Petra Perner

Institute of Computer Vision and Applied Computer Sciences

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Ovidio Salvetti

Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo"

Anja Attig

Institute of Computer Vision and Applied Computer Sciences

Silke Jänichen

Institute of Computer Vision and Applied Computer Sciences

Horst Perner

Institute of Computer Vision and Applied Computer Sciences

Suzanne Little

Dublin City University

Chid Apte

IBM (United States)

Azriel Rosenfeld

University of California, Santa Cruz

Sameer Singh

Dr. Hari Singh Gour University

Maneesha Singh

Loughborough University

Gabriella Sanniti di Baja

Institute for High Performance Computing and Networking

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Kasturba Medical College, Manipal

Amer Sports (United States)

Bandung Institute of Technology

Manipal Academy of Higher Education

Ufa State Aviation Technical University

Harbin Institute of Technology

Amrita Institute of Medical Sciences and Research Centre

Christ University

Institute of Computer Vision and Applied Computer Sciences

National Research Council

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Advances in data mining : applications and theoretical aspects : 9th Industrial Conference, ICDM 2009 Leipzig, Germany, July 20-22, 2009 : proceedings

Springer eBooks (2009)

Petra Perner

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Advances in data mining : applications in image mining, medicine and biotechnology, management and environmental control, and telecommunications : 4th Industrial Conference on Data Mining, ICDM 2004, Leipzig, Germany, July 4-7, 2004 : revised selected papers

Springer eBooks (2004)

Petra Perner

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Case-based reasoning and the statistical challenges

Quality and Reliability Engineering International (2008)

Petra Perner

Case-based reasoning (CBR) solves problems using the already stored knowledge and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR can be seen as a method for problem solving and also as a method to capture new experience and make it immediately available for problem solving. The CBR paradigm has been originally introduced by the cognitive science community. The CBR community aims at developing computer models that follow this cognitive process. Up to now many successful computer systems have been established on the CBR paradigm for a wide range of real-world problems. In this paper we will review the CBR process and the main topics within the CBR work. Hereby we try bridging between the concepts developed within the CBR community and the statistics community. The CBR topics we describe are similarity, memory organization, CBR learning and case-base maintenance. Then we will review, based on applications, the open problems that need to be solved. The applications we focus on are meta-learning for parameter selection, image interpretation, incremental prototype-based classification and novelty detection and handling. Finally, we summarize our concept on CBR. Copyright © 2008 John Wiley & Sons, Ltd.

10.1002/qre.951

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Citations (19)

Fuzzy Conceptual Clustering

Lecture notes in computer science (2010)

Petra Perner Anja Attig

Hierarchical clustering

10.1007/978-3-642-14400-4_6

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Advances in Structural and Syntactical Pattern Recognition

Lecture notes in computer science (1996)

Petra Perner Patrick Wang Azriel Rosenfeld

10.1007/3-540-61577-6

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Citations (35)

Determination of Alzheimer Disease by the Calculation of the Brain/Liquor Quotient.

Petra Perner

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Iris recognition and what is next? Iris diagnosis - a new challenging topic for machine vision from image acquisition to image interpretation

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2017)

Petra Perner

Molecular image-based techniques are widely used in medicine to detect specific diseases. Look diagnosis is an important issue but also the analysis of the eye plays an important role in order to detect specific diseases. These topics are important topics in medicine and the standardization of these topics by an automatic system can be a new challenging field for machine vision. Compared to iris recognition has the iris diagnosis much more higher demands for the image acquisition and interpretation of the iris. One understands by iris diagnosis (Iridology) the investigation and analysis of the colored part of the eye, the iris, to discover factors, which play an important role for the prevention and treatment of illnesses, but also for the preservation of an optimum health. An automatic system would pave the way for a much wider use of the iris diagnosis for the diagnosis of illnesses and for the purpose of individual health protection. With this paper, we describe our work towards an automatic iris diagnosis system. We describe the image acquisition and the problems with it. Different ways are explained for image acquisition and image preprocessing. We describe the image analysis method for the detection of the iris. The meta-model for image interpretation is given. Based on this model we show the many tasks for image analysis that range from different image-object feature analysis, spatial image analysis to color image analysis. Our first results for the recognition of the iris are given. We describe how detecting the pupil and not wanted lamp spots. We explain how to recognize orange blue spots in the iris and match them against the topological map of the iris. Finally, we give an outlook for further work.

Iris Recognition

IRIS (biosensor)

Feature (linguistics)

10.1117/12.2269067

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Watershed Segmentation Via Case-Based Reasoning

Lecture notes in computer science (2007)

Maria Frucci Petra Perner Gabriella Sanniti di Baja

Segmentation-based object categorization

Region growing

10.1007/978-3-540-75555-5_23