@inproceedings{2128,
  abstract     = {{We present the concept of a perceptive motor in terms of a cyber-physical system (CPS). A model application monitoring a knitting process was developed, where the take-off of the produced fabric is controlled by an electric motor. The idea is to equip a synchronous motor with a smart camera and appropriate image processing hard- and software components. Subsequently, the characteristics of knitted fabric are analysed by machine-learning (ML) methods. Our concept includes motor-current analysis and image processing. The aim is to implement an assistance system for the industrial large circular knitting process. An assistance system will help to shorten the retrofitting process. The concept is based on a low cost hardware approach for a smart camera, and stems from the recent development of image processing applications for mobile devices [1–4].}},
  author       = {{Vukovic, Kristijan and Simonis, Kristina and Dörksen, Helene and Lohweg, Volker}},
  booktitle    = {{Conference on Machine Learning for Cyber-Physical Systems (ML4CPS)}},
  keywords     = {{Assistance System, Euler Number, Synchronous Motor, Image Processing System, Image Processing Method}},
  title        = {{{Efficient Image Processing System for an Industrial Machine Learning Task}}},
  doi          = {{10.1007/978-3-662-48838-6_8}},
  year         = {{2015}},
}

@inproceedings{2155,
  abstract     = {{Today, mobile devices (smartphones, tablets, etc.) are widespread and of high importance for their users. Their performance as well as versatility increases over time. This leads to the opportunity to use such devices for more specific tasks like image processing in an industrial context. For the analysis of images requirements like image quality (blur, illumination, etc.) as well as a defined relative position of the object to be inspected are crucial. Since mobile devices are handheld and used in constantly changing environments the challenge is to fulfill these requirements. We present an approach to overcome the obstacles and stabilize the image capturing process such that image analysis becomes significantly improved on mobile devices. Therefore, image processing methods are combined with sensor fusion concepts. The approach consists of three main parts. First, pose estimation methods are used to guide a user moving the device to a defined position. Second, the sensors data and the pose information are combined for relative motion estimation. Finally, the image capturing process is automated. It is triggered depending on the alignment of the device and the object as well as the image quality that can be achieved under consideration of motion and environmental effects.}},
  author       = {{Henning, Kai-Fabian and Fritze, Alexander and Gillich, Eugen and Mönks, Uwe and Lohweg, Volker}},
  booktitle    = {{IST/SPIE Electronic Imaging 2015, Digital Photography and Mobile Imaging XI}},
  keywords     = {{Image processing, Image acquisition, Mobile devices  Sensors, Image fusion, Motion estimation, Cameras}},
  pages        = {{1--12}},
  publisher    = {{SPIE}},
  title        = {{{Stable Image Acquisition for Mobile Image Processing Applications}}},
  doi          = {{10.1117/12.2076146}},
  year         = {{2015}},
}

@book{4336,
  abstract     = {{Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems.

The very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning.

The papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.}},
  editor       = {{Holzinger, Andreas and Röcker, Carsten and Ziefle, Martina}},
  isbn         = {{978-3-319-16225-6}},
  issn         = {{1611-3349}},
  keywords     = {{HCI, ambient assisted living, big data, computational intelligence, context awareness, data centric medicine, decision support, interactive data mining, keyword detection, knoweldge bases, knoweldge discovery, machine learning, medical decision support, medical informatics, natural language processing, pervasive health, smart home, ubiquitous computing, visualization, wearable sensors}},
  pages        = {{275}},
  publisher    = {{Springer}},
  title        = {{{Smart Health: Open Problems and Future Challenges}}},
  doi          = {{10.1007/978-3-319-16226-3}},
  volume       = {{8700}},
  year         = {{2015}},
}

@misc{1158,
  abstract     = {{This thesis is a concept how to build up a media archive for the department of media production of University of Applied Science in Lemgo. It serves two purposes, the perma-nent storage of media data and forms a base to allow the creation of high quality presen-tation material. It contains an analysis of the current situation concerning collecting, storing and processing media data as well as ideas to alter the system. Furthermore guidelines are developed for diverse areas, e.g. data management, data storage and or-ganisation. The aim of this project is to improve public relations and therefore the image and exterior view of the department.}},
  author       = {{Bandeck, Stefan}},
  keywords     = {{Media archive, MIA, public relations, data management, data storage, data processing}},
  pages        = {{76}},
  publisher    = {{Hochschule Ostwestfalen-Lippe}},
  title        = {{{Media Archive (MIA) 2011}}},
  year         = {{2011}},
}

@inproceedings{2058,
  abstract     = {{Nonlinear spatial transforms and fuzzy pattern classification with unimodal potential functions are established in signal processing. They have proved to be excellent tools in feature extraction and classification. In this paper we present a hardware accelerated image processing and classification scheme for rotation and translation tolerant two-dimensional pattern recognition, which is based on one-dimensional nonlinear discrete circular transforms. However, the scheme is simple; it is stable and therefore well suited for industrial applications. An implementation on one field programmable gate array (FPGA) is proposed.}},
  author       = {{Henke, Tobias and Lohweg, Volker}},
  booktitle    = {{IEEE International Conference On Image Processing (ICIP), Proceedings}},
  isbn         = {{0-7803-9134-9}},
  issn         = {{2381-8549 }},
  keywords     = {{Pattern recognition, Field programmable gate arrays, Neural networks, Image processing, Discrete transforms, Signal processing, Image retrieval, Image recognition, Transient analysis, Fuzzy systems}},
  pages        = {{349 -- 352}},
  publisher    = {{IEEE}},
  title        = {{{A Simplified Scheme For Hardware-Based Pattern Recognition}}},
  doi          = {{ 10.1109/ICIP.2005.1529759}},
  year         = {{2005}},
}

@article{2056,
  abstract     = {{Nonlinear spatial transforms and fuzzy pattern classification with unimodal potential functions are established in signal processing. They have proved to be excellent tools in feature extraction and classification. In this paper, we will present a hardware-accelerated image processing and classification system which is implemented on one field-programmable gate array (FPGA). Nonlinear discrete circular transforms generate a feature vector. The features are analyzed by a fuzzy classifier. This principle can be used for feature extraction, pattern recognition, and classification tasks. Implementation in radix-2 structures is possible, allowing fast calculations with a computational complexity of up to. Furthermore, the pattern separability properties of these transforms are better than those achieved with the well-known method based on the power spectrum of the Fourier Transform, or on several other transforms. Using different signal flow structures, the transforms can be adapted to different image and signal processing applications.}},
  author       = {{Lohweg, Volker and Diederichs, Carsten and Müller, Dietmar}},
  issn         = {{1110-8657 }},
  journal      = {{EURASIP journal on applied signal processing : a publication of the European Association for Speech, Signal, and Image Processing }},
  keywords     = {{image processing, nonlinear circular transforms, feature extraction, fuzzy pattern recognition}},
  number       = {{1}},
  pages        = {{1912--1920}},
  publisher    = {{Hindawi Publ.}},
  title        = {{{Algorithms for Hardware-Based Pattern Recognition}}},
  doi          = {{https://doi.org/10.1155/S1110865704404247}},
  volume       = {{12}},
  year         = {{2004}},
}