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Your search keyword '"Barrenechea E"' showing total 4 results
Start Over Author "Barrenechea E" Topic image processing
4 results on '"Barrenechea E"'

1. A t-Norm Based Approach to Edge Detection

Author

Lopez-Molina, C., Bustince, H., Fernández, J., Barrenechea, E., Couto, P., De Baets, B., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Cabestany, Joan, editor, Sandoval, Francisco, editor, Prieto, Alberto, editor, and Corchado, Juan M., editor

Published
2009
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2. Relationship between restricted dissimilarity functions, restricted equivalence functions and normal E N -functions: Image thresholding invariant

Author

Bustince, H., Barrenechea, E., and Pagola, M.

Subjects
*IMAGE processing, *IMAGING systems, *COMPUTER graphics, *INFORMATION processing, *SET theory
Abstract

Abstract: In this paper, we present the definition of restricted dissimilarity function. This definition arises from the concepts of dissimilarity and equivalence function. We analyze the relation there is between restricted dissimilarity functions, restricted equivalence functions (see [Bustince, H., Barrenechea, E., Pagola, M., 2006. Restricted equivalence functions. Fuzzy Sets Syst. 157, 2333–2346]) and normal E N -functions. We present characterization theorems from implication operators and automorphisms. Next, by aggregating restricted dissimilarity functions in a special way, we construct distance measures of Liu, proximity measures of Fan et al. and fuzzy entropies. We also study diverse interrelations between the above-mentioned concepts. These interrelations enable us to prove that under certain conditions, the threshold of an image calculated with the algorithm of Huang and Wang [Huang, L.K., Wang, M.J., 1995. Image thresholding by minimizing the measure of fuzziness. Pattern Recognit. 28 (1), 41–51], with the methods of Forero [Forero, M.G., 2003. Fuzzy thresholding and histogram analysis. In: Nachtegael, M., Van der Weken, D., Van de Ville, D., Kerre, E.E. (Eds.), Fuzzy Filters for Image Processing. Springer, pp. 129–152] or with the algorithms developed in [Bustince, H., Barrenechea, E., Pagola, M., 2007. Image thresholding using restricted equivalence functions and maximizing the measures of similarity. Fuzzy Sets Syst. 158, 496–516] is always the same, that is, it remains invariant. [Copyright &y& Elsevier]

Published
2008
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3. Image thresholding using restricted equivalence functions and maximizing the measures of similarity

Author

Bustince, H., Barrenechea, E., and Pagola, M.

Subjects
*IMAGE processing, *ALGORITHMS, *IMAGE analysis, *IMAGING systems, *PIXELS, *DIGITAL images, *EQUIVALENCE relations (Set theory)
Abstract

In this paper we apply restricted equivalence functions to the computation of the threshold of an image. In the first part we present an algorithm for obtaining the best threshold of a grayscale image with a single object. In the second part we study different algorithms for calculating the optimal threshold. Then we analyze two algorithms for obtaining a sequence of optimal thresholds in images with several objects. Lastly, we compare our results with those obtained with other methods and carry out a study of the time efficiency of the methods we propose. [Copyright &y& Elsevier]

Published
2007
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4. Contrast of a fuzzy relation

Author

Bustince, H., Barrenechea, E., Fernandez, J., Pagola, M., Montero, J., and Guerra, C.

Subjects
*FUZZY relational calculus, *CONTRAST effect, *INFORMATION theory, *SCIENTIFIC literature, *PHOTOGRAPHS, *IMAGE processing
Abstract

Abstract: In this paper we address a key problem in many fields: how a structured data set can be analyzed in order to take into account the neighborhood of each individual datum. We propose representing the dataset as a fuzzy relation, associating a membership degree with each element of the relation. We then introduce the concept of interval-contrast, a means of aggregating information contained in the immediate neighborhood of each element of the fuzzy relation. The interval-contrast measures the range of membership degrees present in each neighborhood. We use interval-contrasts to define the necessary properties of a contrast measure, construct several different local contrast and total contrast measures that satisfy these properties, and compare our expressions to other definitions of contrast appearing in the literature. Our theoretical results can be applied to several different fields. In an , we apply our contrast expressions to photographic images. [Copyright &y& Elsevier]

Published
2010
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