Machine Vision Training in Exeter

Machine Vision Training in Exeter

Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance in industry. Machine Vision is a subset of Computer Vision.

Exeter - The Senate

The Senate
Southernhay Gardens
Exeter EX1 1UG
United Kingdom
GB
Exeter - The Senate
The business centre is located in Exeter, with excellent transport links that is served by two mainline train stations, Exeter Central and Exeter St Davids,...Read more

Client Testimonials

Computer Vision with OpenCV

The hands-on approach

Kevin De Cuyper - Automatic Systems

Machine Vision Course Events - Exeter

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
marvin Marvin Image Processing Framework - creating image and video processing applications with Marvin Exeter - The Senate 14 hours Tue, 2018-02-13 09:30 £2600 / £3200
patternmatching Pattern Matching Exeter - The Senate 14 hours Tue, 2018-03-13 09:30 £2600 / £3200
marvin Marvin Image Processing Framework - creating image and video processing applications with Marvin Exeter - The Senate 14 hours Wed, 2018-04-04 09:30 £2600 / £3200
patternmatching Pattern Matching Exeter - The Senate 14 hours Wed, 2018-05-02 09:30 £2600 / £3200
marvin Marvin Image Processing Framework - creating image and video processing applications with Marvin Exeter - The Senate 14 hours Thu, 2018-05-24 09:30 £2600 / £3200
patternmatching Pattern Matching Exeter - The Senate 14 hours Thu, 2018-06-28 09:30 £2600 / £3200
marvin Marvin Image Processing Framework - creating image and video processing applications with Marvin Exeter - The Senate 14 hours Tue, 2018-07-17 09:30 £2600 / £3200

Course Outlines

Code Name Duration Outline
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.

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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