Computer Vision with OpenCV

One of the following:

• C++
• Java
• Python
• MATLAB
• CUDA
• OpenCL

And basic knowledge of machine learning. Knowledge of linear algebra, statistics, probability are helpful.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.

Audience

This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects

Introduction

• Setting up OpenCV
• API concepts

Main Modules

• The Core Functionality(Core Module)
• Image Processing(Imgproc Module)
• High Level GUI and Media (highgui module)
• Image Input and Output (imgcodecs module)
• Video Input and Output (videoio module)
• Camera calibration and 3D reconstruction (calib3d module)
• 2D Features framework (feature2d module)
• Video analysis (video module)
• Object Detection (objdetect module)
• Machine Learning (ml module)
• Computational photography (photo module)
• OpenCV Viz

Bonus topics

• GPU-Accelerated Computer Vision (cuda module)
• OpenCV iOS

Bonus topics are not available as a part of a remote course. They can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs (for the CUDA module) or MacBooks, Apple developer accounts and iOS-based mobile devices (for the iOS topic). NobleProg cannot guarantee the availability of trainers with the required hardware.