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V. Valev, & Petia Radeva. (1992). A Method of Solving Pattern or image Recognition Problems by Learning Boolean Formulas..
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V. Valev, & Petia Radeva. (1992). Determining Structural Description by Boolean Formulas. In H. Bunke (Ed.), Advances in Structural and Syntactic Pattern Recognition (Vol. 5, 131–140). Machine Perception and Artificial Intelligence:. World Scientific.
Abstract: Pattern recognition is an active area of research with many applications, some of which have reached commercial maturity. Structural and syntactic methods are very powerful. They are based on symbolic data structures together with matching, parsing, and reasoning procedures that are able to infer interpretations of complex input patterns.
This book gives an overview of the latest developments and achievements in the field.
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V. Valev, & Petia Radeva. (1993). On the Determining of Non-Reducible Descriptors for Multidimensional Pattern Recognition Problems..
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V. Valev, & Petia Radeva. (1994). Structural Pattern Recognition by Non-Reducible Descriptors. In Proc. International Workshop on Syntactic and Structural Pattern Recognition..
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V. Valev, & Petia Radeva. (1995). ECG Recognition by Non-Reducible Descriptors..
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V. Valev, & Petia Radeva. (1995). Constructing Quantitative Non-Reducible Descriptors..
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Maria Vanrell, & Jordi Vitria. (1993). Mathematical Morphology, Granulometries and Texture Perception..
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Maria Vanrell, & Jordi Vitria. (1997). Optimal 3x3 decomposable disks for morphological transformations. Image and Vision Computing, 15(2): 845–854.
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Jordi Vitria, M. Bressan, & Petia Radeva. (2006). Bayesian classification of cork stoppers using class-conditional independent component analysis. IEEE Transactions on Systems, Man and Cybernetics (Part C), 36(6).
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Jordi Vitria, M. Bressan, & Petia Radeva. (2007). Bayesian classification of cork stoppers using class-conditional independent component analysis. IEEE Transactions on Systems, Man and Cybernetics (Part C), 37(1): 32–38 (ISI 0,482).
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Jordi Vitria, X. Binefa, & Juan J. Villanueva. (1992). Morphological Algorithms for Visual Analysis of Integrated Circuits..
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Xavier Baro, Sergio Escalera, Petia Radeva, & Jordi Vitria. (2009). Generic Object Recognition in Urban Image Databases. In 12th International Conference of the Catalan Association for Artificial Intelligence (Vol. 202, pp. 27–34).
Abstract: In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (>500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. All this information is extracted without an object of reference, which allows to search for any type of objects using their visual appearance. A new Visual Content layer is built over Google Maps, allowing the object recognition information to be organized and fused with other content, like satellite images, street maps, and business locations.
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Jordi Vitria, C. Gratin, D. Seron, & F. Moreso. (1995). Morphological image analysis for quantification of renal damage.
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Jordi Vitria, & J. Llacer. (1993). Recovering Depth from Focus Using Iterative image Estimation Techniques..
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Jordi Vitria, & J. Llacer. (1995). Recovering brightness and depth from focus using the Expectation-Maximization Algorithm..
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