Computer recognition systems
Michał Woźniak, Elif Derya Übeylı · Expert Systems · 2010
The aim of the recognition task (Duda et al., 2001) is to classify a given object of interest by assigning it to some predefined category, on the basis of observing the features of the object. Depending on the practical application, these objects (so-called patterns) can be images, signal waveforms or any type of measurements that need to be classified (Theodoridis and Koutroumbas, 2003). Pattern recognition has a long history, properly becoming a scientific discipline at the end of the 1950s with the publication of Frank Rosenblatt's work devoted to the perceptron (Rosenblatt, 1958). Since that time, the progress of computer technology has increased the demand for practical applications of pattern recognition and caused the development of new efficient theoretical methods of recognition required by more and more sophisticated decision problems. Nowadays, the worldwide economy is a knowledge economy (Drucker, 1969), which needs the discovery, transfer and better utilization of knowledge, and pattern recognition methods are widely used by today's engineering applications and research. They are an integral part in most machine intelligence systems built for decision making. There is much current research into developing even more efficient and accurate recognition algorithms, based on technologies such as neural networks, statistical and symbolic learning and fuzzy methods to name but a few. Such methods are implemented in the form of computer software and applied in many practical areas, such as character and speech recognition, machine vision, computer-aided medical diagnosis, prediction of customer behaviour, fraud detection and so on. As a result of the call for papers for this issue entitled ‘Computer Recognition Systems’, the articles were submitted and reviewed through a rigorous peer-review process. In the end, four contributions were selected. We hope that the selected papers provide the reader with an excellent discussion of the current issues of computer recognition systems. The selected topics cover the vital parts of modern recognition systems such as information fusion, bioprosthesis decision control, biometrics, and medical decision support. Proença (2010), in his paper ‘An iris recognition approach through structural pattern analysis methods’, proposes a method based on structural (syntactic) pattern recognition. Iris recognition is at present used in several scenarios (airport check-in, refugee control etc.) with good results. In order to achieve acceptable error rates several imaging constraints are enforced, which reduce the fluidity of iris recognition systems. The related papers existing in the literature use statistical pattern recognition and encode iris texture information through phase, zero-crossing or texture analysis based methods. In this report of the experiments performed, three well-known iris image data sets (CASIA, ICE and UBIRIS) are used. The variability of error rates regarding the amount of noise that images contain is also analysed. The experiments show that the proposed method behaves comparably to the statistical approach that constitutes the basis of nearly all deployed systems. Pietka et al. (2010), in their paper ‘Open architecture computer-aided diagnosis system’, extend the traditional goal of a computer-aided diagnosis (CAD) system to assist physicians in performing diagnosis and treatment. The presented platform helps the system designer in developing a new CAD workflow by implementing general-purpose modules as well as problem-dependent procedures. The CAD environment is validated through its use in three systems – a multiple sclerosis CAD, a lung nodule CAD and a pneumothorax CAD – by which it is shown that the various procedures (fuzzy c-means, fuzzy connectedness, labelling, filters) are developed once but employed by many CADs. The results obtained during the CAD evaluation demonstrate the high flexibility of the infrastructure. The trade-offs, well known to CAD designers (e.g. computational cost versus segmentation accuracy), can easily be handled by the operators in a user-friendly manner by choosing various workflow paths. Straszecka (2010), in her paper ‘Combining knowledge from different sources’, deals with the problem of an assessment of symptoms in medical diagnosis. A unified interpretation of symptoms is often necessary to estimate their significance in a diagnosis. This paper shows how to combine evaluations that may originate from an expert or from statistical features of data for diagnostic cases. A new model of diagnostic inference is proposed in the framework, based on Dempster–Shafer theory extended by fuzzy focal elements. An algorithm of the basic probability assignment calculation is suggested and tested for medical data. Wołczowski and Kurzyński (2010), in their paper ‘Human–machine interface in bioprosthesis control using EMG signal classification’, discuss EMG signal characteristics and the problem of processing them, including acquisition, feature extraction and classification. On the basis of a learning set, a fuzzy relation is determined as a solution of an appropriate optimization problem and then the relation in the form of a matrix of membership degrees is used at successive instants of the sequential decision process. The authors describe three algorithms of sequential classification, which differ from one another in the sets of input data and procedure, and infer that the combination of sequential recognition and fuzzy relation brings new possibilities to EMG signal analysis. We would like to thank the Editor-in-Chief of Expert Systems, Jon G. Hall, for his enthusiasm and continuing support for this special issue. Many of the original reviewers helped in the preparation of the special issue, and we thank them greatly for their help. Thanks also to Wiley-Blackwell's Expert Systems' office, who have made this special issue available in good time. Michal Wozniak Michal Wozniak is Professor of Computer Science in the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Poland. He received an MS degree in biomedical engineering in 1992 from the Wroclaw University of Technology, and PhD and DSc (habilitation) degrees in computer science in 1996 and 2007, respectively, from the same university. His research focuses on multiple classifier systems, machine learning, data and web mining, Bayes compound theory, distributed algorithms, computer and networks security and teleinformatics. Professor Wozniak has published over 120 papers and two books, and has edited three books. He is Editor-in-Chief of International Journal of Computer Networks and Communications and associate editor of several international journals including Pattern Analysis and Applications, Expert Systems and International Journal of Communication Networks and Distributed Systems. He serves on the program committees of numerous international conferences. His works have been transitioned into commercial applications. Professor Wozniak has been involved in many research projects related to machine learning, computer networks and telemedicine. Moreover, he has been a consultant on several commercial projects for well-known Polish companies and for the Polish public administration. Professor Wozniak is a member of the IEEE (Computational Intelligence Society and Systems, Man and Cybernetics Society) and IBS (International Biometric Society). For a more detailed profile see http://www.kssk.pwr.wroc.pl/pracownicy/michal.wozniak-en. Elif Derya Übeyli Elif Derya Übeyli (http://edubeyli.etu.edu.tr/) is an Associate Professor at the Department of Electrical and Electronics Engineering, TOBB University of Economics and Technology. She obtained her PhD degree in electronics and computer technology from Gazi University in 2004. She has worked on a variety of topics including biomedical signal processing, neural networks, optimization and artificial intelligence. She has worked on several projects related to biomedical signal acquisition, processing and classification. Dr Übeyli has served (or is currently serving) as a program organizing committee member of many national and international conferences. She is editorial board member of several scientific journals (Journal of Engineering and Applied Sciences, International Journal of Soft Computing, Research Journal of Applied Sciences, Research Journal of Medical Sciences, Scientific Journals International/Electrical, Mechanical, Manufacturing, and Aerospace Engineering, The Open Medical Informatics Journal, Bulletin of the International Scientific Surgical Association, International Journal of Real-Time Systems, Journal of Biomedical Science and Engineering, International Journal of Engineering and Applied Sciences). She is Associate Editor of Expert Systems. She has served as a guest editor to Expert Systems on a special issue on ‘Advances in medical decision support systems’. Moreover, she is voluntarily serving as a technical publication reviewer for many respected scientific journals and conferences. She has also published 118 journal and 44 conference papers on her research areas.