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Felipe Lumbreras, Ramon Baldrich, Maria Vanrell, Joan Serrat, & Juan J. Villanueva. (1999). Multiresolution texture classification of ceramic tiles. In Recent Research developments in optical engineering, Research Signpost, 2: 213–228.
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A. Martinez, & Jordi Vitria. (1996). Designing and Implementing Real Walking Agents using Virtual Environments. In Applications of Artificial Intelligence (pp. 105–114).
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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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Francesc Tous, Agnes Borras, Robert Benavente, Ramon Baldrich, Maria Vanrell, & Josep Llados. (2002). Textual Descriptions for Browsing People by Visual Apperance. In Lecture Notes in Artificial Intelligence (Vol. 2504, pp. 419–429). Springer Verlag.
Abstract: This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building
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Fernando Vilariño, & Petia Radeva. (2003). Cardiac Segmentation with Discriminant Active Contours. (211–217). IOS Press.
Abstract: Dynamic tracking of heart moving is one relevant target in medical imag- ing and can be helpful for analyzing heart dynamics in the study of several cardiac diseases. For this aim, a previous segmentation problem of such structures is stated, based on certain relevant features (like edges or intensity levels, textures, etc.) Clas- sical active models have been used, but they fail when overlapping structures or not well-defined contours are present. Automatic feature learning systems may be a pow- erful tool. Discriminant active contours present optimal results in this kind of problem. They are a kind of deformable models that converge to an optimal object segmenta- tion that dynamically adapts to the object contour. The feature space is designed from a filter bank in order to guarantee the search and learning of the set of relevant fea- tures for optimal classification on each part of the object. Tracking of target evolution is obtained through the whole set of images, using information from the actual and previous stages. Feedback systems are implemented to guarantee the minimum well- separable classification set in each segmentation step. Our implementation has been proved with several series of Magnetic Resonance with improved results in segmenta- tion in comparison to previous methods.
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David Masip, & Jordi Vitria. (2004). Classifier Combination Applied to Real Time Face Detection and Classification. In Recerca Automatica, Visio i Robotica, Ed. UPC, A. Grau, V. Puig (Eds.), 345–353, ISBN 84–7653–844–8.
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Angel Sappa, Niki Aifanti, N. Grammalidis, & Sotiris Malassiotis. (2004). Advances in Vision-Based Human Body Modeling. In N. Sarris and M. Strintzis. (Ed.), 3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body (pp. 1–26).
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P. Andreeva, Maya Dimitrova, & Petia Radeva. (2004). Data Mining Learning Models and Algorithms for Medical Applications. In 18 Conference Systems for Automation of Engineering and Research (SEAR 2004).
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Niki Aifanti, Angel Sappa, N. Grammalidis, & Sotiris Malassiotis. (2005). Human Motion Tracking and Recognition. In Encyclopedia of Information Science and Technology, 1(5):1355–1360.
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Angel Sappa, Niki Aifanti, Sotiris Malassiotis, & N. Grammalidis. (2005). Survey of 3D Human Body Representations. In Encyclopedia of Information Science and Technology, 1(5):2696–2701.
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Ernest Valveny, & Philippe Dosch. (2004). Performance Evaluation of Symbol Recognition. In A. D.(E.) S. Marinai (Ed.), Document Analysis Systems (Vol. 3163, 354–365).
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Jordi Vitria, Petia Radeva, & I. Aguilo. (2004). Recent Advances in Artificial Intelligence Research and Development. In Frontiers in Artificial Intelligence and Applications, 113, J. Vitria, P. Radeva, I. Aguilo (Eds.), ISBN: 1–58603–466–9.
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Ignasi Rius, Dani Rowe, Jordi Gonzalez, & Xavier Roca. (2005). A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 529–536.
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Dani Rowe, Ignasi Rius, Jordi Gonzalez, Xavier Roca, & Juan J. Villanueva. (2005). Probabilistic Image-Based Tracking: Improving Particle Filtering. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 85–92.
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Agata Lapedriza, David Masip, & Jordi Vitria. (2005). The contribution of external features to face recognition. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 537–544.
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