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Kirjailija

Jürgen Beyerer

Kirjat ja teokset yhdessä paikassa: 7 kirjaa, julkaisuja vuosilta 2014–2024, suosituimpiin kuuluu Machine Vision. Vertaile teosten hintoja ja tarkista saatavuus suomalaisista kirjakaupoista.

7 kirjaa

Kirjojen julkaisuvuodet: 2014–2024.

Automatische Sichtprüfung

Automatische Sichtprüfung

Jürgen Beyerer; Fernando Puente León; Christian Frese; Johannes Meyer

Springer Fachmedien Wiesbaden
2024
sidottu
Das Lehrbuch behandelt systematisch die Bildgewinnung für die automatische Sichtprüfung. Die Autoren leiten die wesentlichen Methoden detailliert ab und stellen alle gängigen Bildgewinnungsverfahren in einem strukturierten Zusammenhang dar. Der zweite Teil des Buches ist der Bildsignalbeschreibung und der Bildauswertung gewidmet, wobei insbesondere Methoden behandelt werden, die für die automatische Sichtprüfung relevant sind. Die Autoren skizzieren die Herleitung der beschriebenen Methoden, ohne sich in mathematischen Details zu verlieren. Ihr Ziel ist, dass der Leser die Zusammenhänge wirklich versteht und das "große Bild" des Fachgebietes erkennt. Das Buch ist in sich geschlossen und bedarf zum Verständnis keiner ergänzenden Literatur. Die 3. Auflage wurde an vielen Stellen verbessert und unter anderem durch die detaillierte Einführung von neuronalen Faltungsnetzen auf den aktuellen Stand der Technik aktualisiert. Die Zielgruppen Das Buch eignet sich für Studierende der Informatik, Elektro- und Informationstechnik, der Physik und des Maschinenbaus. Ebenso wendet es sich an Ingenieure in der Automatisierungstechnik.
Pattern Recognition

Pattern Recognition

Jürgen Beyerer; Raphael Hagmanns; Daniel Stadler

De Gruyter
2024
isokokoinen pokkari
The book offers a thorough introduction to Pattern Recognition aimed at master and advanced bachelor students of engineering and the natural sciences. Besides classification - the heart of Pattern Recognition - special emphasis is put on features: their typology, their properties and their systematic construction. Additionally, general principles that govern Pattern Recognition are illustrated and explained in a comprehensible way. Rather than presenting a complete overview over the rapidly evolving field, the book clarifies the concepts so that the reader can easily understand the underlying ideas and the rationale behind the methods. For this purpose, the mathematical treatment of Pattern Recognition is pushed so far that the mechanisms of action become clear and visible, but not farther. Therefore, not all derivations are driven into the last mathematical detail, as a mathematician would expect it. Ideas of proofs are presented instead of complete proofs. From the authors’ point of view, this concept allows to teach the essential ideas of Pattern Recognition with sufficient depth within a relatively lean book.
Machine Learning for Cyber Physical Systems

Machine Learning for Cyber Physical Systems

Oliver Niggemann; Christian Kühnert; Jürgen Beyerer

Saint Philip Street Press
2020
sidottu
This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Machine Learning for Cyber Physical Systems

Machine Learning for Cyber Physical Systems

Oliver Niggemann; Christian Kühnert; Jürgen Beyerer

Saint Philip Street Press
2020
pokkari
This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Machine Vision

Machine Vision

Jürgen Beyerer; Fernando Puente León; Christian Frese

Springer-Verlag Berlin and Heidelberg GmbH Co. K
2016
nidottu
The book offers a thorough introduction to machine vision. It is organized in two parts. The first part covers the image acquisition, which is the crucial component of most automated visual inspection systems. All important methods are described in great detail and are presented with a reasoned structure. The second part deals with the modeling and processing of image signals and pays particular regard to methods, which are relevant for automated visual inspection.
Machine Vision

Machine Vision

Jürgen Beyerer; Fernando Puente León; Christian Frese

Springer-Verlag Berlin and Heidelberg GmbH Co. K
2015
sidottu
The book offers a thorough introduction to machine vision. It is organized in two parts. The first part covers the image acquisition, which is the crucial component of most automated visual inspection systems. All important methods are described in great detail and are presented with a reasoned structure. The second part deals with the modeling and processing of image signals and pays particular regard to methods, which are relevant for automated visual inspection.