Computer Vision Training Courses

Computer Vision Training

Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images.

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Computer Vision with OpenCV

The hands-on approach

Kevin De Cuyper - Automatic Systems

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Computer Vision Course Outlines

Code Name Duration Overview
opencv Computer Vision with OpenCV 28 hours 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.
simplecv Computer Vision with SimpleCV 14 hours SimpleCV is an open source framework — meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It’s helps you build software to make your various technologies not only see the world, but understand it too. Audience This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV. Getting Started Installation Tutorials & Examples SimpleCV Shell SimpleCV Basics The Hello World program Interacting with the Display Loading a Directory of Images Macro’s Kinect Timing Detecting a Car Segmenting the Image and Morphology Image Arithmetic Exceptions in Image Math Histograms Color Space Using Hue Peaks Creating a Motion Blur Effect Simulating Long Exposure Chroma Key (Green Screen) Drawing on Images in SimpleCV Layers Marking up the Image Text and Fonts Making a Custom Display Object
patternmatching Pattern Matching 14 hours Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not. Audience     Engineers and developers seeking to develop machine vision applications     Manufacturing engineers, technicians and managers Format of the course     This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision. Introduction     Computer Vision     Machine Vision     Pattern Matching vs Pattern Recognition Alignment     Features of the target object     Points of reference on the object     Determining position     Determining orientation Gauging     Setting tolerance levels     Measuring lengths, diameters, angles, and other dimensions     Rejecting a component Inspection     Detecting flaws     Adjusting the system Closing remarks  
marvin Marvin Image Processing Framework - creating image and video processing applications with Marvin 14 hours Marvin is an extensible, cross-platform, open-source image and video processing framework developed in Java.  Developers can use Marvin to manipulate images, extract features from images for classification tasks, generate figures algorithmically, process video file datasets, and set up unit test automation. Some of Marvin's video applications include filtering, augmented reality, object tracking and motion detection. In this course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application. Audience     Software developers wishing to utilize a rich, plug-in based open-source framework to create image and video processing applications Format of the course     The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries. Introduction to Marvin Downloading and installing Marvin Setting up an Eclipse development environment The three layers of the Marvin architecture     Framework     Plug-ins     Applications Components and libraries Image processing in Marvin Video processing in Marvin Multi-threading in Marvin Unit testing in Marvin Working with MarvinEditor Creating an application with Marvin Working with plug-ins Testing the application Video applications     Video filtering     Image subtraction and combination     Tracking     Face features detection     Real time tracking of multiple blobs     Partial shape matching     Skin-colored pixels detection Using Marvin Framework for test automation Extending the framework Contributing to the project Closing remarks

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