Machine Vision Training in Cambridge
Local, instructor-led live Machine Vision (MV) training courses demonstrate through interactive discussion and hands-on practice the fundamentals and applications of Machine Vision. Machine Vision training is available as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Cambridge or in NobleProg corporate training centers in Cambridge. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider
Machine Vision Course Events - Cambridge
|Code||Name||Venue||Duration||Course Date||Course Price [Remote / Classroom]|
|rasberrypiopencv||Raspberry Pi + OpenCV: Build a Facial Recognition System||Cambridge||21 hours||Mon, 2018-07-16 09:30||£3900 / £4575|
|patternmatching||Pattern Matching||Cambridge||14 hours||Wed, 2018-08-01 09:30||£2600 / £3050|
|marvin||Marvin Image Processing Framework - Creating Image and Video Processing Applications with Marvin||Cambridge||14 hours||Thu, 2018-08-16 09:30||£2600 / £3050|
|rasberrypiopencv||Raspberry Pi + OpenCV: Build a Facial Recognition System||Cambridge||21 hours||Wed, 2018-09-26 09:30||£3900 / £4575|
|patternmatching||Pattern Matching||Cambridge||14 hours||Tue, 2018-10-16 09:30||£2600 / £3050|
|marvin||Marvin Image Processing Framework - Creating Image and Video Processing Applications with Marvin||Cambridge||14 hours||Tue, 2018-10-16 09:30||£2600 / £3050|
|rasberrypiopencv||Raspberry Pi + OpenCV: Build a Facial Recognition System||Cambridge||21 hours||Wed, 2018-12-05 09:30||£3900 / £4575|
|marvin||Marvin Image Processing Framework - Creating Image and Video Processing Applications with Marvin||Cambridge||14 hours||Wed, 2018-12-05 09:30||£2600 / £3050|
|patternmatching||Pattern Matching||Cambridge||14 hours||Tue, 2018-12-11 09:30||£2600 / £3050|
|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.
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
|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.
Format of the course
|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.
Format of the course
|rasberrypiopencv||Raspberry Pi + OpenCV: Build a Facial Recognition System||21 hours||
This instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition.
The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.
By the end of this training, participants will be able to:
Format of the course