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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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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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David Masip, Agata Lapedriza, & Jordi Vitria. (2007). Measuring External Face Appearance for Face Classification. In Face Recognition, Ed. Kresimir Delac and Mislav Grgic, pp. 287–307, ISBN 978–3–902613–03–5, I–Tech Education and Publishing.
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Oriol Pujol, & Petia Radeva. (2005). On the assessment of texture descriptors in intravascular ultrasound images: A boosting approach to a feasible plaque classification. In Plaque Imaging: Pixel to Molecular Level, IOS Press, J. Suri et al. (Eds.), 113: 276–299, ISBN: 1–58603–516–9.
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Oriol Pujol, & Petia Radeva. (2005). Solving Particularization with Supervised Clustering Competition Scheme. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 11–18.
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Bogdan Raducanu, & Fadi Dornaika. (2008). Dynamic Vs. Static Recognition of Facial Expressions. In Rabuñal (Ed.), Ambient Intelligence. European Conference (Vol. 5355, 13–25). LNCS.
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Bogdan Raducanu, & Jordi Vitria. (2005). Real-Time Face Tracking for Context-Aware Computing. In Artificial Intelligence Research and Development, IOS Press, 91–98.
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Bogdan Raducanu, & Jordi Vitria. (2006). A Robust Particle Filter-Based Face Tracker Using Combination of Color and Geometric Information. In International Conference on Image Analysis and Recognition (ICIAR´06), LNCS 4141 (A. Campilho et al., eds.), 1: 922–933.
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Santiago Segui, Laura Igual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, & Jordi Vitria. (2007). A Semi-Supervised Learning Method for Motility Disease Diagnostic. In Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern (CIARP 2007), LCNS 4756:773–782, ISBN 978–3–540–76724–4.
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Santiago Segui, Laura Igual, Fernando Vilariño, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, et al. (2008). Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy. In and J.K. Tsotsos M. V. A. Gasteratos (Ed.), Computer Vision Systems. 6th International (Vol. 5008, 251–260). LNCS. Berlin Heidelberg: Springer-Verlag.
Abstract: Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are pro- vided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learn- ing framework in order to obtain the characterization of diverse motil- ity events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists.
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Panagiota Spyridonos, Fernando Vilariño, Jordi Vitria, Fernando Azpiroz, & Petia Radeva. (2006). Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions. In and J. Sporring M. N. R. Larsen (Ed.), 9th International Conference on Medical Image Computing and Computer–Assisted Intervention (Vol. 4191, 161–168). LNCS. Berlin Heidelberg: Springer Verlag.
Abstract: Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.
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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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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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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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Fernando Vilariño, Panagiota Spyridonos, Jordi Vitria, Carolina Malagelada, & Petia Radeva. (2006). Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions. In .F. Mart ́ınez-Trinidad et al (Ed.), 11th Iberoamerican Congress on Pattern Recognition (Vol. 4225, 178–187). LNCS. Berlin Heidelberg: Springer Verlag.
Abstract: This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns.
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