J. Korczak

Publications (7)0 Total impact

• Source

  Available from: Cédric Wemmert

  Conference Proceeding: On Machine Learning in Watershed Segmentation

  S. Derivaux, S. Lefevre, C. Wemmert, J. Korczak
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  ABSTRACT: Automatic image interpretation could be achieved by first performing a segmentation of the image, i.e. aggregating similar pixels to form regions, then use a supervised region- based classification. In such a process, the quality of the segmentation step is of great importance. Nevertheless, whereas the classification step takes advantage from some prior knowledge such as learning sample pixels, the segmentation step rarely does. In this paper, we propose to involve machine learning to improve the segmentation process using the watershed transform. More precisely, we apply a fuzzy supervised classification and a genetic algorithm in order to respectively generate the elevation map used in the watershed transform and tune segmentation parameters. The results from our evolutionary supervised watershed algorithm confirm the relevance of machine learning to introduce knowledge in the watershed segmentation process.
  Machine Learning for Signal Processing, 2007 IEEE Workshop on; 09/2007
• Conference Proceeding: Analysis and evaluation of learning classifier systems applied to hyperspectral image classification

  A. Quirin, J. Korczak, M.V. Butz, D.E. Goldberg
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  ABSTRACT: In this article, two learning classifier systems based on evolutionary techniques are described to classify remote sensing images. Usually, these images contain voluminous, complex, and sometimes erroneous and noisy data. The first approach implements ICU, an evolutionary rule discovery system, generating simple and robust rules. The second approach applies the real-valued accuracy-based classification system XCSR. The two algorithms are detailed and validated on hyperspectral data.
  Intelligent Systems Design and Applications, 2005. ISDA '05. Proceedings. 5th International Conference on; 10/2005
• Conference Proceeding: Representation of genetic individuals for unmixing multispectral data

  A. Quirin, J. Korczak
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  ABSTRACT: The traditional classification algorithms for multispectral images assign only one class to each pixel. However, such pixels are actually a mixture of the spectral reflectance values of several different types of ground, of which the various abundances characterize the final shape of the observed spectrum. Within the framework of supervised learning, a representative solution was defined to solve this kind of problem using a genetic algorithm. This paper introduces a representation of the selected and various associated genetic operators (fitness, crossover, mutation) used in remote sensing image classification, as well, it describes a comparison of various representation using two more algorithms on three data sets.
  Evolutionary Computation, 2005. The 2005 IEEE Congress on; 10/2005
• Conference Proceeding: A multi-sample multi-source model for biometric authentication

  N. Poh, S. Bengio, J. Korczak
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  ABSTRACT: In this study, two techniques that can improve the authentication process are examined: (i) multiple samples and (ii) multiple biometric sources. We propose the fusion of multiple samples obtained from multiple biometric sources at the score level. By using the average operator, both the theoretical and empirical results show that integrating as many samples and as many biometric sources as possible can improve the overall reliability of the system. This strategy is called the multi-sample multi-source approach. This strategy was tested on a real-life database using neural networks trained in one-versus-all configuration.
  Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on; 02/2002
• Conference Proceeding: An unsupervised collaborative learning method to refine classification hierarchies

  C. Wemmert, P. Gancarski, J. Korczak
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  ABSTRACT: This article deals with the design of a hybrid learning system. This system integrates different kinds of unsupervised learning methods and gives a set of class hierarchies as the result. The classes in these hierarchies are very similar. The method occurrences compare their results and automatically refine them to try to make them converge towards a unique hierarchy that unifies all the results. Thus, the system decreases the importance of the initial choices made when initializing an unsupervised learning (the choice of the method and its parameters) and to solve some of the limitations of the methods used such as an imposed number of classes, a non-hierarchical result, or the size of the hierarchy
  Tools with Artificial Intelligence, 1999. Proceedings. 11th IEEE International Conference on; 02/1999
• Conference Proceeding: Tools For Image Compression

  M. Derkaoui, J. Korczak, F. Simonis, B. Sittler, T. Toussaint
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  ABSTRACT: Not Available
  Geoscience and Remote Sensing Symposium, 1988. IGARSS '88. Remote Sensing: Moving Toward the 21st Century., International; 10/1988
• Article: Observations and ecogeomorphological modelling of tidal environments.

  M. Marani, R. Aspden, E. Belluco, M. Camuffo, A. D'Aplaos, J. Korczak, S. Lanzoni, C. McGregor, A. Marani, D. C. Mason, M. Menenti, D. Paterson, A. Quirin, R. Rigon, A. Rinaldo, T Scott, S. Silvestri, S. Vardy, H. Wang