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The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field.

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.

The Proceeding of the annual joint workshop of the Fraunhofer IOSB and the Vision and FusionLaboratory (IES) 2018 of the KIT contain technical reports of the PhD-stundents on the status of theirresearch. The discussed topics ranging from computer vision and opticalmetrology to network security and machine learning. This volume provides a comprehensive and up-to-date overview of the research program of the IESLaboratory and the Fraunhofer IOSB.

Each material has its own specific spectral signature independent if it is food, plastics, or minerals. During the conference we will discuss new trends and developments in material characterization. You also will be informed about latest highlights to identify spectral footprints and their realizations in industry.

This book describes in detail mathematical methods, which lead to suitable characteristics for pattern recognition. It deals with various classifiers and classification methods. Emphasis is on the performance of classifiers and the processing chain:

This book summarizes presentations delivered at the 2017 annual joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB) and the Vision and Fusion Laboratory (IES) of the Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT). At this meeting, doctoral students from both institutions reported on the status of their research and discussed topics ranging from computer vision to network security and machine learning.

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 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 proceedings comprise more than 500 pages of technical text documenting the high level of commitment of the security research community.

The conference will serve as an international and interdisciplinary communication platform for decision-makers, executive bodies, economic organisations and research and development partners. The conference defines the position of key organisations and demonstrates the potential of innovative technologiesand preventative research for civil security on an international level.