@misc{11997, abstract = {{In Germany, individuals unable or not yet able to return to the general labor market due to disabilities are employed in sheltered workshops which are called WfbM (“Werkstätten für behinderte Menschen”). These organizations are required to earn the wages for the aforementioned group of people by offering market services. These services include, in particular, assembly activities. However, WfbM face the challenge that customer orders tend to become more complex, especially as a result of an increased number of product variants. This development not only has an impact on the work in WfbM, but also makes it much more difficult to achieve the desired inclusion of people with disabilities in the general labor market. Bearing this in mind, the research question addressed in this article can be stated as such: How far can the use of an informational assistance system compensate for performance deficits of people with disabilities in the context of assembly? The results of the conducted laboratory study show that the implementation of an assistance system can help to reduce existing barriers and challenges resulting from the mismatch between requirements of the general labor market and the performance characteristics of people with cognitive impairments. Practical Relevance: For people with disabilities, the use of assistance systems opens up new opportunities for participation in the general labor market and thus makes an important contribution to implementing the requirements of the “Bundesteilhabegesetz” (a law to strengthen participation of people with disabilities in Germany).}}, author = {{Bendzioch, Sven and Hinrichsen, Sven}}, booktitle = {{Zeitschrift für Arbeitswissenschaft (ZfA)}}, issn = {{2366-4681}}, keywords = {{Informational Assistance System, People with Disabilities, Manual Assembly, Image Processing System, Laboratory Study}}, number = {{2}}, pages = {{240--253}}, publisher = {{Springer-Verlag GmbH }}, title = {{{Potentials of an informational assembly assistance system for persons with cognitive disabilities — Results of a laboratory study}}}, doi = {{10.1007/s41449-024-00414-9}}, volume = {{78}}, year = {{2024}}, } @misc{10783, abstract = {{The development trend in manual assembly towards increasing demands in terms of quality, variety, and cost pressure makes the transition for people with cognitive disabilities to the general labor market extremely difficult. Nevertheless, this employment sector is a central component of many activities in a sheltered workshop. Therefore, this paper investigates the use of an informational assistance system for persons with cognitive impairments to close the gap between the characteristics of this group and the operational requirements. In this way, the transition from the sheltered workshop to the general labor market will be facilitated and promoted.}}, author = {{Bendzioch, Sven and Hinrichsen, Sven}}, booktitle = {{Human Interaction & Emerging Technologies (IHIET 2023): Artificial Intelligence & Future Applications}}, issn = {{2771-0718}}, keywords = {{Manual Assembly, Informational Assistance System, Image Processing System, People with Disabilities}}, location = {{NIzza}}, pages = {{548--556}}, publisher = {{AHFE International}}, title = {{{Informational Assistance System – a Key to Self-Empowerment of Persons with Cognitive Disabilities in Manual Assembly?}}}, doi = {{10.54941/ahfe1004061}}, volume = {{11}}, year = {{2023}}, } @inproceedings{1901, abstract = {{As customers’ options for configuring products to match their requirements increase, the number of assembly variants grows. Due to this large number of variants, assembly processes often cannot be automated in an economical way, and manual assembly remains highly important. Additional support options must be implemented to continue completing manual assembly processes reliably in the future. Image processing systems are one promising approach. The purpose of this paper is to establish the potential offered by industrial image processing in manual assembly, building on fundamental concepts, as well as to identify requirements and provide recommendations for selecting and arranging system components. }}, author = {{Nikolenko, Alexander and Hinrichsen, Sven}}, booktitle = {{Human Systems Engineering and Design II. IHSED 2019. Advances in Intelligent Systems and Computing}}, editor = {{Ahram, T. and Karwowski, W. and Pickl, S. and Taiar, R.}}, isbn = {{978-3-030-27927-1}}, keywords = {{Industrial image processing, Manual assembly, Assistance systems, Machine vision}}, location = {{Universität der Bundeswehr, München}}, pages = {{795--800}}, publisher = {{Springer Nature}}, title = {{{Potential of Industrial Image Processing in Manual Assembly}}}, doi = {{https://doi.org/10.1007/978-3-030-27928-8_121}}, volume = {{1026}}, year = {{2019}}, } @inproceedings{2022, abstract = {{Nowadays, the boost of optical imaging technologies results in more data with a faster rate are being collected. Consequently, data and knowledge discovery science has become an attractive and a fast growing topic in several industry and research area. Motif discovery in image processing aims to tackle the problem of deriving structures or detecting regularities in image databases. Most of the motif discovery methods first convert images into time series and then attempt to find motifs in such data. This might lead to information loss and also the problem of inability to detect shifted and multi-scale image motifs of different size. Here, a method is proposed to find image motifs of different size in image datasets by applying images in original dimension without converting them to time series. Images are inspected by the Complex Quad Tree Wavelet Packet transform which provides broad frequency analysis of an image in various scales. Next, features are extracted from the wavelet coefficients. Finally, image motifs are detected by measuring the similarity of the features. The performance of the proposed method is demonstrated on a dataset with images from diverse applications, such as hand gesture, text recognition, leaf and plant identification, etc. }}, author = {{Deppe, Sahar and Lohweg, Volker}}, booktitle = {{PESARO 2017 The Seventh International Conference on Performance, Safety and Robustness in Complex Systems and Applications}}, editor = {{Leister, Wolfgang}}, issn = {{2308-3700}}, keywords = {{Motif discovery, Image processing, Wavelet transformation}}, location = {{Venice, Italy }}, pages = {{27--32}}, publisher = {{The Seventh International Conference on Performance, Safety and Robustness in Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms in Image and Signal Processing}}, title = {{{Shift-Invariant Motif Discovery in Image Processing 'Best Paper Award'}}}, year = {{2017}}, } @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}}, } @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}}, }