Abstract
We propose a new adaptive novelty detection based algorithm for the primary local recognition of images corrupted by multiplicative/additive and impulse noise. The purpose of primary local recognition or low level analysis such as segmentation, small object and outlier detection is to provide a representation which could be potentially used e.g. in context based classification or nonlinear denoising techniques. The method is based on the estimation of mixing parameters (priors) of probabilistic mixture models along a small sliding window. A novelty score is defined by the mixing parameters and this is utilized by the procedure for determining the corresponding class of image patch with the aid of a lookup f. Numerical simulations demonstrate that the proposed method is able to improve upon previously employed techniques for the same task. In addition, the computational demand required by the proposed method is clearly inferior to some of the recently applied techniques as expert systems or neural networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bishop, C.: Novelty Detection and Neural Network Validation. In: Proceedings, IEE Conference on Vision and Image Signal Processing, pp. 217–222 (1994)
Nairac, A., Townsend, N., Carr, R., King, S., Cowley, P., Tarassenko, L.: A System for the Analysis of Jet Engine Vibration Data. Integrated Computer Aided Engineering 6, 53–65 (1999)
Roberts, S.J.: Novelty Detection Using Extreme Value Statistics. IEE Proceedings on Vision, Image and Signal Processing 146(3), 124–129 (1999)
Schölkopf, B., Williamson, R., Smola, A., Taylor, J.S., Platt, J.: Support Vector Method for Novelty Detection. In: Solla, S.A., Leen, T.K., Muller, K.R. (eds.) Neural Information Processing Systems, pp. 582–588 (2000)
Campbell, C., Bennett, K.P.: A Linear Programming Approach to Novelty Detection. In: Advances in Neural Information Processing Systems, vol. 14, MIT Press, Cambridge (2001)
Ypma, A., Duin, R.P.W.: Novelty Detection Using Self-organising Maps. Progress in Connectionist Based Information Systems 2, 1322–1325 (1998)
Melnik, V.P.: Nonlinear Locally Adaptive Techniques for Image Filtering and Restoration in Mixed Noise Environments: Thesis for the degree of Doctor of Technology, Tampere University of Technology, Tampere, Finland (2000)
Dolia, A.N., Lukin, V.V., Zelensky, A.A., Astola, J.T., Anagnostopoulos, C.: Neural Networks for Local Recognition of Images with Mixed Noise. In: Nasrabadi, N.M., Katsaggelos, A.K. (eds.) Applications of Artificial Neural Networks in Image Processing VI, Proc.SPIE, vol. 4305, pp. 108–118 (2001)
Dolia, A.N., Burian, A., Lukin, V.V., Rusu, C., Kurekin, A.A., Zelensky, A.A.: Neural Network Application to Primary Local Recognition and Nonlinear Adaptive Filtering of Images. In: Proceedings of the 6-th IEEE International Conference on Electronics, Circuits and Systems, Pafos, Cyprus, vol. 2, pp. 847–850 (1999)
Bishop, C.: Neural Network for Pattern Recognition. Oxford University Press, Oxford (1995)
Marr, D.: Vision: A Computational Invistigation into the Human Representation and Processing of Visual Information. Freeman, San Francisco (1982)
Lukin, V.V., Ponomarenko, N.N., Astola, J.T., Saarinen, K.: Algorithms of Image Nonlinear Adaptive Filtering using Fragment Recognition by Expert System. In: Proceeddings of IS@T/SPIE Symp. on Electronic Imaging: Science and Technology, San Jose, California, USA, vol. 2318, pp. 114–125 (1996)
Niemistö, A., Lukin, V., Shmulevich, I., Yli-Harja, O., Dolia, A.: A Training- based Optimization Framework for Misclassification Correction. In: Proceedings of 12th Scandinavian Conference on Image Analysis, Bergen, Norway, pp. 691-698 (2001)
Bendat, A., Pierson, J.S.: Random Data: analysis and Measurement Procedures, 3rd edn. Wiley series in Probability and Statistics. John Wiley & Sons, Inc., New York (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dolia, A.N., Lages, M., Kaban, A. (2003). An Adaptive Novelty Detection Approach to Low Level Analysis of Images Corrupted by Mixed Noise. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_78
Download citation
DOI: https://doi.org/10.1007/978-3-540-45224-9_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40803-1
Online ISBN: 978-3-540-45224-9
eBook Packages: Springer Book Archive