are broken down into easy to understand bite-sized modules that help our learners to understand each lesson very easily.

    We don’t just provide courses at One Education; we provide a rich learning experience. After purchasing a course from One Education, you get complete 1-year access with tutor support.

    Our expert instructors are always available to answer all your questions and make your learning experience exquisite.

    After completing the Computer Vision By Using C++ and OpenCV with GPU support, you will instantly get an e-certificate that will help you get jobs in the relevant field and will enrich your CV.

    If you want to learn about this topic and achieve certifications, you should consider this Computer Vision By Using C++ and OpenCV with GPU support from One Education.

    There are no hidden fees or exam charges. We are very upfront and clear about all the costs of the course.

    Course design

    The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

    You are taught through a combination of

    • Video lessons
    • Online study materials

    Will I receive a certificate of completion?

    Upon successful completion, you will qualify for the UK and internationally-recognised certification and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £9 and Hard Copy Certificate for £15.

    Why study this course

    Whether you’re an existing practitioner or aspiring professional, this course will enhance your expertise and boost your CV with key skills and an accredited qualification attesting to your knowledge.

    The Computer Vision By Using C++ and OpenCV with GPU support is open to all, with no formal entry requirements. All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and to be over the age of 16.

    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

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