Jürgen Beyerer – författare

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 is to 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.

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 is to 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.

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.

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.

Das 21. Fachgespräch Autonome Mobile Systeme (AMS 2009) ist ein Forum, das Wissenschaftlerinnen und Wissenschaftlern aus Forschung und Industrie, die auf dem Gebiet der autonomen mobilen Systeme arbeiten, eine Basis für den Gedankenaustausch bietet und wissenschaftliche Diskussionen sowie Kooperationen auf diesem Forschungsgebiet fördert bzw. initiiert. Inhaltlich finden sich ausgewählte Beiträge zu den Themen Humanoide Roboter und Flugmaschinen, Perzeption und Sensorik, Kartierung und Lokalisation, Regelung, Navigation, Lernverfahren, Systemarchitekturen sowie der Anwendung von autonomen mobilen Systemen.

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 Bildsignalauswertung 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. Es eignet sich für Studierende der Informatik, Elektro- und Informationstechnik, der Physik und des Maschinenbaus. Ebenso wendet es sich an Ingenieure in der Automatisierungstechnik.

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 2. Auflage wurde gründlich überarbeitet, inhaltlich ergänzt und aktualisiert. Neue Beispiele verdeutlichen den Bezug zur Praxis.

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.

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.

The work 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 Lemgo, October 1-2, 2015.

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.

The work 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, September 29th, 2016. 

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.  

The work 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 Lemgo, October 25th-26th, 2017. 

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.

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.