@article{2104, abstract = {{Maintaining confidence in security documents, especially banknotes, is and remains a major concern for the central banks in order to maintain the stability of the economy around the world. In this paper we describe an image processing and pattern recognition approach which is based on the Sound-of-Intaglio concept [1] for the usage in smart devices such as smartphones. Today, in many world regions smartphones are in use. These devices become more and more computing units, equipped with resource-limited but effective CPUs, cameras with illumination, and flexible operating systems. Hence, it appears to be obvious, to apply those smartphones for banknote authentication, especially for visually impaired persons. However, it has to be researched, whether those devices are capable of processing the data under the constraints of image quality and processing power. Our results show that it is in general possible to use such devices for banknote authentication applications.}}, author = {{Lohweg, Volker and Dörksen, Helene and Gillich, Eugen and Hildebrand, Roland and Hoffmann, Jan Leif and Schaede, Johannes}}, journal = {{Optical Document Security - The Conference on Optical Security and Counterfeit Detection III}}, keywords = {{authentication, anti-counterfeit features, mobile device, smartphone, wavelet transform, pattern recognition, Sound-of-Intaglio}}, title = {{{Mobile Devices for Banknote Authentication – is it possible? In: Optical Document Security - The Conference on Optical Security and Counterfeit Detection III, San Francisco, CA, USA, January 18-20, 2012. }}}, year = {{2012}}, } @inproceedings{2113, abstract = {{In this paper, we sketch an idea for the integration of singleclass support vector machines (SVM) into fuzzy class learning. As result,we obtain robust and transparent rule-based fuzzy classification models suitable for online-classification tasks. In particular, the singleclass SVM is employed to extend the applicability of convex fuzzy classifica-tion models to nonconvex datainherent structures. The key point of thisextension is the preservation of the interpretability for both, the classlearning and the classification process. The feasibility of the approach isdemonstrated in the context of a banknote authentication application.}}, author = {{Hempel, Arne-Jens and Hähnel, Holger and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{BVAu 2012 - 3. Jahresolloquium "Bildverarbeitung in der Automation" Centrum Industrial IT, Lemgo,}}, keywords = {{fuzzy classification, pattern recognition, single-class support vector machine, data mining}}, publisher = {{inIT-Institut für industrielle Informationstechnik}}, title = {{{SVM-integrated Fuzzy Pattern Classification for Nonconvex Data-Inherent Structures Applied to Banknote Authentication}}}, year = {{2012}}, } @inproceedings{2087, abstract = {{It is likely in real-world applications that only little data isavailable for training a knowledge-based system. We present a method forautomatically training the knowledge-representing membership functionsof a Fuzzy-Pattern-Classification system that works also when only littledata is available and the universal set is described insufficiently. Actually,this paper presents how the Modified-Fuzzy-Pattern-Classifier’s member-ship functions are trained using probability distribution functions.}}, author = {{Mönks, Uwe and Lohweg, Volker and Petker, Denis}}, booktitle = {{IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems}}, keywords = {{Fuzzy Logic, Probability Theory, Fuzzy-Pattern-Classification, Machine Learning, Artificial Intelligence, Pattern Recognition}}, publisher = {{28 Jun 2010 - 02 July 2010, Dortmund, Germany}}, title = {{{Fuzzy-Pattern-Classifier Training with Small Data Sets}}}, year = {{2010}}, } @inproceedings{2082, abstract = {{A robust vision system for the counterfeit detection of bank ATM keyboards is presented. The approach is based on the continuous inspection of a keyboard surface by the authenticity verification of coded covert surface features. For the surface coding suitable visual patterns on the keyboard are selected while considering constraints from the visual imperceptibility, robustness and geometrical disturbances to be encountered from the aging effects. The system’s robustness against varying camera-keyboard distances, lighting conditions and dirt-and-scratches effects is investigated. Finally, a demonstrator working in real-time is developed in order to publicly demonstrate the surface authentication process.}}, author = {{Iqbal, Taswar and Le, Dinh Khoi and Nolte, Michael and Lohweg, Volker}}, booktitle = {{32nd Annual Conference on Artificial Intelligence Paderborn | September 15 – 18, 2009, accepted for Publication}}, isbn = {{978-3-642-04616-2}}, keywords = {{ATMs, human perception, counterfeit resistance, digital authentication, surface coding, pattern recognition}}, pages = {{347--354}}, publisher = {{Springer}}, title = {{{Human Perception Based Counterfeit Detection for Automated Teller Machines, KI 2009, Artificial Intelligence and Automation }}}, doi = {{https://doi.org/10.1007/978-3-642-04617-9_44}}, volume = {{5803}}, year = {{2009}}, } @article{2069, abstract = {{During printed product manufacturing, measures are taken to ensure a certain level of printing quality and security via authentification methods. This is particularly true in the field of security printing, where the quality standards, which must be reached by the end-products, i.e. banknotes, security documents and the like, are very high. It is accepted, that print defects are generated because printing parameters but also machine parameters will change unnoticed in production. Therefore, a new concept for a multi-sensory adaptive learning and classification model based on Fuzzy-Pattern-Classifiers for data inspection, authentification and machine conditioning is proposed. This kind of inspection concept, which combines optical, acoustical and other machine information, produces a large amount of data, which leads to multivariate methods for data analysis. Multivariate methods are useful for analysis of large and complex data sets that consist of many variables measured on large numbers of physical data. A general aim is to improve the known inspection techniques and propose an inspection methodology that can ensure a comprehensive quality control of the printed substrates processed by printing presses, especially printing presses which are designed to process substrates used in the course of the production of banknotes, security documents and others. }}, author = {{Dyck, Walter and Türke, Thomas and Schaede, Johannes and Lohweg, Volker}}, journal = {{Optical Document Security - The 2008 Conference on Optical Security and Counterfeit Deterrence; Reconnaissance International Publishers and Consultants, San Francisco, CA, USA}}, keywords = {{authentification, anti-counterfeit features, inspection, quality, sensor fusion, pattern recognition}}, title = {{{A New Concept on Quality Inspection and Machine Conditioning for Security Prints}}}, year = {{2008}}, } @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}}, }