Online or onsite, 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 "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Csongrád onsite live Machine Vision trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Szeged
NobleProg Szeged, Maros utca 1., Szeged, hungary, 6721
The NobleProg conference rooms are located in the city center, right next to the garden of the beautiful castle of Szeged and...
The NobleProg conference rooms are located in the city center, right next to the garden of the beautiful castle of Szeged and close to the Tisza river. These conference rooms can be hired as lecture, seminar, teaching or meeting rooms. With natural lighting from two large windows, a variety of artificial lighting options and great technical equipments, such as projector, computers, full sound system with speakers, and internet, this meeting room has great possibilities for successful presentations or trainings. Our professional staff is taking care of the preparation and implementation of the high quality conferences.
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
This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
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